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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1

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íë ææ æí ± µÁY Ä ¸m ƶǀƫƹř ƞƿŹŚƘţ

ΪηΎΑ ϲϣ ήϳΩΎϘϣ ϩΪϨϫΩ ϥΎθϧ έ΍Ϯϧ ϭ έ΍ΰΑ΍ έ΍Ϯϧ ˬ ϮϨϣ ΖγήϬϓ ˬ ϥ΍ϮϨϋ έ΍Ϯϧ ϞϣΎη έ΍ΰϓ΍ ϡήϧ Ϧϳ΍ Ζ˰γ΍ record ̮ ˰ϳ ήτγ ήϫ ϭ ΪηΎΑ ϲϣ Ρήτϣ variable Ύϳ ήϴ ϐΘϣ ϥ΍ϮϨϋ ϪΑ Ϫ̯ Ζγ΍ field ̮ϳ ϥϮΘγ ήϫ ΪηΎΑ ϲϣ Ρήτϣ cace Ύϳ ΩέϮϣ ̮ϳ ϥ΍ϮϨϋ ϪΑ Ϫ̯ ή˰ϫ Ζ˰γ΍ ϪΤϔ˰λ Ϧ ˰ϳ ΍ ΕΎ˰ϋϼσ΍ ζϳ΍ή ˰ϳ ϭ SPSS Ϫ˰ϣΎϧήΑ έΎ˰̯ ϭ Ϊ˰Ϩϳ Ϯ̳ ϲ ϣ Datasheet ϩΪη ίΎΑ ϩήΠϨ̡ ϪΑ Ωέ΍Ω ΖϤδϗϭΩ Datasheet ϢϴϨ̯ ϲϣ Ωέ΍ϭ ϥ΁έΩ ΍έ ήϳΩΎϘϣ Ϫ̯ Data view ˺ ϢϴϨ̯ ϲϣ ϒϳήόΗ ΍έΎϫ ήϴϐΘϣ Ϫ̯ Variable view ˻

ή ˰ϴ ϐΘϣ Ϫ˰̯ έΎ˰Α ή˰ϫ ϭ Ζ˰γΎϬϧ΁ αΎ ˰ϴ Ϙϣ Ύ˰Ϭϧ΁ Ζϴ ˰λΎΧ Ϧϳ ή˰ΘϤϬϣ Ϫ̯ Ϊϧέ΍Ω ϲΗΎϴ λϮμΧ Ύϫ ήϴ ϐΘϣ SPSS έΩ ϢϴϨ̯ ϒϳήόΗ ΍έ ϥ΁ αΎϴϘϣ ΪϳΎΑ ΩϮη ϲϣ ϒϳήόΗ

ŚƷ ŽŚǀƤƯ ƕřƺƳř

Nominal ϱέϮλ ϲϤγ΍ αΎϴϘϣ ˺ έΩ ΩϮ˰η ϲ Ϥ˰γ΍ αΎ ˰ϴ Ϙϣ Ϫ˰Α Ϟϳ ΪΒΗ ϭ ϩΩ΍Ω Ϊ̯ ϥ΁ ϪΑ Ϣϴϧ΍ϮΘϴ ϣ Ϫ̯ Ϣϴ Ϩ̯ ϲ ϣ ϲ ̳Ϊϧί ϥ΁έΩ Ϫ̯ ϱ ήϬη ΪϨϧΎϣ ϩϭή˰̳ ΐ˰γΎϨϣ ϭ Ωή˰̯ ϱ έ΍ά˰̳ Ϊ˰̯ ϥ΍ϮΗ ϲϣ έϮσ ήϫ ϭ Ωέ΍Ϊϧ ϲ ΗϭΎϔΗ Ω΍Ϊϋ΍ ϥΩϮΑ ̱έΰΑ ϭ ̮̩Ϯ̯ αΎϴ Ϙϣ Ϧϳ΍ Ωέ΍Ϊϧ ϲϨόϣ ϥ΁ έΩ ϊϤΟ ϭ Ϧϴ̴ϧΎϴϣϭ Ζγ΍ ϱΪϨΑ

Ordinal ϲΒϴΗήΗ ϱ΍ ϪΒΗέ αΎϴϘϣ ˻ Ύ˰ΠΑΎΟ Ύ˰ϫ Ϊ˰̯ ή˰̳΍ Ϊ˰ϫΩ ϲ ˰ϣ ϥΎθ˰ϧ ΍έ ΐ ˰ϴΗ ήΗ Ύ ˰ϳ Ζϳ Ϯϟϭ΍ ϭ ΪηΎΑ ϲ ϣ Ζϴ Ϥϫ΍ ϪΟέΩ αΎγ΍ ήΑ ϱ έ΍ά̳ Ϊ̯ Ωέ΍Ϊϧ ΩϮΟϭ ΎϬϫϭή̳ ϦϴΑ ̵ήϴ̳ ϩί΍Ϊϧ΍ ϞΑΎϗ ϪϠλΎϓ ϲϟΎϋ ϭ ΪΑ ˬ ΏϮΧ ΕέΎϬϣ ΪϨϧΎϣ ΪϨ̯ ϲϣ ϕήϓΪϧϮη

Scale ϲΒδϧ αΎϴϘϣ .˼ Ω΍ήϓ΍ Ϊϗ ϞΜϣ Ωέ΍Ω ϲΒδϧ αΎϴϘϣ ΪϳΎϴΑ ΖγΪΑ ϱήϴ̳ ϩί΍Ϊϧ΍ ϖϳήσ ί΍ Ϫ̯ ϱΩΪϋ ήϫ

żĩźưţ ƆųŚƃ έΎ˰̰Α ϲ Βδ˰ϧ αΎ ˰ϴ Ϙϣ ϱ ΍ή˰Α ς˰Ϙϓ Ϫ˰̯ Ζγ΍ Mean Ϧϴ̴ϧΎϴ ϣ ϥ΁ Ϧϳ ήΘϓϭήόϣ ϭ ΖγΎϫ ϩΩ΍Ω ΰ̯ήϤΗ ϪϧΎθϧ Ύ˰ϫ ϩΩ΍Ω ή˰̳΍ ϲϟϭ Ωϭέ ϲ ϣ ϥ΁ ϑήσ ϪΑ ΪηΎΑ ̱έΰΑ ήϳ ΩΎϘϣ ή̳΍ ϭ Ζγ΍ αΎδΣ Ύϫ ϩΩ΍Ω ϪΑ έΎϴδΑ Ωϭέ ϲϣ ΩϮΑ Ϊϫ΍ϮΨϧ ΎϳϮ̳ Ϧϴ̴ϧΎϴϣ ΪϨηΎΑ ϩΪϨ̯΍ή̡ ϲϠϴΧ ή̳΍Ϊ ˰ϳ΁ ϲ ˰ϣ Ζ˰γΪΑ Ϫ˰ΒΗέ ϱ ϭέ ί΍ ϭ Ζ˰γ Ύ˰ϫ ϩΩ΍Ω ̶ϧΎ˰ϴϣ Ϫ˰ΒΗέ Ϫ̯ Ζγ΍ Median ϪϧΎϴ ϣ ή̴ϳ Ω κΧΎη n 1 ̱έΰ˰Α ϩΩ΍Ω ϪΑ ϪϧΎϴϣ Ζγ΍ ϪϧΎϴ ϣ ϲτ γϭ ΪηΎΑ Ωήϓ ή̳΍ ϭ Ζγ΍ ϪϧΎϴ ϣ Ϫϴ Ηέ ΪηΎΑ Νϭί Ύϫ ϩΩ΍Ω Ω΍ΪόΗ 2 ΪηΎΑ ϲϤϧ αΎδΣ ̮̩Ϯ̯ Ύϳ ϲϧ΍ ϭ΍ήϓ ϥ΍ΰϴ ϣ Ωϭέ ϲ ϣ έΎ̰Α Ϣϫ Nominal Ύϳ ϲ Ϥγ΍ ϱ Ύϫ ϩΩ΍Ω ϱ ΍ήΑ Ϫ̯ Ζγ΍ Mode Ϊϣ ή̴ϳ Ω κΧΎη Ωέ΍Ω ΍έ Ε΍ΪϫΎθϣ ϦϳήΘθϴΑ ϭ ΪϫΩ ϲϣ ϥΎθϧ ΍έ Ύϫ ϩΩ΍Ω

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SPSS : Statistical Package For Social Science

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˺ϝΎΜϣ κΧΎ˰η β̢˰γ Ϊ ˰ϴ Ϩ̯ Ωέ΍ϭ ϪΤϔλ έΩ ΍έ ήϳ ί ΕΎϋϼσ΍ ϭ ϩΩή̯ ϒϳ ήόΗ ΍έ ϲ Ϡϣ κϟΎΧΎϧ Ϊϣ΁έΩ ϭ έϮθ̯ ϡΎϧ ϱ Ύϫ ήϴϐΘϣ ΪϴϳΎϤϧ ϪΒγΎΤϣ ΍έ ϲ̳ΪϨ̯΍ή̡ ϭ ϊϳίϮΗ ˬϱέΎϣ΁ ϒϠΘΨϣ ϱΎϫ ϲϠϣ κϟΎΧΎϧ ϡ΁έΩ ˺̋ ˻˹ ̋ ˺˹ ˺˼

έϮθ̯ ϡΎϧ ϥ΍ήϳ΍ Ϫϴ̯ήΗ ϥΎΘδϧΎϐϓ΍ ϕ΍ήϋ ϥΎΘδ̯Ύ̡

:ϢϴϨ̯ ϲϣ ϒϳήόΗ variable view έΩ ΍έΎϫ ήϴϐΘϣ country ϡΎϧ ϪΑ ΎϫέϮθ̯ ϡΎϧ ϝϭ΍ ήϴϐΘϣ ΩϮθϧ ̠ϳΎΗ ϲγέΎϓ ϭ Ωέ΍Ϊϧ ϲϟΎ̰η΍ ϩήϴΗ ςΧ ϲϟϭ ΩϮη ϩΩΎϔΘγ΍ ϪϠλΎϓ ί΍ ΪϳΎΒϧ ήϴϐΘϣ Ϣγ΍ ̠ϳΎΗ έΩ ϢϴϨ̯ ϲϣ ΏΎΨΘϧ΍ STRING ήϴϐΘϣ ωϮϧ Ύϳ Type έΩ name of countryˬ ˱ϼΜϣ Ω΍Ω ήϴϐΘϣ ΩέϮϣ έΩ ϲϓΎ̯ ΕΎΤϴοϮΗ ϥ΍ϮΘϴϣ label έΩ ϱ Ύ˰ϫ ή ˰ϴϐΘϣ ϱ Ϊ˰ϨΑ Ϊ˰̯ ιϮμ˰Ψϣ ϭ Ϣϴ ˰ϧί ϲ ˰Ϥϧ ϱΰ ˰ϴ ̩ ϩΪ˰η ϒϳ ήόΗ ϲ Ϥγ΍ ήϴ ϐΘϣ ϥϮ̩ value ΖϤδϗ έΩ ΪϨηΎΑ ϱΩΪϋ Ϫ̯ Ζγ΍ ϲϫϭή̳ έΩ ˱ϼΜϣΩϮ˰˰η ϲ ˰Ϥϧ Ϫ˰˰Θϓή̳ ή˰˰ψϧ έΩ ΕΎΒ˰˰γΎΤϣ έΩ Ϫ˰˰̯ ΩϮ˰˰η ϲ˰ ϓήόϣ ϱΩ΍Ϊ˰˰ϋ΍ ϲ˰ Ϩόϳ missing ΖϤδ˰˰ϗ έΩ Ζϗϭ ήϫ Ϫ̯ ϢϴϨ̰ϴϣ ϲϓήόϣ ΍έ ˺˹˹˹ ΩΪϋ Ύϣ Ζγ΍ ϩΪθϧ ή̡ Ύϳ ΖγΎϧ΍ϮΧΎϧ Ύϫ ϪϨϳ ΰ̳ ϲ ΧήΑ Ύϫ ϪϣΎϨθγή̡ ΩϮθϧ ΏΎδΣ ϭ ΪηΎΑ ϥ΁ ϲϨόϣ ϪΑ Ϊη ΝέΩ ϢϴϨ̯ ϲϣ ϒϳήόΗ ˺˹ ΍έ ϥϮΘγ ϱΎϨϬ̡ ςγϭ Ύϳ ̠̩ ˬ Ζγ΍έ ϢϴϨ̰ϴϣ ΏΎΨΘϧ΍ ΍έ Align ϢϴϨ̯ ϲϣ ΏΎΨΘϧ΍ ϲϤγ΍ ϭ Nominal ΍έ ήϴϐΘϣ αΎϴϘϣ Ύϳ Measure

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βϧΎ ˰ϳ έ΍ϭ ϥ΁ Ϧϳ ή˰Θϓϭήόϣ ϭ Ωϭή ˰ϴ ϣ έΎ˰̰Α Scale Ύ ˰ϳ ϲ Βδ˰ϧ ϱ Ύ˰ϫ ϩΩ΍Ω ϱ ΍ή˰Α ϭ ΖγΎϫ ϩΩ΍Ω ϲ ̳ΪϨ̯΍ή̡ ϪϧΎθϧ f i Ϫ˰˰̯ Ζ˰˰γ΍ Mean = Ϧ

 xi   xi f i ϴ̴ϧΎ˰˰˰˰ϴϣ Ζ˰˰˰˰γ΍ ϩΩ΍Ω ή˰˰˰˰ϫ ϲ˰˰˰˰ϧ΍ϭ΍ήϓ n  fi

( xi  x ) 2 variance = 

βϧΎ ϳ έ΍ϭ ( x  x )  0  i n

ϡή̳ Ϯ˰Ϡϴ̯ Ύ˰ϫ ϩΩ΍Ω Ϊ˰Σ΍ϭ ή˰̳΍ Ζ˰γ΍ ϲ ˰̰ϳ Ύ˰ϫ ϩΩ΍Ω ΪΣ΍ϭ ΎΑ ϥ΁ ΪΣ΍ϭ Ϫ̯ Ζγ΍ έΎϴ όϣ ϑ΍ήΤϧ΍ βϧΎϳ έ΍ϭ έάΟ ϭ ϩΩ΍Ω ϥϮ˰̩ ΪϨΘδ˰ϫ ϲ ΑϮ˰Χ ϱ Ύ˰ϫ κΧΎη έΎϴόϣ ϑ΍ήΤϧ΍ ϭ βϧΎϳέ΍ϭ Ζγ΍ ϡή̳ϮϠϴ̯ Ϣϫ έΎϴ όϣ ϑ΍ήΤϧ΍ ΪηΎΑ ΪϧήΛϮϣ ϥ΁έΩ Ύϫ R=max-min ΪηΎΑ ϲϣ Range Ύϳ Ε΍ήϴϴϐΗ ϪϨϣ΍Ω ϲ̳ΪϨ̯΍ή̡ ή̴ϳΩ κΧΎη ϲ̯έΎ̩ ήΒϧΎϴϣ ϭ ϢϴϨ̯ ϲϣ ϩΩΎϔΘγ΍ ϥ΁ ί΍ΪϨηΎΑ ϩΪϨ̯΍ή̡ ϲϠϴΧ Ύϫ ϩΩ΍Ω ή̳΍ Ϫ̯ Ζγ΍ ̭έΎ̩ ή̴ϳΩ κΧΎη

R  Q3  Q1

Ϫ˰ϧΎϴ ϣ ϥΎϤϫ ϡϭΩ ̭έΎ̩ ϭ ΪϨΘδϫ ήΘθϴ Α ϥ΁ί΍ Ύϫ ϩΩ΍Ω ˻̋ ϭ ήΘϤ̯ ϥ΁ ί΍ Ύϫ ϩΩ΍Ω ̀̋ ϲϨόϳ ϡϮγ ̭έΎ̩ Ζγ΍ ΪηΎΑ ϲϣ ϝΎϣήϧ ϪόϣΎΟ ϥ΁ ΪϨηΎΑ ̮ϳΩΰϧ Ϣϫ ϪΑ Ύϫ ϩΩ΍Ω Ϧϴ̴ϧΎϴϣ ϭ ϪϧΎϴϣ ή̳΍

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GNP Ύϳ ϲϠϣ κϟΎΧΎϧ Ϊϣ΁έΩ ϥ΍ΰϴϣ ϡϭΩ ήϴϐΘϣ Ωέ΍Ω ϱΩΪϋ ήϳΩΎϘϣ ϪΑ ιΎμΘΧ΍ Ϫ̯ ϢϴϨ̯ ϲϣ ΏΎΨΘϧ΍ Numeric ήϴϐΘϣ ωϮϧ Ύϳ Type έΩ Gross National Productˬ ˱ϼΜϣ Ω΍Ω ήϴϐΘϣ ΩέϮϣ έΩ ϲϓΎ̯ ΕΎΤϴοϮΗ ϥ΍ϮΘϴϣ label έΩ Ϣϴϧί ϲϤϧ ϱΰϴ̩ value ΖϤδϗ έΩ ϢϴϨ̯ ϲϣ ϒϳήόΗ ˺˹ ΍έ ϥϮΘγ ϱΎϨϬ̡ ςγϭ Ύϳ ̠̩ ˬ Ζγ΍έ ϢϴϨ̰ϴϣ ΏΎΨΘϧ΍ ΍έ Align ϢϴϨ̯ ϲϣ ΏΎΨΘϧ΍ ϲΒδϧ ϭ Scale ΍έ ήϴϐΘϣ αΎϴϘϣ Ύϳ Measure ΩέϮΧ ϲϣ ΰϴϤϣ Ϣϗέ˻ ΎΗ ϢϴϧΰΑ ˻ ή̳΍ ΍έ Decimal

1 2

Name

Type

country String GNP Numeric

Width

Decimal

Label

Value

8 8

2

country GNP

-

Missing column -

10 10

Align left left

Measure Nominal Scale

Analyze – Report –Case Summaries ϩή˰ΠϨ̡ ί΍ ζ˰Ϡϓ ς˰γϮΗ ΍έ ϱ έΎ˰ϣ΁ ϱ Ύ˰ϫ κΧΎ˰η Ϣϴ ˰ψϨΗ Ζ˰ϬΟ ή˰ψϧ ΩέϮϣ ήϴϐΘϣ Ϫ̯ ΩϮ η ϲ ϣ ίΎΑ ϱ ΍ ϩήΠϨ̡ κΧΎ˰η statistic Ϫ˰Ϥ̳Ω ϥΩί Ύ˰Α β̢˰γ ϭ Ϣϴ ˰ϫΩ ϲ ˰ϣ ϝΎ˰ϘΘϧ΍ Variable Ζ˰γ΍έ ΖϤγ ϩήΠϨ̡ ϪΑ ΩϮΟϮϣ ϭ kutosis ϲ̳Ϊϴ θ˰̯ ˬ skewness ϲ ̴ϟϮ̩ ˬ βϧΎϳ έ΍ϭ ˬϦϴ̴ϧΎϴ ϣ ˬ ϪϧΎϴ ϣ ΪϨϧΎϣ ΍έ ήψϧ ΩέϮϣ ϱ έΎϣ΁ ϱΎϫ ϢϴϨ̯ ϲϣ ΏΎΨΘϧ΍ Ϫ˰̩ήϫ ϭ Ϊ ˰ϳ ΪϨΒϧ ΍έ ϩή˰ΠϨ̡ Ϧϳ ΍ Ζγ΍ ήΘϬΑ ΩϮη ϲ ϣ ήϫΎχ output1 ϡΎϧ ϪΑ ϪϧΎ̳΍ΪΟ ϩήΠϨ̡ έΩ spss ϲΟϭήΧ Ϊη Ϊϫ΍ϮΧ ϪϓΎο΍ ϩήΠϨ̡ ϦϴϤϫ έΩ ΪϴϨ̯ ϪϓΎχ΍

Ύϫ κΧΎη ϪΒγΎΤϣ ϡϭΩ εϭέ Analyze – Descriptive statistics - frequencies ϢϴϨ̯ Ϣγέ ΰϴϧ ΍έ ϲϧ΍ϭ΍ήϓ ϝϭΪΟ Ϣϴϧ΍ϮΗ ϲϣ ΪϨΘδϫ ΎϬ̯Ϊλ Percentile ϭ Ύϫ ̮ϫΩ Cutpoints ˬ ΎϬ̯έΎ̩ Quartiles ϞϣΎη Percentile Values ϲ ˰ϣ ϊ ˰ϳίϮΗ έΎ ˰ϴόϣ Distribution ˬ ϲ ̳Ϊ˰Ϩ̯΍ή̡ έΎϴόϣ Dispersion ˬ ΰ̯ήϤΗ ϱΎϫ κΧΎη Central Tendency ΪϨηΎΑ ϢϴϨ̯ ΏΎδΣ ΍έ ϲ̳Ϊϴθ̯ ϭ ϲ̴ϟϮ̩ Ϣϴϧ΍ϮΗ ϲϣ ΪϨηΎΑ ΕϭΎϔΘϣ ϭ ϩΪϨ̯΍ή̡ Ύϫ ϩΩ΍Ω ή̳΍ ̮ ˰ϴΗ ΍έ Display frequency table Ϫ˰Ϩϳΰ̳ Ϊ ˰ϳΎΑ Ϣϴ ϫ΍Ϯ˰ΨΑ ΍έ ϲ ˰ϧ΍ϭ΍ήϓ ϝϭΪ˰Ο ϭ ΪϨ˰ηΎΑ ϱέ΍ή̰Η Ύϫ ϩΩ΍Ω ή̳΍ ϢϴϧΰΑ

ƾĭŶǀƄĩ ƩŚƯźƳ ŹŵřƺưƳ

ΪηΎΑ ϲϣ kutosis>0 ϩΪϴθ̯ ΖϟΎΣ έΩ ϭ kutosis<0 ϲΨ̡ ΖϟΎΣ έΩ Ζγ΍ Ϊϣ!ϪϧΎϴϣ!Ϧϴ̴ϧΎϴϣ ϭ skewness<0 ̠̩ ϪΑ ϲ̴ϟϮ̩ ΖϟΎΣ έΩ Ϧϴ̴ϧΎ ˰ϴϣ! Ϫ˰ϧΎϴϣ! Ϊ˰ϣ ϭ skewness>0 Ζ˰γ΍έ Ϫ˰Α ϲ ̴ϟϮ˰̩ Ζ˰ϟΎΣ έΩ Ζγ΍

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˻ϝΎΜϣ ϱ Ύ˰ϫ κΧΎ˰η β̢˰γ ΪϴϨ̯ Ωέ΍ϭ ϪΤϔλ έΩ ΍έ ήϳ ί ΕΎϋϼσ΍ ϭ ϩΩή̯ ϒϳ ήόΗ ΍έ ϥΎ̰ϣ ϭ ϥΎϣί ˬ Ϧϴ ηΎϣ ωϮϧ ϱ Ύϫ ήϴϐΘϣ ΍έ ϲϨϴ ˰ηΎϣ ωϮ˰ϧ Ϫ˰̩ ςγϮΗ ϭ ϥΎϣί ϡ΍Ϊ̯ έΩ ΩΩήΗ ϥ΍ΰϴ ϣ ϪΠϴΘϧ Ϊϴϳ ΎϤϧ ϪΒγΎΤϣ ΍έ ϲ ̳ΪϨ̯΍ή̡ ϭ ϊϳ ίϮΗ ˬϱ έΎϣ΁ ϒϠΘΨϣ ΪϴϳΎϤϧ κΨθϣ ΖΧϮγ ϥ΍ΰϴϣ ϥΎϣί ϥΎ̰ϣ ϦϴηΎϣ ωϮϧ ̌ ̋ ˺˻ Ρήσ ϞΧ΍Ω ϲμΨη ˺̋ ˺˼ Ρήσ ΝέΎΧ ϲμΨη ˺̊ ˺̊ Ρήσ ϞΧ΍Ω ϲμΨη ˺̊ ˺˼ Ρήσ ΝέΎΧ ϲδ̯ΎΗ ˻˹ ˺̊ Ρήσ ϞΧ΍Ω ϲδ̯ΎΗ ˻̌ ˺̋ Ρήσ ΝέΎΧ αϮΑϮΗ΍ ˼˹ ˺̌ Ρήσ ϞΧ΍Ω Ζϧ΍ϭ ˼˹ ˺̌ Ρήσ ΝέΎΧ αϮΑϮΗ΍ ̋˹ ˺̀ Ρήσ ϞΧ΍Ω ϲδ̯ΎΗ ̀ ˺̋ Ρήσ ΝέΎΧ ϲμΨη ́ ˺˼ Ρήσ ϞΧ΍Ω Ζϧ΍ϭ

Name

Type

Width

Decimal

Label

car

Numeric

8

0

car

2

place

Numeric

8

0

3

time

Numeric

8

2

4

fuel

Numeric

8

2

1

Value

1=ca 2=taxi 3=bus 4=vanet place 0=in center 1=out center time fuel

Missing column

Align

Measure

-

10

left

Nominal

-

10

left

Nominal

-

10

left

Scale

-

10

left

Scale

Ωέ΍Ω ΖϴϤϫ΍ ϲϧ΍ϭ΍ήϓ ϝϭΪΟ ϭ Ζδϴϧ ϢϬϣ ϱέΎϣ΁ ϱΎϫ κΧΎη Ϊϧ΍ ϩΪη ϱΪϨΑ Ϊ̯ Ϫ̯ ϲϳΎϫ ήϴϐΘϣ ϱ΍ήΑ valid percent ϭ percent ϥϮΘ ˰γ ϭΩ Ϧϴ ˰Α ϲ ˰ϧ΍ϭ΍ήϓ ϝϭΪ˰Ο έΩ Ϣϳ Ωή˰̯ ϲ ˰ϣ ϒ ˰ϳήόΗ missing ή̳΍ ΎΠϨϳ΍ έΩ Ύϣ΍ Ϊη ϲϣ ΩΎΠϳ΍ ΕϭΎϔΗ Ύ˰˰ϫ ϩΩ΍Ω ή˰˰̳΍ Ϫ˰˰̯ Ζ˰˰γ΍ ϲ˰ όϤΠΗ Ϊ˰˰λέΩ ϩΪ˰˰ϨϫΩ ϥΎθ˰˰ϧ ϲ˰ ϧ΍ϭ΍ήϓ ϝϭΪ˰˰Ο έΩ Cumulative percent ϥϮΘ˰˰γ Ωέ΍Ϊϧ ϲϨόϣ ϥϮΘγ Ϧϳ΍ ΪηΎΑ Nominal Ϣϴϫ΍ϮΧ ϲϣ Ύϣ Ύϣ΍ Ζγ΍ ϩΪη έ΍ή̰Η έΎΑ ̮ϳ ΩέϮϣ ήϫ ˱ΎΒϳήϘΗ ϭ ΪϫΪϴϤϧ ϥΎθϧ ϱΰϴ ̩ ΖΧϮγ ϲ ϧ΍ϭ΍ήϓ ϝϭΪΟ ϢϴϨ̯ ϲϣ ϲϓήόϣ ΍έ Gfuel ϡΎϧ ϪΑ ϱΪϳΪΟ ήϴϐΘϣ ΍άϟ ϢϴΠϨδΑ ϒϠΘΨϣ ϱΎϫ ϪϠλΎϓ έΩ Transform-Record-Into Different Variable ϲϣ ΍έ old and new value ϪϤ̳Ω β̢γ ˬϩΩί ΍έ change ϭ ϢϴϨ̯ ϲϣ ΝέΩ ΍έ ΪϳΪΟήϴϐΘϣ ΐδ̩ήΑ ϭ ϡΎϧ ΖϤδϗ έΩ Ϣϴϧί ̮ ˰ϳ Ϫ˰Α ̮ ˰ϳ ήχΎ˰ϨΗ ϭ Ϣϴ ˰ϫΩ ΖΒδ˰ϧ Ϊ ˰ϳΪΟ ή ˰ϴϐΘϣ Ϣϳ Ϊ˰ϗ ήϴϐΘϣ έ΍ΪϘϣ ήϫ ϪΑ Ϣϴϫ΍ϮΧ ϲϣ ή̳΍ Old value Ϊ ˰ϳ ΪΟ Ξ˰ϧέ ̮ ˰ϳ Ϣϳ Ϊ˰ϗ ή ˰ϴ ϐΘϣ έ΍Ϊ˰Ϙϣ ή˰ϫ Ϫ˰Α Ϣϴ ϫ΍Ϯ˰Χ ϲϣ ή̳΍ ϭ Ϣϴϧί ϲϣ ̮ϴ Η ΍έ value ϪϨϳ ΰ̳ Ϣϴ Ϩ̯ ΍έ΍ήϗήΑ ΎΠϨϳ΍ έΩ Ϫ̯ ϢϴϨ̯ ϲϣ ϩΩΎϔΘγ΍ Range ϪϨϳΰ̳ ί΍ ϢϴϨ̯ ϒϳήόΗ Range<10 1 10<Range<20 2 20<Range<30 3 Range>30 4

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Ύϫ ήϴϐΘϣ ΏΎΨΘϧ΍

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Ϣϴ ˰Ϩ̯ ϲϣ ϒϳήόΗ value έΩ Ϣθη ϥϮΘγ έΩ Gfuel ήϴϐΘϣ ϒϳήόΗ ΖϤδϗ έΩ β̢γ ϭ Ϣϴ ϧί ϲ ϣ ΍έ add ϭ ˮΖδϴ̩ ̊ ϭ ˺ˬ˻ˬ˼ Ω΍Ϊϋ΍ ί΍ έϮψϨϣ

Name

Type

Width

Decimal

Label

1

Gfuel

Numeric

8

0

Gfuel

Value 1=low 2=middle 3=high 4=very high

Missing column -

10

Align left

Measure Nominal

Ύ˰Η Ϣϴ ˰Ϩ̯ ϲ ˰ϣ ϪΒ˰γΎΤϣ fuelήϴϐΘϣ ϱ΍ήΑ ΍έ ϱέΎϣ΁ ϱΎϫ κΧΎη ϭ Gfuel ήϴϐΘϣ ϱ΍ήΑ ΍έ ϲϧ΍ϭ΍ήϓ ϝϭΪΟ ϝΎΣ ΪηΎΑ ϪΘη΍Ω ϲϨόϣ

Ʈǀƴĩ ũŹŵ ƾƬƇř ƶŰƠƇ Źŵ řŹŚƷ Ƶŵřŵ ƮǀƳřƺţ ƾƯ ŹƺƐģ ƮǀƃŚŝ ƶŤƃřŵ řŹ ƾƳřƹřźƟ ƩƹŶū źĭř çƩřƺŘſ ŚƸƷƹźĭ ŵřŶƘţ źƔƳ Żř ƹ ƶƬƇŚƟ ƩƺƏ źƔƳ Żř Ţƀǀģ ŚƷ Ƶŵřŵ ƽřźŝ ŮǀŰƇ ƽŶƴŝ ƵŵŹ ƶƤƿźƏ èƩřƺŘſ

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íë ææ çê ± ¹Á{ Ä ¸m ƾƳřƹřźƟ ƩƹŶū

X ŚƷ źǀƜŤƯ

f i ƾƳřƹřźƟ

ƮƬĜƿŵ æ

fi

f

ƾŞƀƳ ƾƳřƹřźƟ Śƿ ƩŚưŤůř

ƾƘưŬţ ƾƳřƹřźƟ

ƾƘưŬţ ƾŞƀƳ ƾƳřƹřźƟ

êå

å ç

i

ƮƬĜƿŵ ơƺƟ ç žƳŚƀǀƫ è …………….…… ……………………………………….. žƳŚƀǀƫ ơƺƟ é

Ζγ΍ ϝΎϤΘΣ΍ ϥΎϤϫ Ϫ̯ Ζγ΍ ΕϻΎΣ Ϟ̯ ϪΑ ΏϮϠτϣ ΕϻΎΣ Ω΍ΪόΗ ϩΪϨϫΩ ϥΎθϧ ϲΒδϧ ϲϧ΍ϭ΍ήϓ ϖ˰Βσ ή˰̳΍ ΍ά˰ϟ ϥ΁ Ϊ˰λέΩ ̶˰όϤΠΗ ̶Βδϧ ̶ϧ΍ϭ΍ήϓ ϭ ΪηΎΑ ̶ϣ ΩϮΧ ί΍ ϞΒϗ ΕϻΎΣ ωϮϤΠϣ ̶όϤΠΗ ̶ϧ΍ϭ΍ήϓ ΪϨΘδϫ βϧΎδϴϟ ήҨί ΕϼϴμΤΗ ̵΍έ΍Ω ˻˹ ΎҨ ήϔϧ ̋˹ Ϫ̯ ΩϮη ̶ϣ κΨθϣ βϧΎδϴϟ ̵΍ήΑϻΎΑ ϝϭΪΟ Ζγ΍ ϩΪη Ωέ΍ϭ missing Ϫ̯ Ζγ΍ ̶ҨΎϫ ϩΩ΍Ω ϪΑ ρϮΑήϣ Invalid Percent ϥϮΘγ

˼ϝΎΜϣ ΍έ ̶Ϡ˰λ΍ ϩΩ΍Ω ϝϭΪ˰Ο ϝΎΣ ΪηΎΑ ̶ϣ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ϦҨ΍ Ϫ̯ Ζγ΍ ήҨί Ρήη ϪΑ ϩέ΍Ω΍ ̮Ҩ ϥΎϨ̯έΎ̯ ΕϼϴμΤΗ Ζδϴϟ ΪϴϨ̯ ΩΎΠҨ΍ ̭έΪϣ Ϊ̯ Ω΍ΪόΗ ̶ϠϴμΤΗ ƮƬĜƿŵ æ ˺̋ ƮƬĜƿŵ ơƺƟ ç ˺̀ žƳŚƀǀƫ è ˺˹ žƳŚƀǀƫ ơƺƟ é ̋ řźŤĩŵ ê ˼

Ϣϴ˰Ϩ̯ ϒҨήόΗ ΍έ ̶ϧ΍ϭ΍ήϓ ϡΎϧ ϪΑ ̵ήϴϐΘϣ ϢҨέϮΒΠϣ Ύϣ΍ Ζγ΍ ̶ϠϴμΤΗ ̭έΪϣ ϥ΁ ϭ ϢҨέ΍Ω ήϴϐΘϣ ̮Ҩ Ύϣ ΎΠϨҨ΍ έΩ ϢϴϨ̯ Ωέ΍ϭ ϝϭΪΟ έΩ ΍έ ΕΎϋϼσ΍ ΎΗ

Ύϫ ήϴϐΘϣ ΏΎΨΘϧ΍

1 2

Name

madrak frequency

Type

Width

Decimal

Numeric Numeric

8 8

-

Label Value -

-

Missing -

column 10 10

Align left left

Measure Scale Scale

έ΍ή̰Η έΎΒ̰Ҩ ϡ΍Ϊ̯ ήϫ ϥϮ̩ Ϊϧί ̶ϣ ̮Ҩ ΍έ ϪϤϫ ϢϴϫΩ Ϟϴ̰θΗ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ madrak ήϴϐΘϣ ̵΍ήΑ ϥϻ΍ ή̳΍ ήϴϐΘϣ ϪΑ ΍έ ϥίϭ ϢϴϨ̯ ϒҨήόΗ έ΍Ω ϥίϭ ήϴϐΘϣ ΪҨΎΑ β̡ ϢҨέ΍Ω ϢϠ̢ҨΩ ήϔϧ ˺̋ Ύϣ Ϫ̯ ̶ΗέϮλ έΩ Ζγ΍ ϩΪη ΖγέΩ ϪΠϴΘϧ ϭ ϢҨήϴ̳ ̶ϣ madrak ήϴϐΘϣ αΎγ΍ ήΑ ΍έ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ̶ϟϭ ϢϴϫΩ ̶ϣ ΖΒδϧ ̶ϧ΍ϭήϓ ΩϮη ̶ϣ ϩΩ΍Ω ϥΎθϧ Data – Weight Case Ϫ̯ ϢϴϤϬϓ ̶ϣ ϪΤϔλ ϦϴҨΎ̡ Ζγ΍έ ΖϤγ έΩ Weight On ϪϧΎθϧ ΎΑ ϢϴϨ̯ ίΎΑ ΍έ ϪΤϔλ ϦҨ΍ Ϫ̯ ϥΎϣί ήϫ Ϊϧέ΍Ω ϥίϭ Ύϫ ϩΩ΍Ω

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řźŤĩŵ ê

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̊ϝΎΜϣ ΏΎδ˰Σ ΍έ Ϊ˰ϣ Ζϴδ˰ϨΟ ή˰ϴϐΘϣ ̵΍ή˰Α ϭ Ϧϴ̴ϧΎ˰ϴϣ ϥίϭ ϭ Ϧ˰γ ήϴϐΘϣ ̵΍ήΑ ϢҨέ΍Ω ΍έ ϥίϭ ϭ Ϧγ ˬΖϴδϨΟ ήϴϐΘϣ Ϫγ ΪϴϨ̯

Ύϫ ήϴϐΘϣ ΏΎΨΘϧ΍ Name

Type

Width

Decimal

Label

1

Gender

Numeric

8

-

-

2 3

Age Weight

Numeric Numeric

8 8

-

-

Value

Missing column

1=male 2=female -

Align

Measure

-

10

left

Nominal

-

10 10

left left

Scale Scale

ϝϭΪ˰Ο ̶ϟϭ Ζγ΍ ϩΪη έ΍ή̰Η έΎΒ̰Ҩ ϩΩ΍Ω ήϫ Ϫ̯ ΩϮη ̶ϣ κΨθϣ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ϥΪη κΨθϣ ί΍ β̡ ΍έ Ύ˰ϫ ϩΩ΍Ω ΪҨΎΑ Ϟ̰θϣ ϦҨ΍ ϊϓέ ̵΍ήΑ ΪηΎΑ ̋ ί΍ ήΘϤ̯ ϥ΁ ̵Ύϫ ϩΩ΍Ω ˻˹ ήΜ̯΍ΪΣ Ϫ̯ Ζγ΍ ΏϮΧ ̶ϧ΍ϭ΍ήϓ ΩϮη ήΘθϴΑ ̶ϧ΍ϭ΍ήϓ ΎΗ ϢϴϨ̯ ̵ΪϨΑ ϩΩέ įŶƴŝ ƵŵŹ Ʃƹř ƁƹŹ Max  Min 50  20   6 ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ϝϮϣήϓ ϦҨ΍ ί΍ Ϧγ ήϴϐΘϣ ̵ΪϨΑ ϩΩέ ̵΍ήΑ

5 5 ϪϨҨΰ̳ ί΍ ̵ΪϨΑ ϩΩέ ΩΎΠҨ΍ ̵΍ήΑ ΪҨ΁ ̶ϣ ΖγΩ ϪΑ ̌ ϩίΎΑ ϝϮσ ϭ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ̋ ΍έ ϩΩέ Ω΍ΪόΗ Transform – recode- into different variable ϢϴϨ̯ ̶ϣ ϒҨήόΗ Age group ϡΎϧ ϪΑ ΍έ ΪҨΪΟ ήϴϐΘϣ ̋̊ ̊́ ̊̀ ̊˺ ̊˹ ˼̊ ˼˼ ˻̀ ˻˹ ˻̌ ̶ϣ ϩΩΎϔΘγ΍ Ζγ΍ ϩΪη ̵ΪϨΑ ϩΩέ Ϫ̯ Age group ΪҨΪΟ ήϴϐΘϣ ί΍ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ϪΒγΎΤϣ ̵΍ήΑ ϪΘ̰ϧ  ϩΩΎϔΘ˰γ΍Age ̶˰ϨόҨ ̶˰ϟϭ΍ ή˰ϴϐΘϣ ί΍ Ϧϴ̴ϧΎ˰ϴϣ Ϟ˰Μϣ ̵έΎ˰ϣ΁ ̵Ύ˰ϫ κΧΎ˰η ϪϴϘΑ ϪΒγΎΤϣ ̵΍ήΑ ̶ϟϭ ϢϴϨ̯ ϢϴϨ̯ ϦϴϴόΗ ϩΪϨҨΎϤϧ ϩΩέ ήϫ ̵΍ήΑ ή̴ϣ Ωέ΍Ϊϧ ̶Ϩόϣ ϩΪη ̵ΪϨΑ ϩΩέ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ΍ήҨί Ωή̯ Ϣϴϫ΍ϮΧ

įŶƴŝ ƵŵŹ

ƵŵŹ ƵŶƴƿŚưƳ

İƳřƹřźƟ

20  26  23 2

˻̀ ˼˼ 27  33  30

2  fi xi Ζγ΍ ϡ΍ i ϩΩέ ϩΪϨҨΎϤϧ x ˬϦϴ̴ϧΎϴϣ ϝϮϣήϓ ϖΒσ ϭ X  i  fi

˻˹ ˻̌

ϢϴϨ̯ ̶ϣ κΨθϣ value ΖϤδϗ έΩ ΍έ ϥ΁ Variable view ΖϤδϗ έΩ Age group ΪҨΪΟ ήϴϐΘϣ ϒҨήόΗ ΎΑ

Name

Type

Width

Decimal

Label -

1

Gender

Numeric

8

-

2 3 4

Age Weight Agegroup

Numeric Numeric Numeric

8 8 8

-

Value

1=male 2=female grouping 1=20-26 variable 2=27-33 for age 3=34-40 4=41-47 5=48-54

Missing column Align

Measure

-

10

left

Nominal

-

10 10 10

left left left

Scale Scale Scale

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Ϫ˰Α ̶˰ϧ΍ϭ΍ήϓ ϝϭΪ˰Ο Ϊ˰η Ϊ˰ϫ΍ϮΧ ήΘϬΑ ̶ϠΒϗ ϝϭΪΟ ί΍ Ϫ̯ ϢϴϨ̯ ̶ϣ Ϣγέ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ΪҨΪΟ ήϴϐΘϣ ̵΍ήΑ Ζ˰γΪΑ ̵ήΘϬΑ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ΎΗ Ωή̯ ήΘ̳έΰΑ ΍έ ϩΩέ ϝϮσ ϭ ήΘϤ̯ ΍έ Ύϫ ϩΩέ ϥ΍ϮΘϴϣ Ωέ΍Ω ̶̴ΘδΑ Ύϣ ̵Ύϫ ϩΩέ Ωέϭ΁ grouping variable for age

Frequency Valid

Percent

Cumulative Percent

Valid Percent

20-26

1

10.0

10.0

10.0

27-33

5

50.0

50.0

60.0

34-40

2

20.0

20.0

80.0

41-47

1

10.0

10.0

90.0 100.0

48-54 Total

1

10.0

10.0

10

100.0

100.0

Transform - visual Bander Śƿ visual Binning ΩϮη ̶Ϥϧ ϡΎΠϧ΍ Nominal ήϴϐΘϣ ̵΍ήΑ ̵ΪϨΑ ϩΩέ Ϫ̯ ΪϴηΎΑ ϪΘη΍Ω ΖϗΩ visual BinningΪҨΪΟ ϩήΠϨ̡ έΩ Ϣϴϧΰϴϣ continue ϭ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ weight ήϴϐΘϣ ϩΪη ίΎΑ ϩήΠϨ̡ έΩ ϞΤϣ ϭ ρΎϘϧ Make cutpoints ϪϤ̳Ω ϥΩί ΎΑ ϝΎΣ gweight ϢϴϨ̯ ̶ϣ Ωέ΍ϭ ΍έ ΪҨΪΟ ήϴϐΘϣ ΐδ̩ήΑ ϭ Ϣγ΍ ϢҨίΎγ ̶ϣ ΍έ Ζδ̰η

Ωέ΍Ω ΩϮΟϭ Ζδ̰η ρΎϘϧ ϦϴϴόΗ ̵΍ήΑ ϩ΍έ Ϫγ Equal Width Intervals ˺ Ϫ˰̯ ϢϴϫΪ˰ϴϣ Ζ˰γ΍ ̊˹ Ύ˰ΠϨҨ΍ έΩ ϭ ΪηΎΑ Ύϣ min ΍έ ϝϭ΍ ϪτϘϧ ̵Ϊ˰˰όΑ Ϣϴ˰˰Ϩ̯ Ωέ΍ϭ ΍έ ˼̂ ̋ ΩΪ˰˰ϋ ̶̴Θ˰˰γϮϴ̡ ̵΍ή˰˰Α Ζ˰˰γ΍ ή˰˰ΘϬΑ ϩΩέ ϝϮ˰σ ̶ϣϮ˰γ ϭ Ϣϴ˰Ϩ̯ ̶˰ϣ Ωέ΍ϭ ˼ Ϫ̯ Ζγ Ύϫ ϩΩέ Ω΍ΪόΗ Ω΍Ϊ˰όΗ Ϊ˰ϴϫΪΑ ΍έ ϩΩέ ϝϮ˰σ ή˰̳΍ Ϊδ˰ҨϮϧ ̶˰ϣ εΩϮΧ Ϫ̯ Ζγ΍ ΖϤδϗ έΩ ΍έ ϩΩέΩΪϋ ϦҨήΧ΁ ϭΪϫΩ ̶ϣ εΩϮΧ ΍έ ϩΩέ .ΪϫΪϴϣ ϥΎθϧ εΩϮΧ ΍έ Last cutpoint location

Equal Percentiles Based on Scanned Cases 2 ΍έ Ωήϴ̴ϴϣ max-min ϪϠλΎϓ ί΍ ϩΩέ ήϫ Ϫ̯ ̵ΪλέΩ ϭ ϩΩέ Ω΍ΪόΗ ϢϴϫΪϴϣ Cutpoints at Mean and Selected Standard Deviation ˼ Based on Scanned Cases

ΪϨ̰ϴϣ ϦϴϴόΗ ΍ έΎϫ ϩίΎΑ έΎϴόϣ ϑ΍ήΤϧ΍ Ϫγ ΎҨ ϭΩˬ̮Ҩ αΎγ΍ ήΑ

΍έ ϩΩέ n+1 Ω΍Ϊ˰όΗ Ζδ˰̰η Ϫ˰τϘϧ n Ω΍Ϊ˰όΗ ΩϮη ϩΩΎϔΘγ΍ Ϫ̯ ϪϨҨΰ̳ Ϫγ ϦҨ΍ ϡ΍Ϊ̯ ήϫ ί΍ ϝΎΣήϫ ϪΑ ϪΘ̰ϧ Ζη΍Ω Ϊϫ΍ϮΧ ΪϧϮη ̶ϣ ήϫΎχ Ϣϫ Ύϫ ϩΩέ ϩήΠϨ̡ ϥΎϤϫ έΩ Make Label ϪϤ̳Ω ϥΩί ΎΑ 8 www.ircdvd.com

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( έΎ˰ϴόϣ ϑ΍ή˰Τϧ΍ ϭ) έΎ˰ϴόϣ ϑ΍ή˰Τϧ΍

Ϧϴ̴ϧΎ˰ϴϣ ήҨΩΎ˰Ϙϣ Ύ˰Α ̶˰ϨΤϨϣ ϦҨ΍ ωΎϔΗέ΍ Ωέ΍Ω ρΎΒΗέ΍ ϭ έ΍Ϊ˰Ϙϣ ̮˰Ҩ ϝϮ˰Σ Ύ˰ϫ ϩΩ΍Ω Ϣ̯΍ήΗ ϥ΍ΰϴϣ Ζγ΍ Ϧϴ̴ϧΎϴϣ ί΍ Ύϫ ϩΩ΍Ω ̶̳ΪϨ̯΍ή̡ ϥ΍ΰϴϣ

ϥ΍ίϮ˰ϣ΁ ζ˰ϧ΍Ω Ε΍ή˰Ϥϧ ̶˰γέΩ ϥΎ˰ΤΘϣ΍ ̮˰Ҩ έΩ ϝΎ˰Μϣ ̵ ϥ΍ϮϨϋ ϪΑ ΖϤ˰γ Ϫ˰Α Ϫ˰̩ ή˰ϫ ϭ Ϊ˰ηΎΑ ̶˰ϣ ή Θθ˰ϴΑ Ϧϴ̴ϧΎ˰ϴϣ ϑ΍ή˰σ΍ ΐϠϏ΍ ΍έ Ε΍ή˰Ϥϧ Ϧ˰Ҩ΍ Ϫ˰̯ ̵Ω΍ή˰ϓ΍ Ω΍Ϊ˰όΗ ϢҨϭή˰Α ζϴ˰̡ ϦϴҨΎ̡ ΎҨ ϻΎΑ Ε΍ήϤϧ ̮˰Ҩ Ύ˰Α ϥ΍ϮΗ ̶ϣ ΖϟϮϬδΑ ΍έ έΎΘϓέ ϦҨ΍ ΩϮη ̶ϣ ήΘϤ̯ Ϊϧ΍ ϪΘϓή̳ .Ωή̯ ϝΪϣ ϝΎϣήϧ ϊҨίϮΗ

ŶƿŶū źǀƜŤƯ įƹŹ źŝ ƍƹźƄƯ šŚŞſŚŰƯ ƭŚŬƳř ƹ ŶƿŶū źǀƜŤƯ ŵŚŬƿř ƁƹŹ ϢҨΩή̯ ̶ϣ ϒҨήόΗ ΍έ ̵ΪҨΪΟ ήϴϐΘϣ Transform – recode- into different variable εϭέ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϢҨέϭΎϴΑ ΖγΪΑ ΍έ ̵ΪҨΪΟ ήϴϐΘϣ ϪΒγΎΤϣ εϭέ ΎΑ Ϣϴϫ΍ϮΧ ̶ϣ ϝΎΣ ̶ϟϭ

Transform – Compute Numeric ΖϤδ˰˰ϗ έΩ ΍έ ϝϮ˰˰ϣήϓ ϭ Ϣϴ˰˰Ϩ̯ ̶˰˰ϣ ΝέΩ ΍έ Ϊ˰˰ҨΪΟ ή˰˰ϴϐΘϣ ϡΎ˰˰ϧ Target Variabele ΖϤδ˰˰ϗ έΩ ΏΎδΣ ϡή̳ ΐδΣ ήΑ ̵ή̴ҨΩ ϥϮΘγ έΩ ΍έ Weight ήϴϐΘϣ Ϣϴϫ΍ϮΧ ̶ϣ ή̳΍ ˱ϼΜϣ ϢϴδҨϮϧ ̶ϣ Expression ϢϴϨ̯ ΏΎΨΘϧ΍ ΍έ ιΎΧ ̶όΑΎΗ ΎҨ Weight  1000 ϢϴδΑϮϨΑ ΍έ ϝϮϣήϓ ϦҨ΍ ̶ϧ΍ϮΗ ̶ϣ ϢϴϨ̯ ΩϮ˰η ̶Ϥϧ ΍ήΟ΍ ϝϮϣήϓ ϩΩ΍Ω ήϴϴϐΗ ΎΑSPSS έΩ Ϫ̯ Ζγ΍ ϦҨ΍ Excel ΎΑ SPSS έΩ ̶δҨϮϧ ϝϮϣήϓ ϕήϓ ϪΘ̰ϧ ή˰ϴϴϐΗ ΍έ ϩΩ΍Ω ϩΎ˰̳ ή˰ϫ Excel έΩ ̶˰ϟϭ ΩϮ˰η ΍ή˰Ο΍ ϝϮ˰ϣήϓ ˱΍ΩΪ˰Πϣ ΖδҨΎΑ ̶ϣ ϭ ΩΩή̳ ̶Ϥϧ ίϭέ ϪΑ ϪΠϴΘϧ ϭ ΪϨ̯ ̶ϣ ήϴϴϐΗ ΰϴϧ ϪΠϴΘϧ ϢϴϫΩ Ϊ˰Σ΍ϭ ή˰ϴϴϐΗ ˱ϻΎΜϣ ΩϮη ϡΎΠϧ΍ ̶λΎΧ ̵Ύϫ ϩΩ΍Ω ̵΍ήΑ ςϘϓ ϭ ̶λΎΧ ςҨ΍ήη ΖΤΗ ϝϮϣήϓ ϦҨ΍ Ϣϴϫ΍ϮΨΑ ή̳΍ ˱ϼΜ˰ϣ Ϣϴ˰Ϩ̯ ϩΩΎϔΘ˰γ΍ Compute ϩή˰ΠϨ̡ ϦϴҨΎ˰̡ if Ϫ˰Ϥ̳Ω ί΍ ΪҨΎΑ ΩϮη ϡΎΠϧ΍ ΎϬϤ ϧΎΧ ̵΍ήΑ ςϘϓ ϡή̳ ϪΑ ϥίϭ Gender =1& Age>30 ΎҨ Gender =1 Ϫ̯ ϢҨέ΍ΰ̴Α ρήη ̵΍ή˰Α ϭ ϢҨ΍ έά˰̴Α ˺ ̶˰λΎΧ ̵Ύ˰ϫ ϩΩ΍Ω ̵΍ήΑ ϢϴϨ̯ ϒҨήόΗ select ϡΎϧ ϪΑ ήϴϐΘϣ ̮Ҩ Ϣϴϧ΍ϮΗ ̶ϣ Ϫ̰ϨҨ΍ ή̴ҨΩ ϩ΍έ Ύϫήϔλ ̵΍ήΑ ΎҨ ϢϴϨ̯ ΏΎδΣ ΎϬ̰Ҩ ̵΍ήΑ ΍έ ϝϮϣήϓ ΪόΑ ˹ ή̴ҨΩ ̵Ύϫ ϩΩ΍Ω

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Data view ϪΤϔ˰λ έΩ Ϊ˰ҨΪΟ ή˰ϴϐΘϣ Compute ϩή˰ΠϨ̡ ί΍ ϩΩΎϔΘ˰γ΍ ΎΑ Ϫ̯ ΖδϨҨ΍ ̶ϠΒϗ ϩ΍έ ΎΑ ϩ΍έ ϦҨ΍ ϕήϓ ϥΩή˰̯ ή˰ΘϠϴϓ ωϮ˰ϧ ̮˰Ҩ Ϧ˰Ҩ ΍ ϭ ΩϮ˰η ̶ϣ ϞλΎΣ Ε΍ήϴϴϐΗ output ϭ ̶ΟϭήΧ έΩ ϩ΍έ ϦҨ΍ ΎΑ ̶ϟϭ ΩϮθϴϣ ΩΎΠҨ΍ Ζγ΍

ϝϭ΍ ε ϭέ

Data – Select Cases Ϫ˰Α Ϊ˰ηΎΑ ϝΎ˰όϓ All Cases ή˰̳΍ Ϫ˰̯ ΩϮ˰η ̶ϣ ίΎΑ ϭήΑϭέ ϩήΠϨ̡ if condition is ϥΩή˰̯ ϝΎ˰όϓ Ύ˰Α ϭ Ζ˰γ΍ ή˰ΘϠϴϓ ϡΪ˰ϋ ̵Ύ˰Ϩόϣ ̶˰ϣ ρή˰η ˱ϼΜ˰ϣ Ϣϴ˰Ϩ̯ ̶ϣ ΝέΩ ΍έ ήψϧ ΩέϮϣ ρήη satisfied ϡΎ˰Πϧ΍ Ύ˰Α ϝΎ˰Σ ή˰̳΍ Ϫ˰̯ Ϊ˰Ϩ̯ ϪΒ˰γΎΤϣ ΍έ ΎϫΩήϣ ςϘϓ ϢҨέ΍ά̳ ϭ Ϊ˰Ϩ̰ϴϣ ϪΒ˰γΎΤϣ ΎϫΩήϣ ̵΍ήΑ ςϘϓ ϢҨήϴ̴Α Ϧϴ̴ϧΎϴϣ ή̳΍ ήΘϠϴϓ ϭ Ϊθ˰˰̯ ̶˰˰ϣ ς˰˰Χ ΍έΎ˰˰Ϭϧί ̵Ύ˰˰ϫ ΩέϮ˰˰̯έ Ϣ˰˰ϫ Data view έΩ ̵΍ή˰˰Α ϥΪ˰˰η ΏΎ˰˰ΨΘϧ΍ ϥ΁ έΩ Ϫ˰˰̯ Ϊ˰˰Ϩ̯ ̶˰˰ϣ ΩΎ˰˰ΠҨ΍ ̶ϧϮΘ˰˰γ ΪϫΩ ̶ϣ ϥΎθϧ ΍έ ΕΎΒγΎΤϣ

ΪϨ̯ ̶ϣ ΏΎΨΘϧ΍ ̶ϓΩΎμΗ ϪϧϮϤϧ ̮Ҩ Random of cases ΏΎΨΘϧ΍ ΎΑ ϪΤϔ˰λ ϦϴҨΎ˰̡ έΩ ϭ ΩϮ˰η ̶˰ϣ ϡΎΠϧ΍ ήΘϠϴϓ ok ϥΩίΎΑ ϭ use filter available έΩ ήΘϠϴϓ ΩέϮϣ ήϴϐΘϣ ΏΎΨΘϧ΍ ΎΑ Ϊη Ϊϫ΍ϮΧ ϩΩ΍Ω ζҨΎϤϧ filter on ΰϴϧ

ϡϭΩ ε ϭέ ΪόΑ ϢҨέ΍Ω ̶ϣ ήΑ All Cases ϪϨҨΰ̳ ϥΩί ϭ΍ Data – Select Cases εϭέ ΎΑ ΍έ ̶ϠΒϗ ̵Ύϫ ήΘϠϴϓ ϝϭ΍ Data – Split Files

Ζγ΍ ήΘϠϴϓ ϡΪϋ ϝϭ΍ ϪϨҨΰ̳ ̶˰˰ΟϭήΧ έΩ ̵΍ Ϫδ˰˰ҨΎϘϣ Ζ˰˰ϟΎΣ έΩ ΍έ ϝϭ΍Ϊ˰˰Ο ϡϭΩ Ϫ˰˰ϨҨΰ̳ ΪϫΩ ̶ϣ ϥΎθϧ Ϊη Ϊϫ΍ϮΧ ϩΩ΍Ω ϥΎθϧ ϪϧΎ̳΍ΪΟ ˱ϼϣΎ̯ ϡϮγ ϪϨҨΰ̳ ϥΎθ˰ϧ Ύ˰Ϭϧί ϭ Ύ˰ϫΩήϣ ΖϤδ˰ϗϭΩ Ϫ˰Α ΍έ gender ήϴϐΘϣ Ϫ̯ Ω΍Ω Ϊϫ΍ϮΧ

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̶ϣ ΝέΩ Ύϣ ϥΎϣήϓ ϭ ϩΪη ίΎΑ syntax ϩήΠϨ̡ ˬ Ε΍έϮΘγΩ ί΍ ̵΍ ϩήΠϨ̡ ήϫ έΩ pasteϪϤ̳Ω ί΍ ϩΩΎϔΘγ΍ ΎΑ ϊϗϮϣ Ζγ΍ ήΘϬΑ Ωή̯ ΍ήΟ΍ ˱΍ΩΪΠϣ ΍έ Ϧϴϣ΍ήϓ Run έϮΘγΩ ΎΑ ϥ΍ϮΗ ̶ϣ ϩήΠϨ̡ ϦҨ΍ ΩΪΠϣ ϥΩή̯ίΎΑ ΎΑ ̶ΘΣ ΩϮη ΩϮη ΝέΩ ϥ΁ έΩ Ε΍έϮΘγΩ ΎΗ ΪηΎΑ ίΎΑ Ϣϫ syntax ϪΤϔλ spss ϪΤϔλ ϥΩή̯ ίΎΑ

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ΪϴϨ̯ ίΎΑ ήҨί ήϴδϣ ί΍ ΍έ Demo ϞҨΎϓ 5ϝΎΜϣ My computer – win(programming) - program files - spssEVAL – Tutorial – sample files- Demo ϭ Ύ˰ϫ ϢϧΎ˰Χ Ϊ˰ϣ΁έΩ ϥ΍ΰϴϣ Ϧϴ̴ϧΎϴϣ β̢γ ΪҨέϭ΁ ΖγΩ ϪΑ Ύϫ ήϴϐΘϣ Ω΍ΪόΗ ˬ Ύϫ case Ω΍ΪόΗ ΩέϮϣ έΩ ΍έ ϡίϻ ΕΎϋϼσ΍ ή˰Α ̶˰ϧ΍ϭ΍ήϓ ϝϭΪ˰Ο Ϊ˰ҨέϭΎϴΑ ΖγΪΑ ϦϴηΎϣ ωϮϧ αΎγ΍ ήΑ ΍έ Ϊϣ΁έΩ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ϭ ΪϴϨ̯ ΏΎδΣ ϪϧΎ̳΍ΪΟ ΍έ ϥΎҨΎϗ΁ ΪҨέϭΎϴΑ ΖγΪΑ ϩΪη ̵ΪϨΑ ϩΩέ Ϊϣ΁έΩ αΎγ΍

ƪƿŚƟ Ĩƿ Żř šŚƗLjƏř Ÿųř File – display data files information ϝϭ΍ εϭέ Analyze-Report – Case Summaries ϡϭΩ εϭέ ϪϧΎ̳΍ΪΟ Ϟ̰η ϪΑ ΎϬϧί ϭ ΎϫΩήϣ Ϊϣ΁έΩ ϥ΍ΰϴϣ Ϧϴ̴ϧΎϴϣ ̵΍ήΑ ϒϟ΍

Gender = Female

Gender = Male

Statistics(a) Household income in thousands N Valid 3179 Missing Mean

Statistics(a) Household income in thousands N Valid 3221 Missing

0 Mean

68.7798

a Gender = Female

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Data – Split Files – gender- organize output by groups- ok Ϧϴ̴ϧΎϴϣ ϪΒγΎΤϣ ̵΍ήΑ ̶ϟ ϭ ϢϴϨ̯ ̶ϣ ΏΎδΣ inccat ΎҨ Ϊϣ΁έΩ ήϴϐΘϣ ̵΍ήΑ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ β̢γ ok β̢γ ϭ Ϣϴϧί ̶ϣ ̮ϴΗ ΍έ Mean ϪϨҨΰ̳ statistics ΖϤδϗ έΩ income ήϴϐΘϣ ΐδΣ ήΑ ΍έ ̶ϧ΍ϭ΍ήϓ ΩϮη ̶Ϥϧ ϪΒγΎΤϣ ϩΪη ̵ΪϨΑ ϩΩέ ήϴϐΘϣ ̵΍ήΑ Ϧϴ̴ϧΎϴϣ

0 70.1608

a Gender = Male

Income category in thousands(a)

Under $25

563

17.7

17.7

Cumulative Percent 17.7

$25 - $49

1208

38.0

38.0

55.7

$50 - $74

572

18.0

18.0

73.7

$75+

836

26.3

26.3

100.0

Total

3179

100.0

100.0

Frequency Valid

Percent

Valid Percent

a Gender = Female Income category in thousands(a)

Valid

Frequency 611

Percent 19.0

Valid Percent 19.0

Cumulative Percent 19.0

$25 - $49

1180

36.6

36.6

55.6

$50 - $74

548

17.0

17.0

72.6

$75+

882

27.4

27.4

100.0

Total

3221

100.0

100.0

Under $25

a Gender = Male

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

ϦϴηΎϣ ωϮϧ ΐδΣ ήΑ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ Ώ ϦҨ΍ ςγϮΗ ΪϫΪϴϣ ϥΎθϧ ΍έ ϦϴηΎϣ ω΍Ϯϧ΍ Ϫ̯ carcat ήϴϐΘϣ β̡ ϢϴϨ̯ ήΘϠϴϓ ΍έ ϦϴηΎϣ ω΍Ϯϧ΍ ΪҨΎΑ ˱ϻϭ΍ Data – Split Files –carcat - organize output by groups- ok έϮΘγΩ ϢϴϨ̯ ̶ϣ ΏΎδΣ ΍έ inccat ΎҨ ϩΪη ̵ΪϨΑ ϩΩέ Ϊϣ΁έΩ ήϴϐΘϣ ̶ϧ΍ϭ΍ήϓ έϮΘγΩ ΪόΑ ϢϴϨ̯ ̶ϣ ήΘϠϴϓ Income category in thousands(a) a Primary vehicle price category = Economy

Valid

Frequency 1174

Under $25 $25 - $49 Total

Percent 63.8

Valid Percent 63.8

Cumulative Percent 63.8 100.0

667

36.2

36.2

1841

100.0

100.0

Income category in thousands(a) a Primary vehicle price category = Standard

$25 - $49

1721

75.6

75.6

Cumulative Percent 75.6

$50 - $74

554

24.4

24.4

100.0

2275

100.0

100.0

Frequency Valid

Valid Percent

Income category in thousands(a) - a Primary vehicle price category = Luxury

Valid

Frequency 566

$50 - $74

Percent 24.8

Valid Percent 24.8

Cumulative Percent 24.8 100.0

$75+

1718

75.2

75.2

Total

2284

100.0

100.0

ƵŶƃ źŤƬǀƟ Śƿ split ŢƫŚů Źŵ carcat źǀƜŤƯ Śƿ ƲǀƃŚƯ ƕƺƳ ŽŚſřźŝ inccat źǀƜŤƯ Śƿ ƵŶƃ įŶƴŝ ƵŵŹ ŶƯōŹŵ İƳřƹřźƟ ƶŞſŚŰƯ Income category in thousands Primary vehicle price category Economy

Valid

Under $25 $25 - $49

Standard

Valid

Luxury

Valid

Total $25 - $49 $50 - $74 Total $50 - $74 $75+ Total

Cumulative Percent 63.8

Frequency 1174

Percent 63.8

Valid Percent 63.8

667 1841 1721 554 2275 566

36.2 100.0 75.6 24.4 100.0 24.8

36.2 100.0 75.6 24.4 100.0 24.8

100.0

1718 2284

75.2 100.0

75.2 100.0

100.0

75.6 100.0 24.8

ŪƿŚŤƳ

ŶƳŹřŵ ŹLJŵ çêååå źƿŻ ŶƯōŹŵ ŶƳŹřŵ Economy ƩŶƯ ƲǀƃŚƯ ƕƺƳ ƶĩ ëè í æ

ŶƳŹřŵ ŹLJŵ ÔÒÍÍÍ įLJŚŝ ŶƯōŹŵ Ŷƴƴĩ İƯ ƵŵŚƠŤſř Luxury ƲǀƃŚƯ ƕƺƳ Żř ƶĩ ÔÒ Ï Ï

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Total

Percent

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

ƵŶƃ źŤƬǀƟ Śƿ split ŢƫŚů Źŵ martial źǀƜŤƯ Śƿ ƪƷŚţ ƕƺƳ ŽŚſřźŝ inccat źǀƜŤƯ Śƿ ƵŶƃ įŶƴŝ ƵŵŹ ŶƯōŹŵ ƲǀĮƳŚǀƯ ƶŞſŚŰƯ Income category in thousands

Marital status Unmarried

Married

Valid

Valid

Frequency 578

Percent 17.9

Valid Percent 17.9

Cumulative Percent 17.9

$25 - $49

1228

38.1

38.1

56.0

$50 - $74

552

17.1

17.1

73.1 100.0

Under $25

$75+

866

26.9

26.9

Total

3224

100.0

100.0

Under $25

596

18.8

18.8

18.8

$25 - $49

1160

36.5

36.5

55.3

$50 - $74

568

17.9

17.9

73.2 100.0

$75+

852

26.8

26.8

Total

3176

100.0

100.0

ŶƳŹřŵ ŹLJŵ çêååå éîååå Ʋǀŝ ŶƯōŹŵ ŶƴŤƀƷ ŵźŬƯ ƶĩ èí æ æ ŶƳŹřŵ ŹLJŵ ìêååå įLJŚŝ Ʋǀŝ ŶƯōŹŵ ŶƴŤƀƷ ƪƷŚŤƯ ƶĩ çë í Ï

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ŪƿŚŤƳ

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

agegroup ΪҨΪΟ ήϴϐΘϣ ΩΎΠҨ΍ ϭ Ϧγ ήϴϐΘϣ ̵ΪϨΑ ϩΩέ Transform-Visual binning – cutpoint 17.5 ƵŶƃ źŤƬǀƟ Śƿ split ŢƫŚů Źŵ agegroup źǀƜŤƯ Śƿ İƴſ ƵŵŹ ŽŚſřźŝ inccat źǀƜŤƯ Śƿ ƵŶƃ įŶƴŝ ƵŵŹ ŶƯōŹŵ ƲǀĮƳŚǀƯ ƶŞſŚŰƯ

Income category in thousands

Age in years (Binned) 19 - 28

39 - 48

49 - 58

59 - 68

69+

Valid

Valid

Valid

Valid

Valid

Valid

Percent

Valid Percent

Cumulative Percent

Under $25

410

52.3

52.3

52.3

$25 - $49

319

40.7

40.7

93.0

$50 - $74

41

5.2

5.2

98.2 100.0

$75+

14

1.8

1.8

Total

784

100.0

100.0

Under $25

331

19.1

19.1

19.1

$25 - $49

929

53.5

53.5

72.6

$50 - $74

293

16.9

16.9

89.5 100.0

$75+

182

10.5

10.5

Total

1735

100.0

100.0

Under $25

124

7.0

7.0

7.0

$25 - $49

641

36.0

36.0

43.0

$50 - $74

453

25.4

25.4

68.4 100.0

$75+

563

31.6

31.6

Total

1781

100.0

100.0

Under $25

73

5.6

5.6

5.6

$25 - $49

341

26.0

26.0

31.6

$50 - $74

247

18.8

18.8

50.4 100.0

$75+

650

49.6

49.6

Total

1311

100.0

100.0

Under $25

169

26.0

26.0

26.0

$25 - $49

135

20.8

20.8

46.8

$50 - $74

75

11.5

11.5

58.3 100.0

$75+

271

41.7

41.7

Total

650

100.0

100.0

Under $25

67

48.2

48.2

48.2

$25 - $49

23

16.5

16.5

64.7

$50 - $74

11

7.9

7.9

72.7 100.0

$75+

38

27.3

27.3

Total

139

100.0

100.0

‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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29 - 38

Frequency

ŪƿŚŤƳ ŹLJŵ ìêååå įLJŚŝ ŶƯōŹŵ êå ĨƿŵżƳ éí êí İƴſ ƵŵŹ Źŵ ƶĪǀƫŚů Źŵ ŶƳŹřŵ ŶƯōŹŵ ŹLJŵ çêååå źƿŻ êç è æî çí İƴſ ƵŵŹ Źŵ æ . ŶƳŹřŵ

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

íë æç ç ± ¹Â Ä ¸m ƶƳƺưƳ ƭŚƀƣř Ϫ̯ Ζγ΍ ϪόϣΎΟ ̮Ҩ ί΍ ήμϨϋ n ϞϣΎη ϩΩΎγ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ˺ Ωέ΍ΩήμϨϋ ήϫ ΏΎΨΘϧ΍ ̵΍ήΑ ̵ϭΎδϣ βϧΎη ΎϬϴ̳̬Ҩϭ ̶ϓΩΎμΗ ζҨΎϣί΁ Ωέ΍Ω ̶ҨΎΗ n ϪϧϮϤϧ ήϫ ΏΎΨΘϧ΍ ̵΍ήΑ ̵ϭΎδϣ βϧΎη ϝϭΪΟ ί΍ ϩΩΎϔΘγ΍ ΎΑ Ϣ̯ ϪϨҨΰϫ ΎΑ ̵έ΍ά̳ ϩέΎϤη ϞΑΎϓ ΎҨ ϩΪη ̵έ΍ά̳ ϩέΎϤη ϪόϣΎΟ ϩΩΎϔΘγ΍ Ωέ΍Ϯϣ ̶ϓΩΎμΗ Ω΍Ϊϋ΍ Ϣ̯ ϪϨҨΰϫ ϭ Ζϋήγ ϪΑ ̶γήΘγΩ ΖϴϠΑΎϗ ̵΍έ΍Ω

ΩϮη ̶ϣ ϞϣΎη ΍έ ϪόϣΎΟ ήμϨϋ Ϧϴϣ I ήϫ Ϫ̯ Ζγ΍ ̵΍ ϪϧϮϤϧ ̮ϴΗΎϤΘδϴγ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ˻ ΐγΎϨϣ ̶ϠϴΧ ήҨά̡ ΐϴΗήΗ ϊϣ΍ϮΟ ̵΍ήΑ Ζγ΍ ήϔλ ήϴϏ ϭ ϡϮϠόϣ Ϫ̯ ϪόϣΎΟ ί΍ ήμϨϋ ήϫ ϞϣΎη ̶ϨόҨ ί΍ ̵Ω΍ΪόΗ Ϫ̩ Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΨϴϣ ˱ϼΜϣ Ωέ΍Ω ΕϭΎϔΗ ˱ϼϣΎ̯ ̵ή̴ҨΩ ΎΑ ϪϧϮϤϧ ήϫ ̵ήϴ̳ ϪϧϮϤϧ ϦҨ΍ έΩϭ Ζγ΍ ̵Ύϫ Ϧ̳΍ϭ ϝϭ΍ ̵ήϴ̳ ϪϧϮϤϧ έΩ ΪϧϮη ̶ϣ Ώ΍ήΧ ̶ΘϓΎδϣ ί΍ ΪόΑ ΎΟέ ̵ήΑ ήϓΎδϣ έΎτϗ ̵Ύϫ Ϧ̳΍ϭ ̋ ˱ϼΜϣ ΩΪϋ ΕϭΎϔΗ ΎΑ ̵ή̴ҨΩ ̵Ύϫ Ϧ̳΍ϭ ϡϭΩ ̵ήϴ̳ ϪϣϮϤϧ έΩ ϭ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ ϭ ˺̋ϭ˺˹ϭ̋ N ϩΪη κΨθϣ ̵ήϴ̳ ϪϧϮϤϧ ϪϠλΎϓ k  ΪόΑ ϩΩή̯ ̵έ΍ά̳ ϩέΎϤη N ΎΗ ˺ ί΍ ΍έ ϪόϣΎΟ εϭέ ϦҨ΍ έΩ n Ϧϴϟϭ΍ ϪΑ k ϥΩϭΰϓ΍ ΎΑ ϭ ΖγΎϣ ϪϧϮϤϧ ΪΣ΍ϭ Ϧϴϟϭ΍ Ϫ̯ ϩΪη ΏΎΨΘϧ΍ k ΎΗ ˺ ί΍ ϑΩΎμΗ ϪΑ ΩΪϋ ̮Ҩ β̢γ ΪҨ΁ ̶ϣ ΖγΪΑ Ϣϫ ΎϫΪΣ΍ϭ ήҨΎγ ˬ ΪΣ΍ϭ ̮Ҩ ΏΎΨΘϧ΍ ϭ ΰҨΎϤΘϣ ̵Ύϫ ϪϘΒσ ϪΑ ϱέΎϣ΁ ϪόϣΎΟ ̮Ҩ ϢϴδϘΗ ϩΪη ̵ΪϨΑ ϪϘΒσ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ˼ ϪϘΒσ ήϫ ί΍ ̶ϓΩΎμΗ ϪϧϮϤϧ ΎҨ΍ΰϣ ϪϘΒσ ήϫ έΩ ̶ϓΎ̯ ήλΎϨϋ ΩϮΟϭ ί΍ ϥΎϨϴϤσ΍ ϪόϣΎΟ ̵ΎϫήΘϣ΍έΎ̡ ί΍ ήΘϬΑ ̵Ύϫ ϦϴϤΨΗ ϥΩέϭ΁ ΖγΪΑ ϪΑ ΍έ ΎϬϧΎδϧ΍ ή̳΍ ̶ϟϭ ΪҨ΁ ̶ϣ ΖγΪΑ ̵΍ ϪΠϴΘϧ ̮Ҩ ϢҨήϴ̴Α ΍έ ϪόϣΎΟ ̮Ҩ ̵ΎϬϧΎδϧ΍ Ϊϗ ςγϮΘϣ ή̳΍ ˱ϼΜϣ ϪΠϴΘϧ ϢϴϨ̯ ϪΒγΎΤϣ ϩΪη ̵ΪϨΑ ϪϘΒσ ̵ήϴ̳ ϪϧϮϤϧ έΩ ΍έ Ϊϗ ςγϮΘϣ ϭ ϢϴϨ̯ ϢϴδϘΗ Ωήϣ ϭ ϥί ϪϘΒσ ϭΩ ΪҨ΁ ̶ϣ ΖγΪΑ ̵ήΘϬΑ ϦҨ΍ ΪηΎΑ ϪϘΒσ ήϫ ϞΧ΍Ω έΩ ήλΎϨϋ ϑϼΘΧ΍ ί΍ ήΘθϴΑ ΕΎϘΒσ ϦϴΑ ήλΎϨϋ ϑϼΘΧ΍ Ϫ̯ ̶ΗέϮλ έΩ ̶Ϡ̯ Ϟλ΍ ΪϫΩ ̶ϣ ΍έ ̵ήΘϘϴϗΩ ΞҨΎΘϧ ϩΩΎγ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ εϭέ ϪΑ ΖΒδϧ εϭέ Ϟϴϣ ϝΎϣήϧ ΖϤγ ϪΑ ϥ΁ ϊҨίϮΗ ΩϮη ̱έΰΑ ϪϧϮϤϧ ϩί΍Ϊϧ΍ ή̳΍ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ήϫ έΩ ̵ΰ̯ήϣ ΪΣ Ϫϴπϗ Ωή̯ Ϊϫ΍ϮΧ ϥΩϮΑ Ϧ̴Ϥϫ ρήη Ϫ̯ Ζγ΍ ϪόϣΎΟ ήλΎϨϋ ί΍ ̶ҨΎϫ ϪηϮΧ ΏΎΨΘϧ΍ ̵΍ ϪηϮΧ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ̊ ΩϮη ΖҨΎϋέ ΪҨΎΑ ϥ΁ έΩ ΐϠϏ΍ έΩ Ύϫ ϪηϮΧ ϞϴϜθΗ ϱΎϨΒϣ ϭ ΩϮη ϲϣ ϩΪϴϣΎϧ ϪηϮΧ ˱ΎΣϼτλ΍ Ϫϛ ϲϳΎϫ ΖϤδϗ ϪΑ ΍έ ϪόϣΎΟ ˬΖγ΍ ϱΩΎΑ΁ Ύϳ ϙϮϠΑ ϭ ήϬη ˬϥΎΘδϫΩ ˬϥΎΘγήϬη ˬϥΎΘγ΍ ϞϴΒϗί΍ ϲϳΎϴϓ΍ήϐΟ ϱΎϫ ϱΪϨΑ ϢϴδϘΗ ˬΕΎϗϭ΍ ϥΎϴΑ ϪΑ. ΪϨηΎΑ ϲϧΎηϮ̡ Ϣϫ ΪϗΎϓ ϭ ΪϨϫΩ ζηϮ̡ ΍έ ϪόϣΎΟ Ϟϛ ΪϳΎΑ Ύϫ ϪηϮΧ. ΪϨϨϛ ϲϣ ϱΪϨΑ ϢϴδϘΗ ΪηΎΑ ϪΘη΍Ω ϖϠόΗ Ύϫ ϪηϮΧ Ϧϳ΍ ί΍ ϲϜϳ ςϘϓ ϭ ϲϜϳ ϪΑ ΪϳΎΑ ϪόϣΎΟ ήλΎϨϋ ί΍ ϡ΍Ϊϛ ήϫ ή̴ϳΩ ˬϱ΍ ϪϠΣήϣ ΪϨ̩ Ύϳ Ϛϳ ϱ΍ ϪηϮΧ ϱήϴ̳ ϪϧϮϤϧ ˬ ϩΪϴ̪ϴ̡ ϱήϴ̳ ϪϧϮϤϧ Ρήσ SPSS

ϱέ΍ΰϓ΍ ϡήϧ ϱ ϪΘδΑ .ΪϨϛ ϲϣ Ϣϫ΍ήϓ Complex Samples ΎΑ ΍έˬϱΪϨΑ ϪϘΒσ ϱήϴ̳ ϪϧϮϤϧ

ŵŹřŵ İĮŤƀŝ İƬƯřƺƗ ƶģ ƶŝ ƶĪƴƿř ƹ ŶǀƷŵ ŮǀƋƺţ řŹ ƶƳƺưƳ ƵŻřŶƳř ƲŤƟŚƿ įŚƷ ƁƹŹ ƩřƺŘſ

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

Crosstabs İƤƟřƺţ ƩƹřŶū

έΩ ΍έ ή˰Ҩί ̶Ϙϓ΍Ϯ˰Η ϝϭΪ˰Ο Ωϭέ ̶˰ϣ έΎ˰̰Α ̶Ϥ˰γ΍ ̵Ύ˰ϫ ή˰ϴϐΘϣ ϦϴΑ ρΎΒΗέ ΍ ϩΩ΍Ω ϥΎθϧ ̵΍ήΑ ̶Ϙϓ΍ϮΗ ϝϭ΍ΪΟ ΪϴϨ̯ Ωέ΍ϭ έ΍ΰϓ΍ ϡήϧ ϪΤϔλ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ Ϟϐη ϭ ΖϴδϨΟ ήϴϐΘϣ ϭΩ

ƱŻ ŵźƯ ϢϴϨ̯ ̶ϣ ϒҨήόΗ ϥΩ΍Ω ϥίϭ ̵΍ήΑ ΰϴϧ ΍έ Ω΍ΪόΗ ήϴϐΘϣ Ϫ̯ ΏΎΨΘϧ΍ έΎΒ̰Ҩ ςϘϓ ΍έ ΖϟΎΣ ήϫ Ύϫ ϩΩ΍Ω Ωϭέϭ ϪΤϔλ έΩ ΪόΑ ˺̋ ̋ ˻˹ ŹŚĪǀŝ Male – jobless ϢϴϨ̯ ̶ϣ ˺˹ ˻˹ ˼˹ ƪƛŚƃ Male – employee

˻̋ ˻̋

Female –jobless Female – employee Name

Type

Width

Decimal

Label

8

0

-

0

-

0

˺

Gender

Numeric

˻

job

Numeric

3

frequency

Numeric

8

Value

Missing column Align

1=male 2=female 1=jobless 2=emploee

Measure

-

10

left

Nominal

-

10

left

Nominal

-

10

left

Scale

ΖϬΟ ϢϴϫΩ ̶ϣ ϥίϭ Ύϫ ϩΩ΍Ω ϪΑ Data – Weightcases – frequency ̵ϮϨϣ ί΍ ϩΩΎϔΘγ΍ ΎΑ β̢γ ϮϨϣ ί΍ ̶Ϙϓ΍ϮΗ ϝϭΪΟ Ϟϴ̰θΗ Analyze –Descriptive Statistics- Crosstabs έΩ ήҨί ϝϭΪΟ Ϫ̯ ϢҨέ΍ά̴Α ̶Ϥγ΍ ΎҨ Nominal ήϴϐΘϣ ϭΩ ΪҨΎΑ ϩΪη ίΎΑ ϩήΠϨ̡ ϥϮΘγ ϭ ήτγ ΖϤδϗ έΩ ΩϮη ̶ϣ ήϫΎχ output job * gender Crosstabulation Count Total

gender male job

jobless emploee

Total

5

female 15

male

20

10

30

25

25

50

20

ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍ Ϫϴοήϓ Ϫ̯ ϭΩ ̶Χ ϡΎϧ ϪΑ ̶ϧϮϣί΁ ί΍ ϥ΍ϮΘϴϣ crosstabs ϩήΠϨ̡ έΩ statistics ϪϤ̳Ω ϥΩί ΎΑ ΩϮϤϧ ϩΩΎϔΘγ΍ ˬΪϨ̰ϴϣ ϥϮϣί΁ ΍έ ̶Ϥγ΍ Chi-Square Tests

Pearson Chi-Square Continuity Correction(a) Likelihood Ratio

1

Asymp. Sig. (2-sided) .004

6.750

1

.009

8.630

1

.003

Value 8.333(b)

df

Fisher's Exact Test

Exact Sig. (2-sided)

Exact Sig. (1-sided)

.009

Linear-by-Linear Association

8.167

N of Valid Cases

50

1

.004

.004

a Computed only for a 2x2 table b 0 cells (.0%) have expected count less than 5. The minimum expected count is 10.00.

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

ϥΎθϧ ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍ ̵΍ήΑ ΍έ ̵έ΍Ω ̶Ϩόϣ ΢τγ Ϫ̯ ϢϴΘδϫ Asymp. Sig. (2-sided) ϥϮΘγ ϝΎΒϧΩ Ύϣ ΪηΎΑ ̶ϣ ήϴϐΘϣ ϭΩ ̶̴ΘδΑ΍ϭ ̶Ϩόϣ ϪΑ Ϫ̯ Ζγ΍ Ϣ̯ έΎϴδΑ ΎΠϨҨ΍ έΩ ΪϫΩ ̶ϣ ΩϮη ̶ϣ ΏΎδΣ ϝϮϣήϓ ϦҨ΍ ςγϮΗ ΪηΎΑ ̶ϣ ̶Ϥγ΍ ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍ ϥϮϣί΁ Ϫ̯ ϭΩ ̶Χ ϥϮϣί΁ Oi ϩΪη ϩΪϫΎθϣ Ei έΎψΘϧ΍ ΩέϮϣ 

(Oi  Ei )2   2  Ϊϧ΍ ϪΘδΑ΍ϭ Ei



(Oi  Ei )2   2  Ϊϧ΍ ϞϘΘδϣ Ei

̶ΟϭήΧ έΩ ϥϮΘγ ϭ ήτγ ήϴϴϐΗ ϩΩή̯ ̮ϴϠ̯ ϥ΁ ̵ϭέ έΎΑ ϭΩ Ζγ΍ ̶ϓΎ̯ ϢϴϨ̯ νϮϋ ΍έ ϥϮΘγ ϭ ήτγ ̵ΎΟ Ϣϴϫ΍ϮΨΑ ή̳΍ ̶ΟϭήΧ ϝϭΪΟ έΩ Ωή̯ νϮϋ ΍έ ϥϮΘγ ϭ ήτγ ̵ΎΟ pivoting tray ϩήΠϨ̡ ϭ formatting toolbar έ΍ΰΑ΍ έ΍Ϯϧ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϭ Ϊϴθ̯ ΰϴϧ split files ί΍ ϩΩΎϔΘγ΍ ϭ ̶ϠΒϗ ̵Ύϫ εϭέ ΎΑ ϥ΍ϮΘϴϣ ΍έ ϝϭΪΟ ϦϴϤϫ

Layer ήϴϐΘϣ ϪҨϻ ̮Ҩ ΩΎΠҨ΍ ΩϮϤϧ ϩΩΎϔΘγ΍ ̶Ϙϓ΍ϮΗ ϝϭ΍ΪΟ έΩ ̶ϣϮγ ήϴϐΘϣ ί΍ ϥ΍ϮΘϴϣ Ϫϫ΍έ Ϫγ ϝϭΪΟ ̮Ҩ ΎΗ ϢϴϨ̯ ϪϓΎο΍ ΍έ ήϴϐΘϣ ϪҨϻ ̮Ҩ ̶ϨόҨ ΍έ ϡϮγ ήϴϐΘϣ Ε΍ήϴΛΎΗ ̶Θϗϭ Ϫ̯ ΪϫΩ ̶ϣ ϥΎθϧ ϭ ϢҨίΎδΑ ϥϮΘγ ϭ ήτγ ̵ΎϫήϴϐΘϣ ϦϴΑ ϪτΑ΍έ ϪϧϮ̴̩ ϢϴϨ̯ ̶ϣ ϝήΘϨ̯ ΪΑΎҨ ̶ϣ ήϴϴϐΗ

ϭ empcat ̶Ϡϐη ϖΑ΍Ϯγ ϥ΍ΰϴϣ ϭ ΪϴϨ̯ ίΎΑ ΍έ Demo ϞҨΎϓ ϪΑ ΍έ genderΖϴδϨΟ ήϴϐΘϣ ϭ ΪҨέϭ΁ ΖγΪΑ ΍έinccat Ϊϣ΁έΩ ΪϴϨ̯ Ωέ΍ϭ ϡϮγ ήϴϐΘϣ ϥ΍ϮϨϋ ΍έ ήτγ ΪλέΩ ϭ ϩΩί ΍έ cell ϪϤ̳Ω crosstabs ϩήΠϨ̡ έΩ ή̳΍ ΪϫΩ ̶ϣ ϥΎθϧ ΰϴϧ ΪλέΩ ΎΑ ΍έ Ω΍Ϊϋ΍ ϢϴϨ̯ ϝΎόϓ

Years with current employer * Income category in thousands * Gender Crosstabulation Count Total

Income category in thousands Gender Female

Years with current employer

Less than 5 5 to 15 More than 15

Years with current employer

$50 - $74 130

148

553

$75+ 61

Under $25 1117

277

211

1189

46

98

165

564

873

1208

572

836

3179

Less than 5

409

516

107

67

1099

5 to 15

131

562

274

208

1175

71

102

167

607

947

611

1180

548

882

3221

More than 15 Total

$25 - $49 557

563

Total Male

Under $25 369

Ζγ΍ έϻΩ ̀̋˹˹˹ ̵ϻΎΑ Ϊϣ΁ έΩ ϭ ϝΎγ ˺̋ ί΍ ήΘθϴΑ ̶Ϡϐη ϪϘΑΎγ ϪΑ ρϮΑήϣ Ω΍ΪόΗ ϦҨήΗϻΎΑ ΎϫΩήϣ ϭ ΎϬϧί ϩϭή̳ έΩ ϪΠϴΘϧ Ζγ΍ έϻΩ ̀̋˹˹˹ ̵ϻΎΑ Ϊϣ΁ έΩ ϭ ϝΎγ ̋ ί΍ ήΘϤ̯ ̶Ϡϐη ϪϘΑΎγ ϪΑ ρϮΑήϣ Ω΍ΪόΗ ϦҨήΗ ϦϴҨΎ̡ ΎϫΩήϣ ϭ ΎϬϧί ϩϭή̳ έΩ

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̶ϨόҨ Ζγ΍ .000 ˬ Asymp. Sig. (2-sided) ϥϮΘγ ΩΪϋ Ϫ̯ ϢҨϮη ̶ϣ ϪΟϮΘϣ ϭΩ ̶Χ ϥϮϣί΁ ΎΑ Ϊϧ΍ ϪΘδΑ΍ϭ ϭΪϨҨ΍ ϭ Ωέ΍Ϊϧ ΩϮΟϭ Ϊϣ΁έΩ ϭ ̶Ϡϐη ϖΑ΍Ϯγ ήϴϐΘϣ ϭΩ ϦϴΑ ̶ϟϼϘΘγ΍ Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Value 2506.010(a)

df

2527.590 2012.691

Asymp. Sig. (2-sided) 6

.000

6

.000

1

.000

6400

̶ϟϭ ΪϫΩ ϥΎθϧ ΍έ ΕϼϴμΤΗ ϥ΍ΰϴϣ ϭ ΖϴδϨΟ ϦϴΑ ρΎΒΗέ΍ Ϫ̯ ΪϴϨ̯ Ϣγέ ̶ϟϭΪΟ Demo ϞҨΎϓ έΩ ϦҨήϤΗ έΩ ΎϬϧ΁ Ϊϣ΁έΩ ςγϮΘϣ ϥ΍ΰϴϣ Ω΍ΪόΗ ̵ΎΟ ϪΑ ΪϨ̯ ΝέΩ ϝϭΪΟ ̶Ϙϓ΍ϮΗ ϝϭΪΟ ί΍ ϥ΍ϮΗ ̶Ϥϧ ΎΠϨҨ΍ έΩ ̶ηέΎϔγ ϝϭΪΟ ΪҨΎΑ Ϫ̰ϠΑ ΩϮϤϧ ϩΩΎϔΘγ΍ Ωή̯ ΩΎΠҨ΍ Analyze – Tables – Custom Table ήτγ έΩ ϭ ΖϴδϨΟ gender ϥϮΘγ έΩ Ϊϣ΁έΩ ήϴϐΘϣ ϭ edu ΎҨ ΕϼϴμΤΗ ΢τγ έΩ ΪηΎΑ scale ΪҨΎΑ ˱ΎϤΘΣ Ϫ̯ income ϢҨέ΍ά̳ ̶ϣ count ΖϤδϗ

ϥϮΘγ ϭ ήτγ ήϴϐΘϣ ϢϬϣ έΎϴδΑ ϪΘ̰ϧ ή̳΍ ΪηΎΑ ϩΪη ̵ΪϨΑ ϩΩέ ήϴϐΘϣ ΪҨΎΑ ˱ΎϤΘΣ nominal ϪΑ ΍ήϧ΁ Ζγ΍έ ̮ϴϠ̯ ΎΑ ΩϮΒϧ ϪΒγΎΤϣ ΖϬΟ ήϴϐΘϣ ϭ ϢϴϨ̯ ̶ϣ ϞҨΪΒΗ ΪηΎΑ scale ΪҨΎΑ ˱ΎϤΘΣ

Gender

Level of education

Did not complete high school High school degree

Female Household income in thousands

Male Household income in thousands

Mean 59.20

Mean 60.49

65.12

67.27

Some college

71.63

68.60

College degree

76.27

81.02

84.81

89.62

Post-undergraduate degree

έΩ summary statistics ϪϤ̳Ω ί΍ ϩΩΎϔΘγ΍ ΎΑ ΍έ ̵ή̴ҨΩ ̵έΎϣ΁ ̵Ύϫ κΧΎη Ϧϴ̴ϧΎϴϣ ̵ΎΟ ϪΑ ϥ΍ϮΗ ̶ϣ ΩϮϤϧ ϪΒγΎΤϣ ϩήΠϨ̡ ϥΎϤϫ 18 www.ircdvd.com

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a 0 cells (.0%) have expected count less than 5. The minimum expected count is 318.50.

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carcat ΎҨ ϦϴηΎϣ ω΍Ϯϧ΍ ϭ gender ΖϴδϨΟ ϦϴΑ ρΎΒΗέ΍ Ϫ̯ ΪϴϨ̯ Ϣγέ ̶ϟϭΪΟ Demo ϞҨΎϓ έΩ ϦҨήϤΗ ϝϭΪΟ έΩ ΎϬϧ΁ Ϊϣ΁έΩ ςγϮΘϣ ϥ΍ΰϴϣ Ω΍ΪόΗ ̵ΎΟ ϪΑ ̶ϟϭ ΪϫΩ ϥΎθϧ ΍έ Primary vehicle price category ΪϨ̯ ΝέΩ Analyze – Tables – Custom Table scale ΪҨΎΑ ˱ΎϤΘΣ Ϫ̯ income Ϊϣ΁έΩ ήϴϐΘϣ ϭ carcat ΎҨ ϦϴηΎϣ ω΍Ϯϧ΍ ήτγ έΩ ϭ ΖϴδϨΟ gender ϥϮΘγ έΩ ˬϦϴ̴ϧΎϴϣ ̵Ύϫ κΧΎη summary statistics ϪϤ̳Ω ϥΩί ΎΑ ϭ ϢҨέ΍ά̳ ̶ϣ count ΖϤδϗ έΩ ΪηΎΑ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ Ϣϫ ΍έ Ϊϣ ϭ ϢϤϴϨϴϣ ˬϢϤҨΰ̯Ύϣ

Gender

Primary vehicle price category

Economy

Maximum 31.00

Minimum 9.00

Mode 25.00

42.45

61.00

29.00

34.00

132.60

1070.00

59.00

65.00

Mean 21.62

Maximum 31.00

Minimum 9.00

Mode 23.00

42.67

61.00

29.00

31.00

136.65

1116.00

58.00

62.00

ƱŻ

ƎſƺŤƯ ƎſƺŤƯ

ŵźƯ

ŶƯōŹŵ

Mean Household income in thousands

80.00

70.16

68.78 60.00

40.00

20.00

ƶƴƿżƷ

Nominal ϒϠΘΨϣ ̵ΎϬϫϭή̳ ϦϴΑ έΩ scale ήϴϐΘϣ ̮Ҩ ϪδҨΎϘϣ ˺ Ϣϫ ΎΑ ήΘθϴΑ ΎҨ scale ήϴϐΘϣ ϭΩ ϪҨΎϘϣ ˻ Ύϫ case ϡΎϤΗ ϦϴΑ έΩ ήϴϐΘϣ ̮Ҩ ϪδҨΎϘϣ ˼

ΪϴϨ̯ ΖγέΩ ΍έ Ύϫέ΍ΩϮϤϧ ϦҨ΍ ϦҨήϤΗ

ϝϭ΍ ωϮϧ έ΍ΩϮϤϧ ΖΧΎγ Graph Chart builder ˺ Ύϫ x έϮΤϣ έΩ Gender ήϴϐΘϣ ˻ Ύϫ y έϮΤϣ έΩ income ήϴϐΘϣ ˼ Clustering variable on ϪϨҨΰ̳ Groups/point id ϪϤ̳Ω έΩ ̊ ΪϴϨ̯ ΝέΎΧ ϥΩϮΑ ϝΎόϓ ί΍ ΍έ X ΎҨ Graph – Legacy dialogs – bar – variable ….. mean Household income in thousands

/ category Axis ……. gender

0.00 Female

Male

Gender

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ϦҨήΗϻΎΑ ΎΑ ϥΎҨΎϗ΁ ̶ϟϭ ΪϧήΧ ̶ϣ ΍έ β̯Ϯϟ ϦϴηΎϣ ϦҨήΘϬΑ έϻΩ έ΍ΰϫ ˺˹̀˹ Ϊϣ΁έΩ ϦҨήΗϻΎΑ ΎΑ ΎϬϤϧΎΧ ϪΠϴΘϧ ΪϧήΧ ̶ϣ β̯Ϯϟ ϦϴηΎϣέϻΩ έ΍ΰϫ ˺˺˺̌ Ϊϣ΁έΩ Ζγ΍ ϥΎҨΎϗ΁ ί΍ ήΘϤ̯ ΪϧήΧ ̶ϣ β̯Ϯϟ ϦϴηΎϣ Ϫ̯ ̶ҨΎϫ ϢϧΎΧ Ϊϣ΁έΩ Ϧϴ̴ϧΎϴϣ

ŹřŵƺưƳ ƕřƺƳř ŹLJŵ

ŶƯōŹŵ ƎſƺŤƯ

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Male Household income in thousands

Mean 22.17

Standard Luxury

Female Household income in thousands

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ϡϭΩ ωϮϧ ϝϭΪΟ ΖΧΎγ

Data – split files - gender Analyze – Descriptive statistics – Descriptive -

Household income in thousands , Price of primary vehicle

Gender

Female Male

Household income in thousands Mean 68.78

Price of primary vehicle Mean 29.95

70.16

30.31

ϡϭΩ έ΍ΩϮϤϧ ΖΧΎγ Graph – Legacy dialogs – bar – simple – summaries of seprate variables - variable ….. mean

69.47 60.00

Mean

40.00

30.13 20.00

0.00 Household income in thousands

Price of primary vehicle

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íë æç æë ± ¹ ZÆq Ä ¸m ϞΒϗ ϪδϠΟ Εϻ΍ϮΌγ ϪΑ ΦγΎ̡ ̮Ҩ έΩ ΍έ ΪϨΘδϫ ̵΍ ϪϨҨΰ̳ ΪϨ̩ ϭ Ϊϧέ΍Ω ϥΎδ̰Ҩ ΦγΎ̡ Ϫ̯ ̶ҨΎϫήϴϐΘϣ Demo ϞҨΎϓ έΩ ϥ΍ϮΘϴϣ ϪϧϮ̴̩ ˺ ˮΩή̯ ̵έϭ΁ ϊϤΟ ϝϭΪΟ Analyze – Tables – Custom Tables Ζγ΍έ ̮ϴϠ̯ ΎΑ ΍ΪΘΑ΍ ΍έ Ϊϧέ΍Ω ήϴΧ ΎҨ ϪϠΑ Ώ΍ϮΟ ςϘϓ Ϫ̯ ϥΎδ̰Ҩ ΦγΎ̡ ΎΑ ̵ΎϫήϴϐΘϣ ϪϴϠ̯ β̢γ ϝϭ΍ εϭέ ΖϤγ ί΍ β̢γ stacking ΖϟΎΣ ϪΑ ϩΩ΍Ωέ΍ήϗ ϝϭΪΟ ήτγ ΖϤδϗ έΩϭ ϩΩή̯ ϞҨΪΒΗ category ωϮϧ ϪΑ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ row labels in columns ϪϨҨΰ̳ category position ΖϤδϗ ϩήΠϨ̡ ϦϴҨΎ̡ Ζγ΍έ

No

Count 2547

Multiple lines

3709

2691

Voice mail

3645

2755

Paging service

4819

1581

Internet

4509

1636

Wireless service

ϡϭΩ εϭέ Analyze – Tables –Tables of frequency ΪϴϧΰΑ΍έ ok ϭ ΪҨήΒΑ ϩήΠϨ̡ έΩ ΍έΎϫήϴϐΘϣ ϪϤϫ

z

xx x

xx d

nd  z.

ˮϢϴϨ̯ ϪΒγΎΤϣ ϪϧϮ̴̩ ΍έ ϪϧϮϤϧ ϩί΍Ϊϧ΍ ˻ ̶Ϥ̯ ήϴϐΘϣ ̵΍ήΑ ± ϒϟ΍ ΩϭΪΤϣΎϧ ϪόϣΎΟ έΩ 

nd 2  z 2 2 z 2 2 n 2 d

 x 

n

d nd  z

 

Ζγ΍ κΨθϣ ϪόϣΎΟήλΎϨϋ Ω΍ΪόΗ N Ϫ̯ ΩϭΪΤϣ ϪόϣΎΟ έΩ  n

2 2

Nz  n

( N  1)d 2  z 2 2 z  1.64 ˬ   0.05 ̵΍ήΑ ̶ϔϴ̯ ήϴϐΘϣ Ώ ϥ΁ ϦΘη΍Ϊϧ ϝΎϤΘΣ΍ q=1-p ϭ ήψϧ ΩέϮϣ Ζϔλ ϦΘη΍Ω ϝΎϤΘΣ΍ p Ϫ̯ ϢϴϫΩ ̶ϣ έ΍ήϗ ΍έ pq ˬ ̵ΎΟ ϪΑ Ζγ΍ Ζϔλ

Nz 2 pq n

( N  1) d 2  z 2 pq

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Yes

Count 3853

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Ʊřźƿř żƿř ƺŤǀŤƀƳř ƉźƟ ƱƺƯŻō

κΧΎη ϥ΍ϮϨϋ ϪΑ ϪϧϮϤϧ κΧΎη έ΍ΪϘϣ ΏΎΨΘϧ΍ ϭ ϪϧϮϤϧ ΎΑ ΖϴόϤΟ κΧΎη ϪδϳΎϘϣ ϱ΍ήΑ νήϓ ϥϮϣί΁ Ωϭέϲϣ έΎϛ ϪΑ ΖϴόϤΟ ϢϳϮη ϞϤΤΘϣ Ϣϴϧ΍ϮΗϲϣ Ϫϛ Ζγ΍ ϱΎτΧ ϥ΍ΰϴϣ ϭ ϪϧϮϤϧ ϭ ΖϴόϤΟ ϩί΍Ϊϧ΍ ϪΑ ϪΟϮΗ ΎΑ ΏΎΨΘϧ΍ Ϧϳ΍ ΖϗΩ ϊϳίϮΗ ϪΑ ΖϴόϤΟ Ϫ̩ήϫ ΩϮΑ Ϊϫ΍ϮΧ ήΘθϴΑ ϥϮϣί΁ ΖϗΩ ΪηΎΑ ήΘθϴΑ ΖϴόϤΟ ϪΑ ΖΒδϧ ϪϧϮϤϧ ϩί΍Ϊϧ΍ Ϫ̩ήϫ ΩϮΑ ΪϨϫ΍ϮΧ ήΗϥ΍ϮΗή̡

ΎϫϥϮϣί΁

ΪηΎΑ ήΘϜϳΩΰϧ ϝΎϣήϧ

ŚƷ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ įřźŝ ƱƺƯŻō śŚŴŤƳř Á

ÃÁ ³ ®Ë

{Y| e

Á{ Y Ì]

ÃÁ ³

Ã{Z ¿ZË YÁ Ì·Z¿M oneway Anova Ì·YÁ µZ° Á ¯ ¾»|Ë § µY|À¯ Á ½Y ¯Â¯

¶¬f » ÉZÅĿ¼¿

t½Â» M Independent-Sample t test ÉYĿ¼¿Á{

Á É f»Y ZaZ¿ ÊÀfËÁ ¾»

¶¬f »

¶¬f »

Äf ]YÁ

Äf ]YÁ

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Ŀ¼¿ ®Ë t ½Â» M one sample T test

Ã{Z ¿ZË YÁ Ì·Z¿M oneway Anova Ì·YÁ µZ° Á ¯ ¾»|Ë § µY|À¯ Á ½Y ¯Â¯

ƲǀĮƳŚǀƯ ƶŝ ƍƺŝźƯ įŚƷ ƱƺƯŻō

ϪόϣΎΟ ϪΑ ΍έ ϪϧϮϤϧ ΖϴλϮμΧ Ϣϴϫ΍ϮΧ ̶ϣ ϭ ϢҨ΍ ϩΩή̯ ΏΎΨΘϧ΍ ϪϧϮϤϧ ̮Ҩ ϭ ϢҨέ΍Ϊϧ ΍έ ϪόϣΎΟ Ϫ̯ ̶ϧΎϣί ϦϴΑ ϑϼΘΧ΍ ϥ΍ϮΘϴϣ ΕΎϴοήϓ ϥϮϣί΁ ΖϴϠΑΎϗ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϭ ϢϴϫΩ ̶ϣ ϡΎΠϧ΍ ϥϮϣί΁ ΍άϟ ϢϴϫΪΑ ΖΒδϧ ΐ̰Ηήϣ ϢϴϨ̯ Ωέ ΍έ ϥ΁ Ύϣ ϭ ΪηΎΑ ΖγέΩ ̵έΎϣ΁ νήϓ ή̳΍ ˬ νήϓ ϥϮϣί΁ ϡΎΠϧ΍ έΩ ΪϴΠϨγ ΍έ Ύϫ Ϧϴ̴ϧΎϴϣ ϢҨϮη ̶ϣ ϡϭΩ ωϮϧ ̵ΎτΧ ΐ̰Ηήϣ ΪηΎΑ ΖγέΩΎϧ ϢϴΘϓήҨά̡ Ϫ̯ ΍έ ̵έΎϣ΁ νήϓ ή̳΍ ϭ ϝϭ΍ ωϮϧ ̵ΎτΧ ̵ΎτΧ Ζγ΍ ήΘϤ̯ ϡϭΩ ωϮϧ ̵ΎτΧ ϭ ΩϮΑ ΪϨϫ΍ϮΧ ήΗ ϥ΍ϮΗή̡ Ύϫ ϥϮϣί΁ ΩϭήΑ ζϴ̡ ϝΎϣήϧ ϑήσ ϪΑ ϊҨίϮΗ Ϫ̩ήϫ ΪηΎΑ ΖγέΩ Ϫ̰ϨҨ΍ ρήη ϪΑ H1 Ωέ ϝΎϤΘΣ΍ ̶ϨόҨ ϡϭΩ ωϮϧ  2 ϡϮϠόϣ βϧΎҨέ΍ϭ ϭ  ϝϮϬΠϣ Ϧϴ̴ϧΎϴϣ ΎΑ ϝΎϣήϧ ϊҨίϮΗ ί΍ ̶ҨΎΗ n ϪϧϮϤϧ ̮Ҩ X 1 , X 2 ,..., X n ϢϴϨ̯ νήϓ Ζγ΍ ϪόϣΎΟ Ϧϴ̴ϧΎϴϣ ΩέϮϣ έΩ ήҨί νήϓ ϥϮϣί΁ ϑΪϫ ΪϨηΎΑ Ωέ΍Ωέ΍ήϗ Ζγ΍ Ϫϓήσ ̮Ҩ ΐ̯ήϣ νήϓ ̮Ҩ Ϫ̯ H1 νήϓ ϞΑΎϘϣ έΩ ϩΩΎγ νήϓ ̮Ҩ H 0 νήϓ

H0:  0 H 1:   0

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έϮσ ϪΑ ϭ ΪϨ̯ ̶Ϥϧ ΕϭΎϔΗ ή̴ҨΩ ϪϧϮϤϧ ϪΑ ϪϧϮϤϧ ̮Ҩ ί΍ ϥϮϣί΁ ϪΠϴΘϧ ϭ Ζγ΍ ήΘθϴΑ ϥ΍ϮΗ ϝΎϣήϧ ϊҨίϮΗ έΩ ΐϴΗήΗ ϪΑ Ϫ̯ ΩϮη ̶ϣ ϩΩΎϔΘγ΍ ̵ήΘϣ΍έΎ̡ ̵Ύϫ ϥϮϣί΁ ί΍ ϥΩϮΑ ϝΎϣήϧ νήϓ ΎΑ ̱έΰΑ ϊϣ΍ϮΟ έΩ ̶Ϡ̯ Ϊϧέ΍Ω Ϫϴ̰Η ΕΎϋϼσ΍ ϦΘϓή̳έ΍ήϗ ̵ΎϬϧϮϣί΁ ΪϨϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ̵ήΘϣ΍έΎ̡Ύϧ ̵Ύϫ ϥϮϣί΁ ί΍ ̮̩Ϯ̯ ϭ ϝΎϣήϧ ήϴϏ ϊҨίϮΗ ΎΑ ̵Ύϫ ΖϴόϤΟ έΩ ΐΟϮϣ ϥ΍ΰϴϣ ϥΎϤϫ ϪΑ Ϫ̯ ή̴ҨΩ Ωή̰Ҩϭέ ΪϨϨ̯ ̶Ϥϧ ̶λΎΧ νήϓ ϥ΁ βϧΎҨέ΍ϭ ϭ ϪόϣΎΟ ̵ΎϬόҨίϮΗ ϩέΎΑ έΩ ̵ήΘϣ΍έΎ̡Ύϧ Ζγ΍ ΪҨΪΟ ̵Ύϫ ϩΩ΍Ω ϪϋϮϤΠϣ ΎΑ t ϥϮϣί΁ ϥΩήΑ έΎ̯ ϪΑ ϭ Εή̡ ήҨΩΎϘϣ ϥΩή̯ ΝέΎΧ ΩϮη ̶Ϥϧ ϥ΍ϮΗ Ζϓ΍

ΪηΎΒϧ ΢ϴΤλ Ϫ̯ ̶σήη ϪΑ Ζγ΍ H0 ϥΩή̯ Ωέ ϝΎϤΘΣ΍ ̵έΎϣ΁ ϥϮϣί΁ ̮Ҩ power ΎҨ ϥ΍ϮΗ

p  value   ή̳΍ Ζγ΍ ϝΎϤΘΣ΍ έ΍ΪϘϣ ΎҨ p  value ί΍ ϩΩΎϔΘγ΍ ̵έΎϣ΁ νήϓ ϥϮϣί΁ ̵ΎϬηϭέ ί΍ ̶̰Ҩ ΩϮη ̶ϣ Ωέ H 0 νήϓ έ΍ΪϘϣ

Mean Household income in thousands

ΖϴόϤΟ ί΍ ϩϭή̳ ϭΩ ϦϴΑ ϲΒγΎϨϣ ϪδϳΎϘϣ ϥ΍ϮΗϲϣ ΖϴόϤΟ ΰϛήϤΗ κΧΎη ϥ΍ϮϨϋ ϪΑ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ΎΑ Ωέϭ΁ ΖγΪΑ ϪϧϮϤϧ

ΖϴόϤΟ ϩΪϨϳΎϤϧ ϭ κΧΎη ϥ΍ϮϨϋ ϪΑ Ϊϣ ϭ ϪϧΎϴϣ ϞΜϣ ΰϛήϤΗ ϱΎϫέΎϴόϣ ή̴ϳΩ Ύϳ Ϧϴ̴ϧΎϴϣ Ϫϛ ϲϳΎΠϧ΁ ί΍ ϪδϳΎϘϣ ΍έ ΎϬϧ΁ ΰϛήϤΗ ϱΎϫέΎϴόϣ Ζγ΍ ήΘϬΑ ΖϴόϤΟ ϭΩ Ύϳ ϲ̳̬ϳϭ ϭΩ ϪδϳΎϘϣ ϱ΍ήΑ ˬΪϨΘδϫ ϩΪη ϪΘΧΎϨη Ωήϛ Analyze – Compare Means – Means Dependent list ΖϤδϗ έΩ Ωέ΍ϭ ΍έ ϩΪη ϱήϴ̳ ϩί΍Ϊϧ΍ ϲ̳̬ ϳϭ Ύϳ ήϴϐΘϣ ϱΩΪϋ ήϳΩΎϘϣ ϱ΍έ΍Ω ΪϳΎΑ ήϴϐΘϣ Ϧϳ΍ ΪϴϨϛ ΪηΎΑ Independent list ΖϤδϗ έΩ ϭ ϲϓήόϣ ΍έ ϱΪϨΑϩϭ ή̳ Ύϳ ϱΪϨΑ ϪΘγΩ ήϴϐΘϣ αΎγ΍ήΑ ϲ̳̬ϳϭ Ϧϴ̴ϧΎϴϣ έΎϛ Ϧϳ΍ ΎΑ ΪϴϨϛ ΖγΪΑ ϩϭή̳ ήϫ ϱ΍ήΑ ϱΪϨΑ ϪΘγΩ ήϴϐΘϣ ΪϧϮηϲϣ ϪδϳΎϘϣ ϭ ϩΪϣ΁

ϞϫΎΗ Ζϴόοϭ αΎγ΍ήΑ Ϊϣ΁έΩ ϪδҨΎϘϣ ˱ϼΜϣ xx ΪηΎΑ ̶ϣ T  Ϧϴ̴ϧΎϴϣ ϩέΎϣ΁ s n ̵ΎϬϴ̳̬ϳϭ Á Statistics ϩέΎϣ΁ ΍έ ϩήϴϏ ϭ έΎϴόϣ ϑ΍ήΤϧ΍ ˬϦϴ̴ϧΎϴϣ ϪϧϮϤϧ ̮ϳ ̵ΎϬϴ̳̬ϳϭ ϪΑ ρϮΑήϣ ήϳΩΎϘϣ ΪϨҨϮ̳ ̶ϣ Parameters ήΘϣ΍έΎ̡ ΍έ ̶Ϡλ΍ ϪόϣΎΟ έΩ ΎϬϧ΁ ϝΩΎόϣ Ϣϴθ̯ ̶ϣ ϞϫΎΗ Ζϴόοϭ αΎγ΍ ήΑ ΰϴϧ ΍έ income ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ έ΍ΩϮϤϧ

69.272 69.68

Report Household income in thousands Marital status Mean

N

Std. Deviation

Unmarried

69.2723

3224

78.32925

Married

69.6804

3176

79.12361

Total

69.4748

6400

78.71856

60.00

40.00

20.00

0.00 Unmarried

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Married

Marital status

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Means ƵŶƃ įŶƴŝ ƵŵŹ źǀƜŤƯ Ĩƿ Źŵ İĭĦƿƹ Ŷƴģ Śƿ ƹŵ įřźŝ įŵŶƗ źǀƜŤƯ Ĩƿ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ įźŤƯřŹŚě ƱƺƯŻō

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ΎϫήϴϐΘϣ ̵ΪϨΑ ϪҨϻ Ωή̯ ̵ΪϨΑ ϪҨϻ ϥ΍ϮΘϴϣ ΖϴδϨΟ ϩ΍ήϤϫ ϪΑ ΍έ ΖϴόϗϮϣ Mean ϞϤόϟ΍έϮΘγΩ ί΍ ϩΩΎϔΘγ΍ ΎΑ

Analyze – Compare Means – Means- dependent list ………income

Report Household income in thousands Marital status Unmarried

Married

Total

Gender Female

Mean 72.0633

N 1533

Std. Deviation 83.02021

Male

66.7422

1691

73.75302

Total

69.2723

3224

78.32925

Female

65.7179

1645

68.15853

Male

73.9392

1530

89.27551

Total

69.6797

3175

79.13606

Female

68.7788

3178

75.74700

Male

70.1608

3221

81.56216

Total

69.4744

6399

78.72471

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Independent list………….Martial Next Independent list………….Genderl

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̶ϣ box ̮Ҩ ήϴϐΘϣ ήϫ ̵΍ήΑ έ΍ΩϮϤϧ ωϮϧ ϦҨ΍ έΩ Ζγ΍ Boxplot έ΍ΩϮϤϧ ϦҨήΘϬΑ Ύϫ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ̵΍ήΑ Ζγ΍ ήΘθϴΑ Ύϫ ϩΩ΍Ω ̶̳ΪϨ̯΍ή̡ ΪηΎΑ ήΘθϴΑ box ωΎϔΗέ΍ Ϫ̩ ήϫ Ϫ̯ Ϊθ̯ 1

Max ήΗϻΎΑ ή̳΍ ϭ Ζγ΍ ϥέΎϘΘϣ ΪηΎΑ box ςγϭ Ϧϴ̴ϧΎϴϣ ή̳΍ Ζγ΍ ̶̴ϟϮ̩ ϪϧΎθϧ ΪηΎΑ ςγϭ ί΍ήΗ ϦϴҨΎ̡ ΎҨ Q3 ϥ΁ ˺ ϩέΎϤη Ϫ̯ Ζγ΍ Εή̡ ϩΩ΍Ω ̮Ҩ ϩΪϨϫΩ ϥΎθϧ Ζγ΍ ˺ ϩέΎϤη case ϪΑ ρϮΑήϣ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ΪϨҨϮ̳ ̶ϣ outliar Εή̡ ϩΩ΍Ω ϪΑ ΪηΎΑ ϪΘη΍Ω ΍έ ρήη ϦҨ΍ Ϫ̯ ̵΍ ϩΩ΍Ω ̶Ϡ̯ έϮσ ϪΑ Mean=median= ΩϮη ̶ϣ ΏΎδΣ Εή̡ Q2

Q  Q3  Q1 Q1

1 Q  Q3  Outliar  Q1  1 Q

2 2 Min

ΩϮΟϭ ΎΑ Ϊϧέ΍Ω ϪϠλΎϓ Q ήΑ΍ήΑ Ϫγ ί΍ ήΘθϴΑ Ϫ̯ΪϨҨϮ̳ ̶ϣ Extreme variable ΰϴϧ Εή̡ ̶ϠϴΧ ̵Ύϫ ϩΩ΍Ω ϪΑ ϢҨέϭΎϴϧ ΖγΪΑ ΖγέΩ ΍έ ϥϮϣί΁ ϪΠϴΘϧ ϭ ΩϮη ϞҨΎϤΘϣ ΎϬϧ΁ ΖϤγ ϪΑ Ϧϴ̴ϧΎϴϣ Ζγ΍ Ϧ̰Ϥϣ Εή̡ ̵Ύϫ ϩΩ΍Ω ϢϴϨ̯ ϑάΣ ΍έ Εή̡ ̵Ύϫ ϩΩ΍Ω Ζγ΍ ήΘϬΑ β̡ ̶ϟϭ΍ ̶̳ΪϨ̯΍ή̡ ̶ϨόҨΪηΎΑ ϪΘη΍Ω ΩϮΟϭ ̶ϧΎηϮ̢Ϥϫ ή̳΍ ήϴϐΘϣϭΩ Ϧϴ̴ϧΎϴϣ ϦϴΑ ̵Ύϫ boxplot ϪδҨΎϘϣ έΩ ϡΎΠϧ΍ ΍έ ϥϮϣί΁ ΩϮη ̶ϣ ϭ ΪηΎΑ ̶Ϥϧ Ύϫ Ϧϴ̴ϧΎϴϣ ϦϴΑ ̵ΩΎҨί ϑϼΘΧ΍ ΍άϟ Ζγ΍ ̶ϣϭΩ ̶̳ΪϨ̯΍ή̡ ϞϣΎη Ω΍Ω

Graph –legacy Dialogs - Boxplot – simple – variable…….income Category Axis ………..Martial

ρΎϘϧ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ έ΍ΩϮϤϧ ϦҨ΍ Ωέ΍Ω ΩϮΟϭ ̵έΎϴδΑ Εή̡

1200.00 77 250

2,980 1,812

̵ϮϨϣ ί΍ Εή̡ ̵Ύϫ ϩΩ΍Ω ϑάΣ ̵΍ήΑ Data-select cases-if income<200 ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ

3,254 5,530 4,277 4,057 1,706 4,709

Εή̡ ̶ϠϴΧ Ϫ̯ ΍έ ˻˹˹ ί΍ ήΘθϴΑ ΎΗ έ΍ΩϮϤϧ ϭ ΕΎΒγΎΤϣ έΩ ΪϨΘδϫ ΩέϭΎϴϧ

427 1000.00

Household income in thousands

Ʊřźƿř żƿř ƺŤǀŤƀƳř

4,929 150 800.00

2,535 2,001 1,263 2,844

600.00

400.00

200.00

5,363 5,840 6,017 5,845 4,386 1,557 4,965 1,953 1,425 2,432 4,925 2,308 241 4,958 1,143 5,926 6,228 283 217 5,402 4,726 3,636 2,182 1,647 4,806 3,834

6,046 97 1,147 506 5,606 2,415 3,008 278 2,049 3,042 4,005 3,307 3,758 4,904 1,749 5,649 1,286 1,312 4,449 2,839 2,636 4,604 2,687 5,043

0.00 Unmarried

Married

Marital status

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One_ sample T Test ƵŶƃ ƶŤųŚƴƃ ŹřŶƤƯ Ĩƿ ƪŝŚƤƯ Źŵ ƶƳƺưƳ ƲǀĮƳŚǀƯ ƱƺƯŻō

t ϥϮϣί΁ ϭ ϪδϳΎϘϣ ϥΎϜϣ΍ έϮΘγΩ Ϧϳ΍ ϱ΍ήΟ΍ ΎΑ Ωϭέϲϣ έΎϛ ϪΑ ϥϮϣί΁ ζΠϨγ ϱ΍ήΑ

Ζγ΍ Ύϫ ϩΩ΍Ω ϥΩϮΑ ϝΎϣήϧ ήΑ νήϓ ϥϮϣί΁ Ϧϳ΍ έΩ ϪΘη΍Ω ϱΩΪϋ ήϳΩΎϘϣ ΪϳΎΑ ϩΪη ϲϓήόϣ ήϴϐΘϣ ΪηΎΑ ΪϨηΎΑ ϪΘη΍Ω Ϣϫ ϲ̴ϟϮ̩ ϱέ΍ΪϘϣ ΎϫϩΩ΍Ω

ή̳΍ Ωήϛ Ϊϫ΍ϮΧ ΖϣϭΎϘϣ ϩΎΒΘη΍ ήΑ΍ήΑέΩ ϥϮϣί΁ Ϧϳ΍ Ζγ΍ ϩΪη ϪΘϓή̳ ήψϧ έΩ ήϳί ϞϜη ϪΑ ϥϮϣί΁ ϲγΪΣ έ΍ΪϘϣ 80 ϭ Ζγ΍ ϪϧϮϤϧ Ϧϴ̴ϧΎϴϣέ΍ΪϘϣ 

Ϣϴϧίϲϣ Ϧϴ̴ϧΎϴϣ ϱ΍ήΑ ΖϴόϤΟ ί΍ Ϫϛ

Ζγ΍

Test value …..……80

ϼΜϣ ΎΠϨϳ΍ έΩ test value έ΍ΪϘϣ ΎΑ ΪϳΎΑ ϥΎθϨϴ̴ϧΎϴϣ Ϫϛ ϲϳΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ Test variables ΖϤδϗ έΩ Ωήϴ̳ϲϣ έ΍ήϗ ΩϮη ϪδϳΎϘϣ

80 ΩϮΟϭ 95% ϥΎϨϴϤσ΍ ϪϠλΎϓ Ϛϳ ϼΜϣ ϥΎϨϴϤσ΍ ϪϠλΎϓ ΪλέΩ ϒϳήόΗ ϥΎϜϣ΍ ΰϴϧ options ϪϤϛΩ ΏΎΨΘϧ΍ ΎΑ Ωέ΍Ω ϞϣΎη Ϫϛ Ζγ΍ ϒϠΘΨϣ ϱΎϫ ϪϧϮϤϧ ί΍ ϲϠλ΍Ϯϓ ΪλέΩ ϩΪϨϫΩ ϥΎθϧ ϥΎϨϴϤσ΍ ϪϠλΎϓ ϢϴηΎΑ ϪΘη΍Ω ΖϗΩ ˬΖϴόϤΟ ί΍ ή̴ϳΩ ϪϧϮϤϧ ˺˹˹ έΩ ˬΪϫΩϲϣ ϥΎθϧ

̂̋ ϥΎϨϴϤσ΍ ϪϠλΎϓ Ϛϳ ϝΎΜϣ ϱ΍ήΑ ΪϨηΎΑ ϲϣ Ϧϴ̴ϧΎϴϣ ΩϮΑ ΪϨϫ΍ϮΧ ϪϠλΎϓ Ϧϳ΍ έΩ Ύϫ ϪϧϮϤϧ Ϧϴ̴ϧΎϴϣ ˬϪϧϮϤϧ ̂̋

Ζδϴϧ ϩΪη ϪΘϓή̳ ϪϧϮϤϧ ί΍ ̶ηΎϧ ̶ϨόΑ Ζγ΍ έ΍Ω ̶Ϩόϣ ̵ϭΎδΗ ΎҨ ϑϼΘΧ΍ ϦϴҨϮ̳ ̶ϣ ̶Θϗϭ ϪΘ̰ϧ ΎҨ ϑϼΘΧ΍ ̶ϨόҨ ΪηΎΒϧ έ΍Ω ̶Ϩόϣ ή̳΍ ΩϮΑ Ϊϫ΍ϮΧ ϦϴϤϫ ϪΠϴΘϧ ΩϮη ϥϮϣί΁ Ϣϫ ̵ή̴ҨΩ ϪϧϮϤϧ ΎΑ ή̳΍ Ϫ̰ϠΑ Ζγ΍ ϥϮϣί΁ ˬ κϴΨθΗ εϭέ Ζγ΍ ΖϴόϤΟ ί΍ ̶ηΎϧ ̵ϭΎδΗ

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Analyze – compare Means – One_ sample T Test / Test variable ……Income

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ˮϪϧ ΎҨ Ζγ΍ έ΍Ω ̶Ϩόϣ Ϊϣ΁έΩ ήϴϐΘϣ ΩέϮϣ έΩ ́˹ έ΍ΪϘϣ ΎΑ ϩΪη ϩΩί αΪΣ Ϧϴ̴ϧΎϴϣ ϢϴϨϴΒΑ Ϣϴϫ΍ϮΧ ̶ϣ

H 0:   80 H1:   80

H0: 800

SPSS 

H1: 800

T-TEST /TESTVAL = 80 /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95) .

N Household income in thousands

Mean

6400

Std. Deviation

69.4748

78.71856

Std. Error Mean .98398

One-Sample Test Test Value = 80

t Household income in thousands

-10.696

df 6399

Mean Sig. (2-tailed) Difference .000

-10.52516

95% Confidence

Interval of the

Difference

Lower Upper

-12.4541 -8.5962

ϪΠϴΘϧ Ζγ΍ 6400-1=6399 ̶ϨόҨ n-1 ̵Ω΍ί΁ ϪΟέΩ

xx 69.47.48  80 10.52516 = = = -10.696 < 0 s 0.98398 0.98398 n Sig.(2-tailed) = p  value  0.000 <0.05 =  H 0 νήϓ Ωέ Ωέ΍Ϊϧ ΩϮΟϭ H 0 ΪϴҨΎΗ ̵΍ήΑ ̶ϠϴϟΩ β̡ Ζγ΍ ϩΪη ήΘ̰̩Ϯ̯  ί΍ p  value έ΍ΪϘϣ ϥϮ̩ ˺ T



ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ ϭ ϩΪη ̶ϔϨϣ   80 ̶ϨόҨ Ζγ΍ ̶ϔϨϣ ϩίΎΑ ϦҨ΍ ϥϮ̩ Ζγ΍ έΎ̰η΁ ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ΍ H 0 νήϓ ϩΪϨϨ̯ Ωέ ϞϣΎϋ Ζγ΍ ήΘϤ̯ ́˹ ί΍ Ϧϴ̴ϧΎϴϣ ̶ϨόҨ Ζγ΍ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

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One-Sample Statistics

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H 0:   70 H1:   70

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ϢϴϨ̯ ̶ϣ έ΍ή̰Η ΰϴϧ ̀˹ ΩΪϋ ̵΍ήΑ ΍έ ϥϮϣί΁ ϦҨ΍

H0: 700

SPSS 

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H1: 700

T-TEST /TESTVAL = 70 /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95) .

One-Sample Statistics

N 6400

Std. Error Mean

Std. Deviation

69.4748

78.71856

.98398

One-Sample Test Test Value = 70

t Household income in thousands

df

-.534

Sig. (2-tailed)

Mean Difference

.594

-.52516

6399

95% Confidence Interval of the Difference Lower Upper -2.4541

1.4038

ϪΠϴΘϧ

Sig.(2-tailed) = p  value  0.594 !0.05 =

 Ωέ΍Ϊϧ ΩϮΟϭ H 0 Ωέ ̵΍ήΑ ̶ϠϴϟΩ β̡ Ζγ΍ ϩΪη ήΘ̳έΰΑ  ί΍ p  value έ΍ΪϘϣ ϥϮ̩ ˺

ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ΍ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ϩΪϨϨ̯ ΪϴҨΎΗ ϞϣΎϋ Ζδϴϧ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

ήϴϴϐΗ ΍έ  ΎҨ ϝϭ΍ ωϮϧ ̵ΎτΧ έ΍ΪϘϣ ϥ΍ϮΘϴϣ Option ΖϤδϗ έΩ One_ sample T Test ϩήΠϨ̡ έΩ ϪΘ̰ϧ ΪϧϮη ̶ϣ Ωέ Ύϫ Ϫϴοήϓ ήΘθϴΑ ϢҨ΍ά̴Α ˺ ΍έ  ̶ϨόҨ ϢҨήΒΑϻΎΑ ̂​̂ ΎΗ ΍έ ϥΎϨϴϤσ΍ ΐҨήο ή̳΍ Ω΍Ω

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Household income in thousands

Mean

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ϝΎΜϣ ΪҨέϭ΁ ΖγΪΑ ΍έweight ήϴϐΘϣ ΎҨ ϥίϭ Ϧϴ̴ϧΎϴϣ ϭ ΪϴϨ̯ ίΎΑ ΍έ spss sample data.sav ϞҨΎϓ

Analyze- report – frequency Ζγ΍ ̀̋ ϥίϭ Ϧϴ̴ϧΎϴϣ Ϫ̰ϨҨ΍ νήϓ ΎΑ ΪϴϫΩ ϡΎΠϧ΍ ̶ϧϮϣί΁

H0: 750 H 0:   75

SPSS  1:  75  0 H 

H1:   75

T-TEST /TESTVAL = 75 /MISSING = ANALYSIS /VARIABLES = weight wieght_after /CRITERIA = CI(.95) .

Statistics weight N

Valid Missing

Mean

‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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11 0 68.3182

One-Sample Test Test Value = 75

weight

t -1.220

df 10

Sig. (2-tailed) .250

Mean Difference -6.68182

95% Confidence Interval of the Difference Lower Upper -18.8805 5.5169

ϪΠϴΘϧ Sig.(2-tailed) = p  value  0.250 !0.05 =

 Ωέ΍Ϊϧ ΩϮΟϭ H 0 Ωέ ̵΍ήΑ ̶ϠϴϟΩ β̡ Ζγ΍ ϩΪη ήΘ̳έΰΑ  ί΍ p  value έ΍ΪϘϣ ϥϮ̩ ˺

ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ΍ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ϩΪϨϨ̯ ΪϴҨΎΗ ϞϣΎϋ Ζδϴϧ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

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ΪҨέϭ΁ ΖγΪΑ ΍έ weight -after ήϴϐΘϣ ΎҨ ϢҨ̫έ ί΍ ΪόΑ ϥίϭ Ϧϴ̴ϧΎϴϣ

Analyze – compare Means – One_ sample T Test / Test variable ……weight , weight - after Test value …..……75

Ζγ΍ ̀̋ ϢҨ̫έ ί΍ ΪόΑ ϥίϭ Ϧϴ̴ϧΎϴϣ Ϫ̰ϨҨ΍ νήϓ ΎΑ ΪϴϫΩ ϡΎΠϧ΍ ̶ϧϮϣί΁ T-TEST /TESTVAL = 75 /MISSING = ANALYSIS /VARIABLES = wieght_after weight /CRITERIA = CI(.95) .

One-Sample Statistics N 11 11

Std. Deviation 18.15802 14.29240

Std. Error Mean 5.47485 4.30932

One-Sample Test Test Value = 75

weight wieght_after

t -1.167 -2.658

df 9 10

Sig. (2-tailed) .273 .024

Mean Difference -7.05000 -11.45455

95% Confidence Interval of the Difference Lower Upper -20.7111 6.6111 -21.0563 -1.8528

ϪΠϴΘϧ Sig.(2-tailed) = p  value  0.024 0.05 =



H 0 νήϓ Ωέ Ωέ΍Ϊϧ ΩϮΟϭ H 0 ΪϴҨΎΗ ̵΍ήΑ ̶ϠϴϟΩ β̡ Ζγ΍ ϩΪη ήΘ̰̩Ϯ̯  ί΍ weight-afterήϴϐΘϣ)

p  value έ΍ΪϘϣ ϥϮ̩ ˺

ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ΍ έΎ̰η΁ ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ΍ H 0 νήϓ Ωέ ϞϣΎϋ ΪηΎΑ ϡή̳ϮϠϴ̯ ̀̋ Ϊϧ΍ϮΘϴϤϧ ϢҨ̫έ ί΍ ΪόΑ ϥίϭ Ϧϴ̴ϧΎϴϣ β̡ Ζγ΍ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

exclude cases analysis by ϪϨҨΰ̳ option έΩ ή̳΍ ϪΘ̰ϧ έΩ ϢϴηΎΑ ϪΘη΍Ω missing ϭ ϢϴϧΰΑ ̮ϴΗ ΍έ analysis ϭ ΪϨ̯ ̶ϣ ΏΎδΣ ϪϧΎ̳΍ΪΟ ΍έ ϡ΍Ϊ̯ ήϫ ήϴϐΘϣ ϭΩ ϪδҨΎϘϣ ϪΘϓή̳ ήψϧ έΩ ϥϭΪΑ ΍έ ϪΘϓέ έΎϛ ϪΑ ϱΎϫήϴϐΘϣ ϱ΍ήΑ ϥϮϣί΁ Ωϭέ ϲϣ έΎϛ ϪΑ ή̴ϳΩ ϱΎϫήϴϐΘϣ ϱ΍ήΑ ϩΪθϤ̳ ήϳΩΎϘϣ ̮ϴΗ ΍έ exclude cases listwise̶ϨόҨ ϡϭΩ ϪϨҨΰ̳ ή̳΍ ̶ϟϭ ̶ϣ ϑάΣ ΍έ Ωέ΍Ω missingϪ̯ ̵ case ΎҨ ΩέϮϣ Ϟ̯ ϢϴϧΰΑ έΎϛ ϪΑ ϱΎϫήϴϐΘϣ ϪϤϫ ϱ΍ήΑ ϩΪθϤ̳ έ΍ΪϘϣ ΎΑ ΩέϮϣ ΪϨ̯ έΩ Ϫ̩ ΪηΎΑ ̵΍ ϩΩ΍Ω ϥΩή̰ϧ ̠ҨΎΗήΛ΍ έΩ Ϫ̩ missing ϦҨ΍ .Ϊη Ϊϫ΍ϮΨϧ ϪΘϓή̳ ήψϧ έΩ ϥϮϣί΁ έΩ ϪΘϓέ ΪϨ̯ ̶Ϥϧ ̶ϗήϓ ΪηΎΑ ϩΪη ϒҨήόΗ missing value 30 www.ircdvd.com

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weight wieght_after

Mean 68.3182 63.5455

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( Independent-Samples T Test ƵŵŹ Śƿ Ƶƹźĭ ƹŵ Ʋǀŝ źǀƜŤƯ Ĩƿ ƲǀĮƳŚǀƯ ƱƺƯŻō

ϢϴϨϛϲϣ ϩΩΎϔΘγ΍ εϭέ Ϧϳ΍ ί΍ ϩϭή̳ ϭΩ ϦϴΑ έΩ ΖϴόϤΟ ϲ̳̬ϳϭ Ϛϳ Ϧϴ̴ϧΎϴϣ ϥΩϮΑ ήΑ΍ήΑ ϥϮϣί΁ ϱ΍ήΑ

ϲϤϛ έ΍ΪϘϣ Ϛϳ ήϴϐΘϣ ϭ Ζγ΍ ϩΪη ϊϳίϮΗ ϝΎϣήϧ ΕέϮλ ϪΑ ΖϴόϤΟ ϲ̳̬ϳϭ Ϫϛ Ζγ΍ Ϧϳ΍ήΑ νήϓ Ζγ΍ ϱΩΪϋ

Ζγ΍ ϩΪη ΏΎΨΘϧ΍ ϲϓΩΎμΗ ϭ ϞϘΘδϣ ΕέϮλ ϪΑ ϪϧϮϤϧ ϦϴϨ̪Ϥϫ Ζγ΍ έ΍ΩέϮΧήΑ ΖϴϤϫ΍ ί΍ ϥϮϣί΁ Ϧϳ΍ έΩ Εή̡ ρΎϘϧ ΩϮΟϭ ϡΪϋ ϭ ϥέΎϘΗ Ζδϴϧ αΎδΣ ϲ̴ϟϮ̩ ϪΑ ΖΒδϧ ϥϮϣί΁ Ϧϳ΍ ΪϫΩϲϣ ήϴϴϐΗ ΍έ ϥϮϣί΁ ϲΟϭήΧ

ˬϩϭή̳ ϭΩ έΩ Ύϫϥ΁ ϥΩϮΒϧ ήΑ΍ήΑ ΎΑ

ΎϫβϧΎϳέ΍ϭ ϥΩϮΑ ήΑ΍ήΑ

Analyze -

ΪϴϨϛ ϲσ ΍έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ΍ ϪΑ ϲγήΘγΩ ϱ΍ήΑ Compare Means - Independent-Samples T Test/Test variables… ….weight

test variable ΖϤδϗ έΩ ΍έ ΪϧϮη ϥϮϣί΁ ΪϳΎΑ Ϫϛ ϲϳΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ ΪϴϨϛ Ωέ΍ϭ Grouping Variable ΖϤδϗ έΩ ΪϴϨϛ κΨθϣ ΍έ ϱΪϨΑϩϭή̳

ήϴϐΘϣ ϭΩ ϦϴΑ ϪδϳΎϘϣ Ϊϧ΍ϮΗ ϲϣ ςϘϓ ϥϮϣί΁ Ϧϳ΍ Ωέϭ΁ ΖγΪΑ ΍έ ϩϭή̳ ήϴϐΘϣ ϱ΍ήΑ define group ϪϤϛΩ ΏΎΨΘϧ΍ ΎΑ ϭΩ ϒϳήόΗ ϱ΍ήΑ ήψϧ ΩέϮϣ έ΍ΪϘϣ ϱΪϨΑϩϭή̳

̵ΪϨΑ ϩϭή̳ ήϴϐΘϣ ή̳΍ ̶ΘΣ ΪϴϨϛ Ωέ΍ϭ ΍έ ϩϭή̳ ϥϮϣί΁ ϦҨ΍ ΪηΎΑ ϪΘη΍Ω ϩϭή̳ ϭΩ ί΍ ήΘθϴΑ Ϊη Ϊϫ΍ϮΧ ϪΒγΎΤϣ ϩϭή̳ ϭΩ έΩ ςϘϓ έ΍ΪϘϣ ϱϭΎδϣ ϭ ήΘθϴΑ ϭ ήΘϤϛ ΕέϮλ ϪΑ Ύϫϩϭή̳ ϚϴϜϔΗ ϥΎϜϣ΍ ΰϴϧ

cut point έ΍ΪϘϣ ϢϴψϨΗ ΎΑ ϦϴϨ̪Ϥϫ Ζδϫ ΰϴϧ cut point

H0:m1 m2 0 H0: m1  m2 SPSS 

H1:m1 m2 0 H1: m1  m2

κΨθϣ ΍έ ϝϭ΍ ϪΘγΩ ˬϱΪϨΑϩϭή̳ ήϴϐΘϣ ϱ΍ήΑ

Group1 έ΍ΪϘϣ ΪϨϛϲϣ

κΨθϣ΍έ ϡϭΩ ϪΘγΩ ˬϱΪϨΑϩϭή̳ ήϴϐΘϣ ϱ΍ήΑ

Group2 έ΍ΪϘϣ ΪϨϛϲϣ

Cut έ΍ΪϘϣ ϦϴϴόΗ ΎΑ ϥ΍ϮΘϴϣ ΩϮΑ ̵ΩΪϋ ˬ̵ΪϨΑ ϩΩέ ήϴϐΘϣ ή̳΍ Ωή̯ ϒϳήόΗ ήϳί ΕέϮλ ϪΑ ΍έ ϩϭή̳ ϭΩ Point cut point ί΍ ϱϭΎδϣ ϭ ήΘθϴΑ ήϳΩΎϘϣ ˺ ϩϭή̳ cut point ί΍ ήΘϤϛ ήϳΩΎϘϣ ˻ ϩϭή̳

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Grouping variables……gender

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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

ΪϴϨ̯ ̶γέήΑ ϥ΍Ωήϣ ϭ ϥΎϧί ϩϭή̳ ϭΩ έΩ ΍έ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ

H0: m1  m2 H1: m1  m2

SPSS 

H0:m1 m2 0

ϥ΍Ωήϣ ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1

H1:m1 m2 0

ϥΎϧί ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2

T-TEST GROUPS = gender(1 2) /MISSING = ANALYSIS /VARIABLES = weight /CRITERIA = CI(.95) .

weight

gender male

N

female

Mean

Std. Deviation

Std. Error Mean

4

75.7500

19.12023

9.56012

6

62.7500

18.86730

7.70254

Independent Samples Test Levene's Test for Equality of Variances

F weight

Equal variances assumed Equal variances not assumed

t-test for Equality of Means

Sig. .009

t

.928

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

1.062

8

.319

13.00000

12.24027

-15.22611

41.22611

1.059

6.512

.327

13.00000

12.27701

-16.47694

42.47694

ϪΠϴΘϧ Sig = p  value  0.928 > 0.05 =



̶Ϥϧ Ωέ Ύϫ βϧΎҨέ΍ϭ ̵ήΑ΍ήΑ β̡ Ζγ΍ ϩΪη ήΘ̳έΰΑ  ί΍ Ζγ΍ βϧΎҨέ΍ϭ ϪΑ ρϮΑήϣ Ϫ̯ weightήϴϐΘϣ) p  value έ΍ΪϘϣ ϥϮ̩ ˺ Ωή̯ Ϣϴϫ΍ϮΧ ̶γέήΑ ΍έ ϡϭΩ ήτγ ΕέϮμϨҨ΍ ήϴϏ έΩ ϢϴϨ̯ ̶ϣ ϩΎ̴ϧ ϝϭ΍ ήτγ έΩ Ϧϴ̴ϧΎϴϣ ϪΑ ρϮΑήϣ p  value ϪΑ ΍άϟ ΩϮη Sig(2-tailed) = p  value  0.319 > 0.05 =



ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ΍ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ΪϴҨΎΗ ϞϣΎϋ Ζγ΍ ήΑ΍ήΑ ϥΎϧί ϭ ϥ΍Ωήϣ ϩϭή̳ ϭΩ έΩ ϥίϭ Ϧϴ̴ϧΎϴϣ β̡ Ζδϴϧ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

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Group Statistics

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ΪϴϨ̯ ̶γέήΑ ϝΎγ ˼˹ ϦϴҨΎ̡ ϭ ϝΎγ ˼˹ ̵ϻΎΑ ̶Ϩγ ϩϭή̳ ϭΩ έΩ ΍έ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ compute ΎҨ recode ϩ΍έ ί΍ ϥ΍ϮΘϴϣ ϝΎγ ˼˹ ϦϴҨΎ̡ ϭ ϝΎγ ˼˹ ̵ϻΎΑ ϩϭή̳ ϭΩ ϪΑ ϥίϭ ήϴϐΘϣ ̵ΪϨΑ ϩϭή̳ ̵΍ήΑ ϪϤ̳Ω ϭ Independent-Samples T Test ϩήΠϨ̡ έΩ Ϫ̯ Ζγ΍ ϦҨ΍ ϩ΍έ ϦҨήΗ ϩΩΎγ ̶ϟϭ ΩϮϤϧ ϩΩΎϔΘγ΍ Ωή̯ Ωέ΍ϭ ΍έ ˼˹ ΩΪϋ ϭ ΩϮϤϧ ϩΩΎϔΘγ΍ cutpoint ϪϨҨΰ̳ ί΍ option

H0: m1  m2 H1: m1  m2

H0:m1 m2 0

SPSS 

ϝΎγ ˼˹ ί΍ ήΗ ϦϴҨΎ̡ ̶Ϩγ ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1

H1:m1 m2 0

ϝΎγ ˼˹ ί΍ ήΗϻΎΑ ̶Ϩγ ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2

Group Statistics

weight

age >= 30.00

7

Mean 74.5714

Std. Deviation 18.36534

Std. Error Mean 6.94145

3

52.5000

10.85127

6.26498

N

< 30.00

Independent Samples Test

ϪΠϴΘϧ Sig = p  value 

Levene's 0.281 > 0.05Test = for Equality of Variances



F weight

Equal variances assumed Equal variances not assumed

1.334

Sig. .281

t-test for Equality of Means

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

1.903

8

.093

22.07143

11.59642

-4.66997

48.81283

2.360

6.606

.052

22.07143

9.35060

-.30937

44.45223

ϪΠϴΘϧ ̶Ϥϧ Ωέ Ύϫ βϧΎҨέ΍ϭ ̵ήΑ΍ήΑ β̡ Ζγ΍ ϩΪη ήΘ̳έΰΑ  ί΍ Ζγ΍ βϧΎҨέ΍ϭ ϪΑ ρϮΑήϣ Ϫ̯ weightήϴϐΘϣ) p  value έ΍ΪϘϣ ϥϮ̩ ˺ Ωή̯ Ϣϴϫ΍ϮΧ ̶γέήΑ ΍έ ϡϭΩ ήτγ ΕέϮμϨҨ΍ ήϴϏ έΩ ϢϴϨ̯ ̶ϣ ϩΎ̴ϧ ϝϭ΍ ήτγ έΩ Ϧϴ̴ϧΎϴϣ ϪΑ ρϮΑήϣ p  value ϪΑ ΍άϟ ΩϮη Sig(2-tailed) = p  value  0.093 > 0.05 =



ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ΍ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ΪϴҨΎΗ ϞϣΎϋ Ζγ΍ ήΑ΍ήΑ ϝΎγ ˼˹ ί΍ ήΗ ϦϴҨΎ̡ ϭ ϝΎγ ˼˹ ί΍ ήΗϻΎΑ ̶Ϩγ ϩϭή̳ ϭΩ έΩ ϥίϭ Ϧϴ̴ϧΎϴϣ β̡ Ζδϴϧ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

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T-TEST GROUPS = age(30) /MISSING = ANALYSIS /VARIABLES = weight /CRITERIA = CI(.95) .

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Ϫ̯ ̶ϧΎδ̯ ϭ Ϊϧέ΍Ω ϪϴϟΎϋ ΕϼϴμΤΗ Ϫ̯ ̶ϧΎδ̯ ϩϭή̳ ϭΩέΩ Demo ϞҨΎϓ έΩ ΍έ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ΪϴϨ̯ ̶γέήΑ Ϊϧ΍ ϩΩή̰ϧ ϞϴϤ̰Η ΍έ ϥΎθΗϼϴμΤΗ

ϩΪθϧ ϞϴϤ̰Η ΕϼϴμΤΗ ϩϭή̳ έΩ income Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1

H0: m1  m2 H1: m1  m2

SPSS 

H0:m1 m2 0

ϪϴϟΎϋ ΕϼϴμΤΗ ϩϭή̳ έΩ income Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2

H1:m1 m2 0

.

T-TEST GROUPS = ed(1 5) /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95) .

Household income in thousands

Level of education Did not complete high school

N

Post-undergraduate degree

Mean

Std. Deviation

Std. Error Mean

1390

59.8662

61.30036

1.64420

359

87.1699

94.79998

5.00335

‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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Group Statistics

Independent Samples Test Levene's Test for Equality of Variances

F Household income in thousands

Equal variances assumed Equal variances not assumed

t-test for Equality of Means

Sig.

35.128

t

.000

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

-6.636

1747

.000

-27.30373

4.11419

-35.37298

-19.23448

-5.184

438.180

.000

-27.30373

5.26659

-37.65464

-16.95282

ϪΠϴΘϧ Sig = p  value  0.000 0.05 =



Ωέ Ύϫ βϧΎҨέ΍ϭ ̵ήΑ΍ήΑ β̡ Ζγ΍ ϩΪη ήΘ̰̩Ϯ̯  ί΍ Ζγ΍ βϧΎҨέ΍ϭ ϪΑ ρϮΑήϣ Ϫ̯ income ήϴϐΘϣ) p  value έ΍ΪϘϣ ϥϮ̩ ˺ ΩϮη ̶ϣ Ωέ H 0 νήϓ β̡ Ζγ΍  ί΍ ήΘ̰̩Ϯ̯ ϢϴϨ̯ ̶ϣ ϩΎ̴ϧ ϡϭΩ ήτγ έΩ Ϧϴ̴ϧΎϴϣ ϪΑ ρϮΑήϣ p  value ϪΑ ΍άϟ ΩϮη ̶ϣ Sig(2-tailed) = p  value  0.000 0.05 =



ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ΍ έΎ̰η΁ ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ΍ H 0 νήϓ Ωέ ϞϣΎϋ Ζδϴϧ ήΑ΍ήΑ ϪϴϟΎϋ ϭ ϩΪθϧ ϞϴϤ̰Η ΕϼϴμΤΗ ϩϭή̳ ϭΩ έΩ Ϊϣ΁έΩ Ϧϴ̴ϧΎϴϣ β̡ Ζγ΍ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˼ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̊

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

( Paired-Samples T Test ŢƠū źǀƜŤƯ ƹŵ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ ƱƺƯŻō ϢϴϨϛϲϣ ϩΩΎϔΘγ΍ ϥϮϣί΁ Ϧϳ΍ ί΍ ΪϧϮη ϪδϳΎϘϣ ΪϳΎΑ ϪϧϮϤϧ έΩ ήϴϐΘϣ ϭΩ Ϧϴ̴ϧΎϴϣ ή̳΍

Ϫϛ ΪϧϮηϲϣ ϪΘϓή̳ ήψϧ έΩ

x,y ΐΗήϣ Νϭί ΕέϮλ ϪΑ ϩΪη ϱήϴ̳ ϩί΍Ϊϧ΍ ϱΎϫ ϪμΨθϣ ϥϮϣί΁ Ϧϳ΍ έΩ Ζγ΍ ϪϧϮϤϧ έΩ ϡϭΩ ϲ̳̬ϳϭ y ϭ ϝϭ΍ ϲ̳̬ϳϭ x ϥ΁ έΩ ΍ΪΘΑ΍ έΩ ϪϧϮϤϧ Ω΍ήϓ΍ ϪΑ ϢϴϨϛ κΨθϣ ϥϮΧ έΎθϓ ζϫΎϛ έΩ ΍έ ϭέ΍Ω Ϛϳ ήΛ΍ Ϣϴϫ΍ϮΧ ϲϣ ϝΎΜϣ ϱ΍ήΑ ϥϮΧ έΎθϓ ΰϴϧ ϲόϗ΍ϭ ϭέ΍Ω ϥΪϧ΍ΩέϮΧ ί΍ β̢γ ϢϴϨϛ ϲϣ ϱήϴ̳ ϩί΍Ϊϧ΍ ΍έ ϥϮΧ έΎθϓ ϩΩ΍Ω ΎϤϧϭέ΍Ω ϪδϳΎϘϣ ϲόϗ΍ϭ ϱϭέ΍Ω ί΍ ΪόΑ ϭ ϞΒϗ ΍έ ϥϮΧ έΎθϓ Ϧϴ̴ϧΎϴϣ Ϊϫ΍ϮΧϲϣ ϥϮϣί

΁ Ϊη Ϊϫ΍ϮΧ ϱήϴ̳ϩί΍ Ϊϧ΍ ΪϨϛ ΪϨΘδϫ ϱΩΪϋ ήϳΩΎϘϣ Ζγ΍ Ϧϳ΍ ήΑ νήϓ Ζγ΍ ϝΎϣήϧ ϊϳίϮΗ ϱ΍έ΍Ω ΎϬϧ΁ ΕϭΎϔΗ Ύϳ Ϊϧέ΍Ω ϝΎϣήϧ ϊϳίϮΗ ήϴϐΘϣ ϭΩ ήϫ ΎϫϩΩ΍Ω

Ϊϧ΍ϩΪη ΏΎΨΘϧ΍ ϲϓΩΎμΗ ϭ ϞϘΘδϣ ΕέϮλ ϪΑ Ύϫ

ϪϧϮϤϧ έϮσ ϪΑ ήϴϐΘϣ Ϛϳ έΩ ϩΪθϤ̳ έ΍ΪϘϣ ΩϮΟϭ ΕέϮλ έΩ ΪηΎΑ ήΑ΍ήΑ ΪϳΎΑ ήϴϐΘϣ ϭΩ ήϫ ϱ΍ήΑ ΎϫϪϧϮϤϧ

Ω΍ΪόΗ Ϊη Ϊϫ΍ϮΧ ϪΘϓή̳ ϩΪϳΩΎϧ ΩέϮϣ ϥ΁ ϞϣΎϛ Ωέ΍Ϊϧ ϲϟΎϜη΍ ήϴϐΘϣ ϭΩ βϧΎϳέ΍ϭ έ΍ΪϘϣ έΩ ΕϭΎϔΗ ΪϴϨϛ ϲσ ΍έ ήϳί ϞΣ΍ήϣ ϥϮϣί΁ Ϧϳ΍ ϪΑ ϲγήΘγΩ ϱ΍ήΑ Analyze -

Compare Means - Paired-Samples T Test...

H0: m1  m2 H1: m1  m2

SPSS 

H0:m1 m2 0

ϝϭ΍ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1

H1:m1 m2 0

ϡϭΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2

ϪΑ ϭ ϩΩήϛ ΏΎΨΘϧ΍ ̠̩ ΖϤγ ΖϤδϗ έΩ ΍έ ήϴϐΘϣ ϭΩ ήϫ .ΪϴϫΩ ϝΎϘΘϧ΍ pair Variable ΖϤδϗ

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Analyze -

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ΪϴϨ̯ ϪδҨΎϘϣ Ϣϫ ΎΑ ΍έ weight-after ϭ weight ήϴϐΘϣ ϭΩ

Compare Means - Paired-Samples T Test...

H0: m1  m2 H1: m1  m2

SPSS 

H0:m1 m2 0

weight ϥίϭήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1

H1:m1 m2 0

weight-after ϢҨ̫έ ί΍ ΪόΑ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2

T-TEST PAIRS = weight WITH wieght_after (PAIRED) /CRITERIA = CI(.95) /MISSING = ANALYSIS.

N Pair 1

weight & wieght_after

10

Correlation .941

Sig. .000

Paired Samples Test Paired Differences

Pair 1

weight - wieght_after

Mean 5.65000

Std. Deviation 7.37884

Std. Error Mean 2.33339

95% Confidence Interval of the Difference Lower Upper .37150 10.92850

t 2.421

df 9

Sig. (2-tailed) .039

ϪΠϴΘϧ

ήϴϐΘϣ ϭΩ ̶ϨόҨ ΪηΎΑ ήΘ̰ҨΩΰϧ 1 ΩΪϋ ϪΑ ΩΪϋ ϦҨ΍ Ϫ̩ ήϫ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ΍έ 0.941 ΩΪϋ correlation ΎҨ ̶̴ΘδΒϤϫ ΐҨήο ˺ ΪϧΩϮΑ ̲ϨϫΎϤϫ Ϣϫ ΎΑ ˱ϼϣΎ̯ ϭ Ζγ΍ ϪΘη΍Ω ΩϮΟϭ ϢҨ̫έ ί΍ β̡ ϥίϭ ζϫΎ̯ Ωέ΍Ϯϣ ϡΎϤΗ έΩ ̶ϨόҨ Ϊϧέ΍Ω Ϣϫ ̵ϭέ ̵ήΘθϴΑ ήΛ΍ ΩϮη ̶ϣ Ωέ H 0 νήϓ ˬ  ί΍ p  value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˻ Sig(2-tailed) = p  value  0.039 0.05 =



ΩϮΧ ϦҨ΍ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩ΍Ω ϥΎθϧ ΩΪϋ ϭΩ ˼ Ζγ΍ έΎ̰η΁ ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ΍ H 0 νήϓ Ωέ ϞϣΎϋ

Ζδϴϧ ήΑ΍ήΑ ϢҨ̫έ ί΍ ΪόΑ ϥίϭ ϭ ϥίϭ ήϴϐΘϣ ΖϔΟ Ϧϴ̴ϧΎϴϣ β̡ Ζγ΍ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ̊ Ζγ΍ έ΍Ω ̶Ϩόϣ ˬΪηΎΑ ˻ ί΍ ήΘ̳έΰΑ ΎҨ ήΑ΍ήΑt ϖϠτϣ έΪϗ .̋

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Paired Samples Correlations

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ΩϮΟϭ ϩϭή̳ ϦϳΪϨ̩ ϦϴΑ έΩ κΧΎη Ϛϳ έ΍ΪϘϣ ϪδϳΎϘϣ ϥΎϜϣ΍ Ϫϓήσ Ϛϳ βϧΎϳέ΍ϭ ΰϴϟΎϧ΁ ϝϭΪΟ ί΍ ϩΩΎϔΘγ΍ ΎΑ Ωέ΍Ω ϲϫϭή̳ ϥϭέΩ ϱΎτΧ ΕΎόΑήϣ Ϧϴ̴ϧΎϴϣ έ΍ΪϘϣ ΖΒδϧ ή̳΍ βϧΎϳέ΍ϭ ΰϴϟΎϧ΁ ϝϭΪΟ ϱΎϫ ϩέΎϣ΁ ϪΑ ϪΟϮΗ ΎΑ ϲϫϭή̳ ϦϴΑ ϱΎτΧ ΕΎόΑήϣ Ϧϴ̴ϧΎϴϣ έ΍ΪϘϣ ΎΑ ϩϭή̳ Ϧϴ̴ϧΎϴϣ ί΍ ϩϭή̳ ήϫ ήϳΩΎϘϣ ϑϼΘΧ΍ ΕΎόΑήϣ ωϮϤΠϣ

ζϘϧ ϩΪϨϫΩ ϥΎθϧ ΪηΎΑ ϱΩΎϳί ϑϼΘΧ΍ ϱ΍έ΍Ω Ϟϛ Ϧϴ̴ϧΎϴϣ ί΍ ϩϭή̳ ήϫ Ϧϴ̴ϧΎϴϣ ϑϼΘΧ΍ ΕΎόΑήϣ ωϮϤΠϣ

ΪϫΩϲϣ ϥΎθϧ ΍έ ϩϭή̳ Ϧϴ̴ϧΎϴϣ ήϴϴϐΗ έΩ ϞϣΎϋ

Ϫϴϟϭ΍ ϱΎϫ νήϓ ΪηΎΑ ϪΘη΍Ω ΢ϴΤλ ήϳΩΎϘϣ ΪϳΎΑ έϮΘϛΎϓ ήϴϐΘϣ ΪηΎΑ ϪΘγϮϴ̡ ήϳΩΎϘϣ ϱ΍έ΍Ω ΪϳΎΑ ϪΘδΑ΍ϭ ήϴϐΘϣ ΪϨηΎΑ ϲϓΩΎμΗ ϝΎϣήϧ ϊϳίϮΗ ϱ΍έ΍Ω ΪϳΎΑ ϩϭή̳ ήϫ έΩ ϪϧϮϤϧ ΪηΎΑ ϥέΎϘΗ ϱ΍έ΍Ω ϊϳίϮΗ ΪϳΎΑ ϲϟϭ Ζγ΍ ϡϭΎϘϣ ϥΩϮΑ ϝΎϣήϧ ρήη ϪΑ ΖΒδϧ βϧΎϳέ΍ϭ ΰϴϟΎϧ΁ ϥϮϣί΁ ϩέΎϣ΁ ί΍ ϩΩΎϔΘγ΍ ΎΑ ΍έ ρήη Ϧϳ΍ ΪϨηΎΑ ϩΪη ΏΎΨΘϧ΍ ήΑ΍ήΑ βϧΎϳέ΍ϭ ΎΑ ϲΘϴόϤΟ ί΍ ΪϳΎΑ Ύϫϩϭή̳

Ωήϛ ϱήϴ̳ ϩί΍Ϊϧ΍ ϥ΍ϮΗϲϣ

Levens

Ωέ΍Ω ΩϮΟϭ Ύϫϩϭή̳ ϦϴΑ έΩ Ϧϴ̴ϧΎϴϣ έ΍ΪϘϣ έΩ ήϴϴϐΗ ϱήϴ̳ ϩί΍Ϊϧ

΍ ϥΎϜϣ΍ ϝϭΪΟ Ϧϳ΍ ί΍ ϩΩΎϔΘγ΍ ΎΑ ΪϴϨϛ ϲσ ΍έ ήϳί ϞΣ΍ήϣ έϮΘγΩ Ϧϳ΍ ϪΑ ϲγήΘγΩ ϱ΍ήΑ

Analyze - Compare Means - One-Way ANOVA... Ωέ΍ϭ Dependent List ϞΤϣ έΩ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ ΪϴϨϛ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ έ΍ΪϘϣ Ϫϛ ϱήϴϐΘϣ ϞϣΎϋ ήϴϐΘϣ ϥϮϣί΁ Ϊϴϫ΍ϮΧ ϲϣ ϥ΁ ϒϠΘΨϣ ϱΎϫϩϭή̳

ϪΑ ΖΒδϧ ΪϴϨϛ Ωέ΍ϭ Factor ΖϤδϗ έΩ ΍έ ΪϴϨϛ Ωέ΍ϭ Ζδϴϟ έΩ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ ϦϳΪϨ̩ ϥ΍ϮΗϲϣ

ήϴϐΘϣ Ϛϳ ί΍ ςϘϓ έϮΘϛΎϓ ήϴϐΘϣ ϱ΍ήΑ ϲϟϭ ΪϴϨϛ Ωήϛ ϩΩΎϔΘγ΍ ϥ΍ϮΗϲϣ

έϮΘϛΎϓ ήϴϐΘϣ ΡϮτγ έΩ ήϴϐΘϣ ήϫ Ϧϴ̴ϧΎϴϣ έΎϛ Ϧϳ΍ ΎΑ ΪϧϮηϲϣ ϪδϳΎϘϣ

ϡΎΠϧ΍ ΍έ ΎϫϲϳΎΗϭΩ ϪΑ ρϮΑήϣ ϥϮϣί΁ Ϊϴϫ

΍ϮΧϲϣ ή̳΍

ΪϴϨϛ ΏΎΨΘϧ΍ ΍έ Post Hoc ϪϨϳΰ̳ ΪϴϫΩ ϥΩϮΑ ήΑ΍ήΑ ΕέϮλ έΩ Ϊϴϧ΍ϮΗ ϲϣ ΍έ ϥϮϣί΁ ωϮϧ ΪϨϧΎϣ ϲϟϭ΍ έΩΎϛ ϱΎϫ ϪϨϳΰ̳ ί΍ ϲϜϳ Ύϫ βϧΎϳέ΍ϭ ΪϴϨϛ ΏΎΨΘϧ΍ tukey-Bunfferoni- LSD ΪϴϧΰΑ ΍έContinue

ϪϤϛΩ β̢γ ϱΎϫϦϴ̴ ϧΎϴϣ ί΍ ϲτΧ ϲΒϴϛήΗ Ϊϴϫ΍ϮΧ ϲϣ ή̳΍ Contrast ϪϤϛΩ ί΍ ΪϴϨϛ ϪδϳΎϘϣ Ϣϫ ΎΑ ΍έ Ύϫϩϭή̳

ΪϴϨϛ ϩΩΎϔΘγ΍ ΐϳ΍ήο ωϮϤΠϣ Ζγ΍ ήΘϬΑ Ϫϛ ΪϴηΎΑ ϪΘη΍Ω ϪΟϮΗ ΪηΎΑ ήϔλ ήΑ΍ήΑ ϲτΧ ΐϴϛήΗ 37 www.ircdvd.com

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One-Way Anova Ƶƹźĭ ƲƿŶƴģ Źŵ źǀƜŤƯ Ĩƿ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ ƱƺƯŻō

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źǀƜŤƯ Ŷƴģ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ ƱƺƯŻō ΩϮΑ Ϊϫ΍ϮΧ ήϳί ΕέϮλ ϪΑ ϥϮϣί΁ νήϓ H0: m1=m2=m3 H1: m1#m2#m3

ϡΪϋ ΖϠϋ Ϫϛ Ωήϛ κΨθϣ ϲϳΎΗϭΩ ϱΎϫϥϮϣί΁ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϥ΍ϮΗ

ϲϣ ΩϮη Ωέ ήϔλ νήϓ Ϫϛ ϲΗέϮλ έΩ Ζγ΍ϩΩϮΑ Νϭί ϡ΍Ϊϛ ϱ΍ήΑ ϱϭΎδΗ

ΪϨγΎϨηϲϣ ΰϴϧ

Post Hoc ϥ΍ϮϨϋ ϪΑ ΍έ ϥϮϣί΁ Ϧϳ΍ Ωέ΍Ω ΩϮΟϭ Contrast ΎϫϦϴ̴ϧΎϴϣ ί΍ ϲΒϴϛήΗ ϥϮϣί΁ ϥΎϜϣ΍ ϦϴϨ̪Ϥϫ

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Coefficient ΖϤδϗ έΩ ΍έ Ϧϴ̴ϧΎϴϣ ήϫ ΐϳ΍ήο ΐϴΗήΗ ϪΑ ΪϴϧΰΑ ΍έ Add ϪϤϛΩ ϭ ϩΩήϛ Ωέ΍ϭ Coefficient ΖϤδϗ έΩ ΐϳ΍ήο ωϮϤΠϣ Ϫϛ ΪϴϨϛ ΖϗΩ ΪηΎΑ ήϔλ ήΑ΍ήΑ ΪϳΎΑ ϭ Ζγ΍ ϩΪη ϪΒγΎΤϣ Total ϪϨϳΰ̳ Ϊϳέ΍Ω ΝΎϴΘΣ΍ ΎϫϦϴ̴ϧΎϴϣ ϦϴΑ ϲτΧ ϪτΑ΍έ Ϛϳ ή̳΍

ΪϴϨϛ ΏΎΨΘϧ΍ ΍έ Polynomial ΪϴϧΰΑ ΍έ Continue ϪϤϛΩ

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ˮΩέ΍Ω ̵ήΛ΍ Ϫ̩ ̶ϠϴμΤΗ ϒϠΘΨϣ ΡϮτγ ήΑ Ϊϣ΁έΩ Ϧϴ̴ϧΎϴϣ Ϫ̯ ΪϴϨ̯ ϪδҨΎϘϣ Demo ϞҨΎϓ έΩ

Analyze - Compare Means - One-Way Anova

H 0 : m1  m 2  m3  m 4  m5

GET FILE='C:\Program Files\SPSS\Tutorial\sample_files\demo.sav'.

DATASET NAME DataSet1 WINDOW=FRONT. ONEWAY income BY ed /STATISTICS HOMOGENEITY /MISSING ANALYSIS .

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H 1 : m1  m 2  m3  m 4  m5

did not completed high school ϝϭ΍ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1 high sckool degree ϡϭΩ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2 some college ϡϮγ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m3 college degree ϡέΎϬ̩ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m4 post-undergraduate degree ϢΠϨ̡ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m5

Test of Homogeneity of Variances Household income in thousands Levene Statistic 14.766

df1

df2 6395

4

Sig. .000

ANOVA Household income in thousands Sum of Squares Between Groups

df

Mean Square

376079.699

4

94019.925

Within Groups

39276042.251

6395

6141.680

Total

39652121.950

6399

ΩϮη ̶ϣ Ωέ H 0 νήϓ ˬ  Sig = p  value  0.000 0.05 =

F 15.309

Sig. .000

ϪΠϴΘϧ ί΍ p  value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˺



ήΑ΍ήΑ ΕϼϴμΤΗ ̵ΎϬϫϭή̳ ϦϴΑ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ β̡ Ζγ΍ ΕϼϴμΤΗ ̵ΪϨΑ ϩϭή̳ ί΍ ̶ηΎϧ ϭ Ζγ΍ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˻ Ζδϴϧ between Groups - Ϟϛ Ϧϴ̴ϧΎϴϣ ί΍ ϩϭή̳ ήϫ Ϧϴ̴ϧΎϴϣ ϑϼΘΧ΍ ΕΎόΑήϣ ωϮϤΠϣ ̶ϫϭή̳ ϦϴΑ ̵ΎτΧ ΕΎόΑήϣ ΩΎҨί ΕϭΎϔΗ ˼ ϩΪϨϫΩ ϥΎθϧ within Groups ± ϩϭή̳ Ϧϴ̴ϧΎϴϣ ί΍ ϩϭή̳ ήϫ ήϳΩΎϘϣ ϑϼΘΧ΍ ΕΎόΑήϣ ωϮϤΠϣ ϲϫϭή̳ ϥϭέΩ ϱΎτΧ ΕΎόΑήϣ ΎΑ Ζγ΍ ϩϭή̳ Ϧϴ̴ϧΎϴϣ ήϴϴϐΗ έΩ factor ϞϣΎϋ ζϘϧ

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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1

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βϧΎҨέ΍ϭ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ Test of Homogeneity of Variances ϝϭΪΟ έΩ ΎϬδϧΎҨέ΍ϭ ϪδҨΎϘϣ ϥϮϣί΁ ί΍ ϩΪϣ΁ ΖγΪΑ ΞҨΎΘϧ ̊ p  value  0.000 = Sig <  ϭ Ϊϧέ΍Ω ̵έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ΕϼϴμΤΗ ήϴϐΘϣ ϩϭή̳ ̋ έΩ Ϊϣ΁ έΩ ήϴϐΘϣ ̵Ύϫ

Ωέ΍Ω ΩϮΟϭ ΖϟΎΣ ϭΩ Post HOC ϩήΠϨ̡ έΩ ΪϨηΎΑ ϪΘη΍Ϊϧ ̶ΗϭΎϔΗ ϊϣ΍ϮΟ βϧΎϳέ΍ϭ Ϫ̯ ̶ΘϟΎΣ έΩ ϩΩΎϔΘγ΍ ΩέϮϣ ̵ΎϬϧϮϣί΁ ϪΑ ρϮΑήϣ ϻΎΑ ΖϤδϗ Equal Variances Assumed

ΪϨηΎΑ ΕϭΎϔΘϣ ϊϣ΍ϮΟ βϧΎϳέ΍ϭ Ϫ̯ ̶ΘϟΎΣ έΩ ϩΩΎϔΘγ΍ ΩέϮϣ ̵ΎϬϧϮϣί΁ ϪΑ ρϮΑήϣ ϦϴϳΎ̡ ΖϤδϗ Equal Variances Not Assumed

έϮσ ϪΑ Ϊϧέ΍Ϊϧ ̵έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ Ϣϫ ΎΑ ΎϬϫϭή̳ βϧΎϳέ΍ϭ Ϫ̯ ̶ΘϟΎΣ έΩ ΎϬδϧΎҨέ΍ϭ ̵ήΑ΍ήΑ ϻΎΑ ΖϤδϗ έΩ ΩϮη ̶ϣ ΏΎΨΘϧ΍ ̵έΎϣ΁ ϞϴϠΤΗ ϭ ϪϳΰΠΗ ̵΍ήΑ ΍έ Dunnett ˬDuncanˬTukey ϝϭ΍ΪΘϣ ϥϮϣί΁ Ϫγ ϪϧϮϤϧ ϩϭή̳ ̋ ΎϬϫϭή̳ ί΍ ̶̰ϳ ϥΎϴϣ ί΍ Ϫ̯ Ω΍Ω έ΍ήϗ ϪΟϮΗ ΩέϮϣ ΪϳΎΑ ΍έ ϪΘ̰ϧ Ϧϳ΍ Dunnett ϥϮϣί΁ ΩέϮϣ έΩ Ϧϳ΍ ΪϨΠϨδΑ ϥ΁ ΎΑ ΍έ ΎϬϫϭή̳ ήϳΎγ ΎΗ Ϣϳήϴ̳ ̶ϣ ήψϧ έΩ ΪϫΎη ϝήΘϨ̯ ϩϭή̳ ϥ΍ϮϨϋ ϪΑ ΍έ ̶̰ϳ ̶ϠϴμΤΗ ϩϭή̳ ϥ΍ϮϨϋ ϪΑ ΎϬϫϭή̳ Ϧϳ΍ ί΍ ϡ΍Ϊ̯ ήϫ ΏΎΨΘϧ΍ ΪηΎΑ Last ήΧ΁ ϩϭή̳ Ύϳ First ϝϭ΍ ϩϭή̳ ϥ΍ϮΗ ̶ϣ ϩϭή̳ Control ϪϨϳΰ̳ Dunnett ϥϮϣί΁ ϥΩή̯ ϝΎόϓ ΎΑ έΎ̯ Ϧϳ΍ ̵΍ήΑ ΪϨ̯ ̶Ϥϧ ΩΎΠϳ΍ ̵ήϴϴϐΗ ΞϳΎΘϧ έΩ ήΧ΁ Ύϳ ϝϭ΍ ήΑ ̮ϴϠ̯ ΎΑ ΪόΑ ϪϠΣήϣ έΩ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ ϝήΘϨ̯ ϩϭή̳ ϥ΁ ̵ϭήΑϭέ ϊΑήϣ έΩ ϭ ϩΪη ϝΎόϓ Category ΍έ ήψϧ ΩέϮϣ ̵ΎϬϴΟϭήΧ Ϣϴϧ΍ϮΗ ̶ϣ Ok ̵ϭέ ήΑ ̮ϴϠ̯ ΎΑ ϭ Ϣϳϭέ ̶ϣ ̶ϠΒϗ ϩήΠϨ̡ ϪΑ Continue ̵ϭέ ϢϴϨ̯ ϩΪϫΎθϣ ˬ Games Howell ˬ Dunnet’s T3 ˬ Tamhane’s T2 ̵ΎϬϧϮϣί΁ ΎϬδϧΎҨέ΍ϭ ̵ήΑ΍ήΑΎϧ ϦϴҨΎ̡ ΖϤδϗ έΩ ΪϧϮη ̶ϣ ϩΩΎϔΘγ΍ Dunnet’s C ΍έ ΎϬδϧΎϳέ΍ϭ ̵ήΑ΍ήΑ Ϫ̯ Ho νήϓ ή̴ϳΩ ϥΎϴΑ ϪΑ ˬΪθϧ ϪΘϓήϳά̡ ΎϬδϧΎϳέ΍ϭ ̶Ϩ̴Ϥϫ νήϓ ϥϮ̩ ϝΎΜϣ Ϧϳ΍ έΩ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ̶ϨϴҨΎ̡ ̵ΎϬϧϮϣί΁ ί΍ Ζγ΍ ϩΪη Ωέ ΪϨ̯ ̶ϣ Ρήτϣ

έΩ ΪϨΘδϫ ̵ϭΎδϣ ˱ΎΒҨήϘΗ ΎҨ ̵ϭΎδϣ ϪϧϮϤϧ ̵Ύϫ ϩί΍Ϊϧ΍ Ϫ̯ ̶όϗϮϣ ΎΗ F ϩέΎϣ΁ ˬ post hoc ΞҨΎΘϧέΩ ϪΘ̰ϧ ΞҨΎΘϧ ϭ Ζγ΍ ϥ΍ϮΗ ΪϗΎϓ ̵ϭΎδϣΎϧ ϪϧϮϤϧ ϩί΍Ϊϧ΍ ΎΑ ̶ϟϭ ΪϨ̯ ̶ϣ ΖϣϭΎϘϣ ΰϴϧ ̵ϭΎδϣΎϧ ̵Ύϫ βϧΎҨέ΍ϭ ήΑ΍ήΑ ΪϫΩ ̶ϣ ΢ϴΤλΎϧ ΞҨΎΘϧ ϦҨ΍ ϪΑ Ϫ̯ ϢϴΘδϫ ϝΩϭΩ Ύϣ ΪηΎΑ ˹ ˹̋ ̮ҨΩΰϧ sig ˬF ϩέΎϣ΁ ΩέϮϣ έΩ ϭ ΪϧΩϮΒϧ ήΑ΍ήΑ Ύϫ βϧΎҨέ΍ϭ ή̳΍ ϮϫΩή̯ ΏΎΨΘϧ΍ ΰϴϧ ΍έ Welch,Brown-Forsythe ̵Ύϫ ϪϨҨΰ̳ option ϪϤ̳Ω έΩ ΍άϟ ˮϪϧ ΎҨ ϢϴϨ̰Α ΩΎϤΘϋ΍ ϢϴϨ̯ ΍ήΟ΍ ΍έ ANOVA ϩέΎΑϭΩ ϭ ϩΩή̯ ϑάΣ ΍έ Εή̡ ήҨΩΎϘϣ ΪҨΎΑ ΪϴҨΎΗ ΕέϮλ έΩ ϢϴϨ̯ ̶ϣ ̶γέήΑ

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ήϴϐΘϣ ϩϭή̳ ̋ έΩ Ϊϣ΁έΩ ήϴϐΘϣ ̵Ύϫ Ϧϴ̴ϧΎϴϣ ϦϴΑ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ANOVA ϝϭΪΟ έΩ βϧΎҨέ΍ϭ ΰϴϟΎϧ΁ ί΍ ϩΪϣ΁ ΖγΪΑ ΞҨΎΘϧ ̋ Ϣϫ ΎΑ ϭΩ ϪΑϭΩ Post HOC ϪϨҨΰ̳ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϭ ϢϴηΎΑ ΎϬϓϼΘΧ΍ ϝΎΒϧΩ ϪΑ ΪҨΎΑ β̡ Ωέ΍Ω ΩϮΟϭ ̵έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ΕϼϴμΤΗ ϢϴϨ̯ ϪδҨΎϘϣ

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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1

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Post Hoc Tests Multiple Comparisons Dependent Variable: Household income in thousands

(I) Level of education

Tamhon's

Did not complete high school

m1

m2 Some college

High school degree-m2

College degree

m4 Post-undergraduate degree

m5

m1  m 3 m1  m 4 m1  m 5 m2  m4 m2  m5 m5  m3

Std. Error

Sig. Upper Bound

Lower Bound .1905

-6.34094

2.33139

.064

-12.8724

Some college-m3

-10.26837(*)

2.74450

.002

-17.9587

-2.5781

College degree-m4

-18.78400(*)

3.01158

.000

-27.2233

-10.3447

Post-undergraduate degree-m5

-27.30373(*)

5.26659

.000

-42.1229

-12.4846

Did not complete high school-m1 Some college-m3 College degree-m4

6.34094 -3.92743 -12.44306(*)

2.33139 2.74970 3.01632

.064 .811 .000

-.1905 -11.6318 -20.8952

12.8724 3.7769 -3.9909

Post-undergraduate degree-m5

-20.96279(*)

5.26930

.001

-35.7894

-6.1362

Did not complete high school-m1

10.26837(*)

2.74450

.002

2.5781

17.9587

3.92743

2.74970

.811

-3.7769

11.6318

High school degree-m2

m3

Mean Difference (IJ)

College degree-m4

-8.51563

3.34591

.105

-17.8907

.8594

Post-undergraduate degree-m5 Did not complete high school-m1

-17.03536(*)

5.46465

.019

-32.4016

-1.6691

18.78400(*)

3.01158

.000

10.3447

27.2233

High school degree-m2

12.44306(*)

3.01632

.000

3.9909

20.8952

Some college-m3

8.51563

3.34591

.105

-.8594

17.8907

Post-undergraduate degree-m5

-8.51973

5.60355

.749

-24.2704

7.2309

27.30373(*) 20.96279(*) 17.03536(*)

5.26659 5.26930 5.46465

.000 .001 .019

12.4846 6.1362 1.6691

42.1229 35.7894 32.4016

8.51973

5.60355

.749

-7.2309

24.2704

Did not complete high school-m1 High school degree-m2 Some college-m3 College degree-m4

m1  m 2 ϥϮϣί΁ έΎϬ̩ ήϫ ϪΠϴΘϧ m2 ϭ m1 ϞΜϣ Ζγ΍ ήΑ΍ήΑ Ϣϫ ήγ Ζθ̡ ϱΎϫ ϩϭή̳ έΩ Ϊϣ΁έΩ Ϧϴ̴ϧΎϴϣ ˺ m 2  m3 m3 ϭ m1 ϞΜϣ Ζδϴϧ ήΑ΍ήΑ ϪϠλΎϓ ΎΑ ϱΎϬϫϭή̳ έΩ ϲϟϭ m 4  m3

m 4  m5

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High school degree

(J) Level of education

95% Confidence Interval

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

(I) Level of education

Dunnett T3

Did not complete high school

m1 High school degree

Some college

m3 College degree

m4 Post-undergraduate degree

m5

Std. Error

Sig.

-6.34094

2.33139

Some college-m3

-10.26837(*)

College degree-m4 Post-undergraduate degree-m5

(J) Level of education

Upper Bound

Lower Bound

.064

-12.8721

.1902

2.74450

.002

-17.9585

-2.5782

-18.78400(*)

3.01158

.000

-27.2230

-10.3450

-27.30373(*)

5.26659

.000

-42.1194

-12.4881

Did not complete high school-m1 Some college-m3 College degree-m4

6.34094 -3.92743 -12.44306(*)

2.33139 2.74970 3.01632

.064 .810 .000

-.1902 -11.6317 -20.8950

12.8721 3.7768 -3.9911

Post-undergraduate degree-m5

-20.96279(*)

5.26930

.001

-35.7859

-6.1397

Did not complete high school-m1

High school degree-m2

10.26837(*)

2.74450

.002

2.5782

17.9585

High school degree-m2

3.92743

2.74970

.810

-3.7768

11.6317

College degree-m4

-8.51563

3.34591

.104

-17.8905

.8593

Post-undergraduate degree-m5 Did not complete high school-m1

-17.03536(*)

5.46465

.019

-32.3985

-1.6723

18.78400(*)

3.01158

.000

10.3450

27.2230

High school degree-m2

12.44306(*)

3.01632

.000

3.9911

20.8950

Some college-m3

8.51563

3.34591

.104

-.8593

17.8905

Post-undergraduate degree-m5

-8.51973

5.60355

.747

-24.2675

7.2280

27.30373(*) 20.96279(*) 17.03536(*)

5.26659 5.26930 5.46465

.000 .001 .019

12.4881 6.1397 1.6723

42.1194 35.7859 32.3985

8.51973

5.60355

.747

-7.2280

24.2675

Did not complete high school-m1 High school degree-m2 Some college-m3 College degree-m4

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m2

95% Confidence Interval

Mean Difference (IJ)

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Dunnett C

(I) Level of education

(J) Level of education

Did not complete high school

High school degree-m2

High school degree

Some college

m3 College degree

m4 Post-undergraduate degree

m5

Sig.

95% Confidence Interval Upper Upper Bound Bound

2.33139

-12.7076

.0257

-10.26837(*) -18.78400(*) -27.30373(*)

2.74450 3.01158 5.26659

-17.7647 -27.0099 -41.7380

-2.7720 -10.5581 -12.8695

6.34094

2.33139

-.0257

12.7076

Some college-m3 College degree-m4

-3.92743 -12.44306(*)

2.74970 3.01632

-11.4370 -20.6810

3.5821 -4.2051

Post-undergraduate degree-m5

-20.96279(*)

5.26930

-35.4039

-6.5217

10.26837(*) 3.92743

2.74450 2.74970

2.7720 -3.5821

17.7647 11.4370

-8.51563

3.34591

-17.6548

.6236

-17.03536(*)

5.46465

-32.0089

-2.0618

Did not complete high school-m1

18.78400(*)

3.01158

10.5581

27.0099

High school degree-m2 Some college-m3 Post-undergraduate degree-m5

12.44306(*) 8.51563 -8.51973

3.01632 3.34591 5.60355

4.2051 -.6236 -23.8715

20.6810 17.6548 6.8320

Did not complete high school-m1

27.30373(*)

5.26659

12.8695

41.7380

High school degree-m2

20.96279(*)

5.26930

6.5217

35.4039

Some college-m3

17.03536(*)

5.46465

2.0618

32.0089

8.51973

5.60355

-6.8320

23.8715

College degree-m4 Post-undergraduate degree-m5 Did not complete high school-m1

Did not complete high school-m1 High school degree-m2 College degree-m4 Post-undergraduate degree-m5

College degree-m4

‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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m2

Std. Error

-6.34094

Some college-m3

m1

Mean Difference (IJ)

* The mean difference is significant at the .05 level.

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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1

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(I) Level of education GamesHowell

Did not complete high school

m1 High school degree

m2

m3 College degree

m4 Post-undergraduate degree

m5

95% Confidence Interval Upper Upper Bound Bound

Std. Error

Sig.

-6.34094

2.33139

.051

-12.7040

.0221

Some college-m3

-10.26837(*)

2.74450

.002

-17.7602

-2.7766

College degree-m4

-18.78400(*) -27.30373(*)

3.01158 5.26659

.000 .000

-27.0053 -41.7298

-10.5627 -12.8776

6.34094

2.33139

.051

-.0221

12.7040

(J) Level of education High school degree-m2

Post-undergraduate degree-m5 Did not complete high school-m1 Some college-m3

-3.92743

2.74970

.609

-11.4330

3.5782

College degree-m4

-12.44306(*)

3.01632

.000

-20.6770

-4.2091

Post-undergraduate degree-m5

-20.96279(*)

5.26930

.001

-35.3961

-6.5295

Did not complete high school-m1 High school degree-m2 College degree-m4 Post-undergraduate degree-m5

10.26837(*) 3.92743 -8.51563 -17.03536(*)

2.74450 2.74970 3.34591 5.46465

.002 .609 .081 .016

2.7766 -3.5782 -17.6488 -31.9958

17.7602 11.4330 .6175 -2.0749

Did not complete high school-m1

18.78400(*)

3.01158

.000

10.5627

27.0053

High school degree-m2

12.44306(*)

3.01632

.000

4.2091

20.6770

Some college-m3

8.51563

3.34591

.081

-.6175

17.6488

Post-undergraduate degree-m5

-8.51973

5.60355

.550

-23.8554

6.8160

Did not complete high school-m1 High school degree-m2

27.30373(*) 20.96279(*)

5.26659 5.26930

.000 .001

12.8776 6.5295

41.7298 35.3961

Some college-m3

17.03536(*) 8.51973

5.46465 5.60355

.016 .550

2.0749 -6.8160

31.9958 23.8554

College degree-m4

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Some college

Mean Difference (IJ)

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

íì æ çç ± ºnÀa Ä ¸m

̵ήΘϣ΍έΎ̡Ύϧ ϥϮϣί΁ ί΍ ϩΩΎϔΘγ΍ Ϟ΋ϻΩ ΪηΎΒϧ ϝΎϣήϧ Ύϫ ϩΩ΍Ω ϊҨίϮΗ ΪηΎΑ ΩΎҨί ̶̳ΪϨ̯΍ή̡ ϑϼΘΧ΍ ΪηΎΑ Ϣ̯ ϪϧϮϤϧ ϩί΍Ϊϧ΍

ˮϢϴϨ̯ ̶Ϥϧ ϩΩΎϔΘγ΍ ̵ήΘϣ΍έΎ̡Ύϧ ϥϮϣί΁ ί΍ ϡίϻ ςҨ΍ήη ϦΘδϧ΍Ω ϥϭΪΑ ΍ΪΘΑ΍ ί΍ ΍ή̩ Ζγ΍ Ϣ̯ ΪηΎΑ ϝΎϣήϧ Ύϫ ϩΩ΍Ω ϊҨίϮΗ Ϫ̯ ̶τҨ΍ήη έΩ ϥϮϣί΁ ϦҨ΍ ϥ΍ϮΗ ΍ήҨί ήϔλ νήϓ Ωέ ̵΍ήΑ ϭ Ζγ΍ ̶ҨΎϋΩ΍ νήϓ ˬϞΑΎϘϣ νήϓ Ύϣ΍ ΪϨ̯ ̶ϣ κΨθϣ ΍έ ΖϴόϤΟ ϊҨίϮΗ ήϔλ νήϓ ϢϴϨ̯ Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ ̮Ҩ Ϫ΋΍έ΍ ΎΑ Ϫ̯ Ζγ΍ ϦҨ΍ ϥϮϣί΁ ϑΪϫ ϭ Ζγ΍ ϩΪη ΎϋΩ΍ Ωέ ήϔλ Ϫϴοήϓ Ϫ̯ ϢҨ΍ ϩΩ΍Ω ϥΎθϧ ϪϧϮϤϧ ̮Ҩ ΎΑ ςϘϓ ϥϮ̩ Ζγ΍ ΢ϴΤλ ϞΑΎϘϣ νήϓ ϢϴҨϮ̳ ̶Ϥϧ Ζϗϭ ̨ϴϫ Ζδϴϧ ϞΑΎϘϣ Ϫϴοήϓ ΕΎΒΛ΍ ϞϴϟΩ ϦҨ΍ ̶ϟϭ Ϊη ϥϮϣί΁ ̵΍ήΑ Ύϣ ̶Ϡλ΍ ϑΪϫ ΪҨ΂ϴϣ ΖγΪΑ ϪϧϮϤϧ ί΍ ϭ Ζγ΍ ήϔλ νήϓ Ωέ ̵΍ήΑ έ΍ΪϘϣ ϦҨήΘϤ̯ p  value ̵΍ήΑ Ζγ΍ ϩΪη ϡΎΠϧ΍ ̵΍ ϩΩϮϬϴΑέΎ̯ ϥϮϣί΁ ϦҨ΍ΎΑ Ϫϧή̳ϭ Ζγ΍ H 0 Ϫϴοήϓ Ωέ ϭ p  value ϥΪη ̮̩Ϯ̯ ΍έ κΧΎηϭ ϢҨήϴ̴Α ̶ϟϮϤόϣ ϪϧϮϤϧ ϢΒϨ̯ ΏΎΨΘϧ΍ ΖγέΩ ΍έ Ϫϴοήϓ ϝϭ΍ί΍ Ζγ΍ ήΘϬΑέΎ̰ϨҨ΍ ί΍ ̵ήϴ̳ϮϠΟ ΪϨ̯ ϞϤΤΗΪϧ΍ϮΘϴϣ ϖϘΤϣ Ϫ̯ Ζγ΍ ̶ҨΎτΧ Ϣϫ  ϢϴϨ̯ ΏΎΨΘϧ΍ ΍έ ϪϴϘΑ ϭ ϪϧϮϤϧ ϢΠΣΪόΑ ϢϴϨ̯ Ν΍ήΨΘγ΍

ϥϮϣί΁ ϥ΍ϮΗ ϢϴϤμΗ ϢҨ΍ ϩΩϮΑ ΎτΧ ϥϭΪΑ ΩϮη ςϠϏ Ϣϫ ϪϧϮϤϧ ϥϮϣί΁ ςγϮΗ ϭ ΪηΎΑ ςϠϏ ήϔλ νήϓ Ύ˱όϗ΍ϭ ή̳΍ ϢҨ΍ ϩΪη ΎτΧ έΎ̩Ω ΕέϮμϨҨ΍ ήϴϏ έΩ Ϫϧή̳ϭ ΖγέΩ  Ζγ΍ ϩΩϮΑ ΢ϴΤλ ΖϴόϤΟ έΩ ήϔλ νήϓ Ϫ̯ ̶σήη ϪΑ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ Ωέ ϝϭ΍ ωϮϧ ̵ΎτΧ  Ζγ΍ ϩΩϮΑ ςϠϏ ΖϴόϤΟ έΩ ήϔλ νήϓ Ϫ̯ ̶σήη ϪΑ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ ϝϮΒϗ ϡϭΩ ωϮϧ ̵ΎτΧ 1   ϥΎϨϴϤσ΍ ΢τγ 1   ϥϮϣί΁ ϥ΍ϮΗ H 0 Ωέ ϡΪϋ H 0 Ωέ  ϝϭ΍ ωϮϧ ̵ΎτΧ ϭ ̵έ΍Ω ̶Ϩόϣ ΢τγ 1     ϡϭΩ ωϮϧ ̵ΎτΧ ΢ϴΤλ H 0

ςϠϏ H 0  1  

ΪΘϓ΍ ̶ϣ ϥΎϨϴϤσ΍ ϪϠλΎϓ έΩ x ϥ΁ ̵ΎΗ ̂˾ Ϧϴ̴ϧΎϴϣ ϢҨήϴ̴Α ϪϧϮϤϧ ˺˹˹ ή̳΍ ̶ϨόҨ ̂˾ ϥΎϨϴϤσ΍ ΢τγ έΩ ϭ ˹ ˹˺ ̶̰ηΰ̡ έΩΪϨηΎΑ ϪΘη΍Ω ̵ήΘϤ̯ ϝϭ΍ ωϮϧ ̵ΎτΧ Ϫ̯ ϢϴϨ̯ ϩΩΎϔΘγ΍ ΪҨΎΑ ̶ҨΎϬϧϮϣί΁ ί΍ ˹ ˹˾ ̶ΘόϨλ ϥ΁ ϥΩϮΑ ςϠϏ ρήη ϪΑ ήϔλ νήϓ Ωέ ̶ϨόҨ ΪηΎΑ ϦҨήΘθϴΑ ϥ΁ 1   Ϫ̯ Ζγ΍ ̶ϧϮϣί΁ ϥϮϣί΁ ϦҨήΗ ϥ΍ϮΗή̡ Ŷǀƴĩ ƢǀƤŰţ     1 ƶƐŝřŹ ƵŹŚŝ Źŵ ƩřƺŘſ 45 www.ircdvd.com

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Ωή̯ ϩΩΎϔΘγ΍ Ωέ΍ΪϧΎΘγ΍ ήϴϐΘϣ ί΍ ϥ΍ϮΘϴϣ ΪϨηΎΒϧ ΪΣ΍ϭ Ϣϫ ήϴϐΘϣ ϭΩ ˬϪδҨΎϘϣ ΖϬΟ ή̳΍ ϪΘ̰ϧ ϥ΁ ΝήΨϣ ϭ ήδ̯ ΕέϮλ ΪΣ΍ϭ s ήΑ ϢϴδϘΗ ΎΑ ϭ ΩϮη ̶ϣ ήϔλ ̶ϨΤϨϣ ΍ΪΒϣ ί΍ νήϋ x  x ϪΒγΎΤϣΎΑ xx z  ΩϮΑ Ϊϫ΍ϮΧ ΪΣ΍ϭ ̶Α z ΖҨΎϬϧ έΩ ϭ ϩΪη ̶̰Ҩ s ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ COMPUTE έϮΘγΩ ί΍ SPSS έ΍ΰϓ΍ ϡήϧ έΩ z ϪΒγΎΤϣ ̵΍ήΑ

įźŤƯřŹŚěŚƳ įŚƷ ƱƺƯŻō

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Ʊřźƿř żƿř ƺŤǀŤƀƳř

ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ήҨί έϮΘγΩ ί΍ SPSS έΩ ̵ήΘϣ΍έΎ̡Ύϧ ̵Ύϫ ϥϮϣί΁ ϡΎΠϧ΍ ̵΍ήΑ Analyze – Nonparametric Tests – 1-sample k-s…

Ϫ̯ ΪϫΪϴϣ ϥΎθϧ Kolmogorov- Smirnov Z ϑϮϧήϴϤγ΍ ± ϑϭή̳ϮϤϟϮ̯ εϭέ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϥϮϣί΁ ϦҨ΍ ̶ҨΎϤϧ ΎҨ ϥϮγ΍Ϯ̡ ˬΖΧ΍ϮϨ̰Ҩ ˬϝΎϣήϧ ˮΖγ΍ ̶ϋϮϧ Ϫ̩ ϩΪη ΏΎΨΘϧ΍ ήϴϐΘϣ ϊҨίϮΗ ϪΒΗέ ϑϼΘΧ΍ ϭ Ωέϭ΁ ̶ϣ ΖγΪΑ ΍έ ϪϧΎϴϣ ˬΪϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ϪϧΎϴϣ ϭ Ύϫ ϩΩ΍Ω ̵ΪϨΑ ϪΒΗέ ί΍ ̵ήΘϣ΍έΎ̡Ύϧ ϥϮϣί΁ Most έΩ ϥϮϣί΁ ϪΠϴΘϧ έΩ ΪηΎΑ ϪΘη΍Ω ϑϼΘΧ΍ ̶ϠϴΧ ή̳΍ ϭϭ ΪϨ̯ ̶ϣ ϪΒγΎΤϣ Ύϫ ϩΩ΍Ω ΎΑ ΍έ ϪϧΎϴϣ ϑϼΘΧ΍ έ΍ΪϘϣ ϭΩ ϦҨήΘ̳έΰΑ ϖϠτϣ έ΍ΪϘϣ Absolute ϥ΍ϮϨϋ ΎΑ ϑϼΘΧ΍ έ΍ΪϘϣ Ϧϴϟϭ΍ ΪδҨϮϧ ̶ϣ Extreme Ωέ΍Ω ϡίϻ ΪηΎΑ ̶ϣ ϥ΁ ήҨί έΩ ϩΪη ̟Ύ̩

ήҨί έϮΘγΩ ί΍ΪηΎΒϧ ϝΎϣήϧ ϊҨίϮΗΎҨ Ϣ̯ ϪϧϮϤϧ Ω΍ΪόΗ ̶Θϗϭ ϪϧϮϤϧ ϭΩ ϦϴΑ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί΁ ̵΍ήΑ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍

Analyze – Nonparametric Tests – 2 Independent samples

ϥϮϣί΁ ω΍Ϯϧ΍ Kolmogorov- Smirnov Z ϑϮϧήϴϤγ΍ ± ϑϭή̳ϮϤϟϮ̯ ˺ έΩ ϪδҨΎϘϣ ̵΍ήΑ ΰϴϧ ΍έ ϊҨίϮΗ Ϟ̰η ̵ΰ̯ήϣ κΧΎη ήΑ ϩϭϼϋ ΪηΎΒϧ ϪϴΒη ϪϧϮϤϧ ϭΩ ήϫ ϊҨίϮΗ Ϫ̯ ̶ΗέϮλέΩ Ωήϴ̳ ̶ϣ ήψϧ Ζγ΍ ήΘΒγΎϨϣ ϩέΎϣ΁ ϦҨ΍

Man-Whitney U ̶ϨΘҨϭ Ϧϣ ˻ ΎΑ ϭ Ζγ΍ ΐγΎϨϣ ̵ΰ̯ήϣ ̵Ύϫ κΧΎη ϪδҨΎϘϣ ̵΍ήΑ ΪϨ̯ ̶ϣ ΏΎδΣ ΍έ ϩέΎϣ΁ Ύϫ ϪΒΗέ ϊϤΟ ί΍ ϩΩΎϔΘγ΍

Moses extreme reactions αίϮϣ ˼ ϩϭή̳ ϦϴΑ ϪδҨΎϘϣ ϭ Ωέ΍Ω Ϊϴ̯ΎΗ ΎϬϧ΁ ϪΒΗέ ϭ ̶ҨΎϬΘϧ΍ έ΍ΪϘϣ ήΑ ΪϫΩ ̶ϣ ϡΎΠϧ΍ ̶ҨΎϬΘϧ΍ έ΍ΪϘϣ ̵ϭέ ΍έ ζҨΎϣί΁ ϭ ΪϫΎη

Wald wolfowitz runs Ϊϟ΍ϭ ̊ ΪϨ̯ ̶ϣ ϪδҨΎϘϣ ΍έ ϊҨίϮΗ Ϟ̰η ϑϭή̳ϮϤϟϮ̯ ΪϨϧΎϣ ϭ ΪϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ρϮϠΨϣ ̵Ύϫ ϪΒΗέ ί΍

ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ήҨί έϮΘγΩ ί΍ ϩϭή̳ ΪϨ̩ ϦϴΑ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί΁ ̵΍ήΑ

Analyze – Nonparametric Tests – K Independent samples

Ύϫ ϪϧϮϤϧ ή̳΍ ̶ϟϭ ΪϧΩϮΑ ϞϘΘδϣ Ύϫ ϪϧϮϤϧ ΎΠϧ΁ έΩ ̶ϟϭ ΩϮΑ Ϣϫ ϝΎϣήϧ ΖϟΎΣ έΩ Ύϫ ϥϮϣί΁ ϦҨ΍ ϪΑΎθϣ ϞϘΘδϣ ̵Ύϫ ϪϧϮϤϧ ήϔϧ ϭΩ ̵΍ήΑ ϭέ΍Ω ήϴΛΎΗ ζҨΎϣί΁ ˱ϼΜϣ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ήҨί Ε΍έϮΘγΩ ί΍ ΪϨηΎΑ ςΒΗήϣ ΪϧήҨά̡ ̶ϣ ήϴΛΎΗ Ϣϫ ί΍ ϭ Ζγ΍ ςΒΗήϣ ϭέ΍Ω ί΍ ΪόΑ ϭ ϞΒϗ ήϔϧ ̮Ҩ ̵΍ήΑ Ύϣ΍ Ζγ΍

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Ύϫ ΖϔΟ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί΁

Analyze – Nonparametric Tests –2 Related samples ϥϮϣί΁ ω΍Ϯϧ΍ Ύϫ ϩΩ΍Ω Ϫ̯ Ζγ΍ ̶Θϗϭ Wilcoxon ˺ ϥϮϣί΁ ϦҨ΍ έΩ ϭ ϥΎϣί ϞΜϣ ΪϨηΎΑ ϪΘγϮϴ̡ Ύϫ ϪΒΗέ ϑϼΘΧ΍ ΖϬΟ ϭ Ύϫ ϪΒΗέ ϑϼΘΧ΍ ϦҨ΍ ί΍ ϩΩΎϔΘγ΍ ΩϮη ̶ϣ ϪΘϓή̳ ήψϧέΩ Ζγ΍ ΢Οέ΍ ϥϮϣί΁ Ζ˰ϬΟ ς˰Ϙϓ ̶˰ϟϭ Ζγ΍ ̶ҨϻΎΑ ϞΜϣ Sign ˻ ΩϮη ̶ϣ ϪΘϓή̳ ήψϧ έΩ Ύϫ ϪΒΗέ ϑϼΘΧ΍ Ζ˰γέΩ ΕέϮ˰λ ϪΑ Ύϫ ϩΩ΍Ωή̳΍ McNemar ˼ Ϧ˰˰Ҩ΍ ί΍ Ζ˰˰γ΍ ή˰˰ΘϬΑ ˺ϭ˹ ΪϨΘδ˰˰ϫ ς˰˰ϠϏ ϭ ϥϮϣί΁ Ϧϳ΍ ί΍ ϻϮϤόϣ ΪϴϨϛ ϩΩΎϔΘγ΍ ϥϮϣί΁ ΩϮηϲϣ ϪΘϓή̳ έΎϛ ϪΑ έΎΘϓέ Ϛϳ ί΍ ΪόΑ ϭ ϞΒϗ ϪδϳΎϘϣ ϱ΍ήΑ

ϩϭή˰̳ ˬΎ˰ϫϩΩ΍Ω Ϊ˰ϴϨϛ ϩΩΎϔΘ˰γ΍ εϭέ Ϧ˰ϳ΍ ί΍ McNemar εϭέ ί΍ ϲ˰ϤϴϤόΗ ΪϨηΎΑ ϲϳΎΗΪϨ̩ ϱΎϫ ϩΩ΍Ω ή̳΍ ΪϨΘδϫ ϱΪϨΑ

ϲϳΎΗΪϨ̩ ϱΎϫϪϧϮϤϧ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ϥϮϣί΁

Analyze – Nonparametric Tests – K Related samples

ϥϮϣί΁ ω΍Ϯϧ΍

ϱήΘϣ΍έΎ̡ ϱΎϬηϭέ ΪϨϧΎϣ ϦϣΪϳήϓ ϥϮϣί΁ ˺ ΍έ ήϴϐΘϣ ΪϨ̩ ϊϳίϮΗ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ϱέ΍ά̳ ϪΒΗέ ί΍ ϩΩΎϔΘγ΍ ΎΑ ΪϨϛ ϲϣ ϥϮϣί΁ ϥϮϣί΁ Ύϫ ϪΒΗέ ϪδϳΎϘϣ ϭ ϩϭή̳ ήϫ ϱ΍ήΑ ΩϮη ϲϣ ϡΎΠϧ΍ ϖϓ΍ϮΗ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϝ΍ΪϨϛ ϥϮϣί΁ ˻ ϡΎΠϧ΍ ΍έ ϥϮϣί΁ Ϧϳ΍ ϱ΍ ϪΒΗέ ϲ̴ΘδΒϤϫ

ΪϫΩϲϣ

ϦϣΪϳήϓ ϥϮϣί΁ ΪϨϧΎϣ ΰϴϧ ϥ΍ήϛΎϛ ϥϮϣί΁ ˼ ˺ϭ˹ ϲϳΎΗϭΩ ϱΎϫ ϩΩ΍Ω ΎΑ ϲϟϭ ϩΩήϛ ϞϤϋ ϪδϳΎϘϣ ϱ΍ήΑ ΪϫΩ ϲϣ ϡΎΠϧ΍ ΍έ ΕΎΒγΎΤϣ Ζγ΍ ΐγΎϨϣ ςϠϏ ϭ ΖγέΩ

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ΖΒΛ ϪϴϧΎΛ ΐδΣ ήΑ Ϊϧέ΍ά̴Α ΩϮΧ ̵ΎΟ έΩ ΍έ ΐγΎϨϣ Ϫότϗ ΎΗ ΪϨϨ̰ϴϣ ϑήλ Ϫ̪Α ˺˹ Ϫ̯ ΍έ ̶ϧΎϣί ϝΎΜϣ ϢҨ΍ ϩΩή̯ ΪϴҨΎϤϧ ϪΒγΎΤϣ ΍έ ˯Ύϴη΍ κϴΨθΗ ϥΎϣί ̵΍ήΑ ̶ϔϴλϮΗ ̵ΎϫέΎϣ΁ ˺ ŦƬŨƯ Ƶźƿřŵ ƖŝźƯ ̶̳ΪϨ̯΍ή̡ ϭ ΰ̯ήϤΗ ̵ΎϫέΎϴόϣ

ççå èåå çëå ΪϴϨ̯ Ϣγέ ϥΎϣί Ϫγ ϦҨ΍ ̵΍ήΑ ̵΍ ϪδҨΎϘϣ έ΍ΩϮϤϧ ̮Ҩ ˻ çêå çîå èåå ˮΖγ΍ ϝΎϣήϧ ϊҨίϮΗ ̵΍έ΍Ω Ϟ̰η Ϫγ ήϫ ̵΍ήΑ κϴΨθΗ ϥΎϣί ΎҨ΁ ˼ ϥΎϣί Ϧϴ̴ϧΎϴϣ ϦϴΑ ΪϴϫΩ ϥΎθϧ Boxplot έ΍ΩϮϤϧ ί΍ ϩΩΎϔΘγ΍ ΎΑ ˽ çëå çíå çîå Ωέ΍Ω ΩϮΟϭ ̵έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ϩϭή̳ Ϫγ ϦҨ΍ έΩ κϴΨθΗ çèå èéå æîå ϭ ̵ήΘϣ΍έΎ̡ ΪϴϨ̯ ϥϮϣί΁ ΍έ ϻΎΑ νήϓ ̵έΎϣ΁ ̵Ύϫ ϥϮϣί΁ ί΍ ϩΩΎϔΘγ΍ ΎΑ ̋ æîå èåå çêå ̵ήΘϣ΍έΎ̡Ύϧ

ççå çìå çéå

çêå èçå çìå

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Ώ΍ϮΟ Ωέ΍Ω ΩϮΟϭ ϩ΍έ ϭΩ ΎϫήϴϐΘϣ ΖϴϴόΗ ϭ Ύϫ ϩΩ΍Ω ΖΒΛ ̵΍ήΑ έΩ Ϫ̯ ϥΎϣί ̶ϣϭΩ ϭ ΚϠΜϣ ˬϩήҨ΍Ω ˬϊΑήϣ ϞϣΎη Ϟ̰η ήϴϐΘϣ ̶ϟϭ΍ ϢҨήϴ̴Α ήψϧ έΩ ήϴϐΘϣ ϭΩ ϝϭ΍ ΖϟΎΣ ϢҨέ΍Ω ΩέϮϣ ˼˹ ΕέϮμϨҨ΍ ϢҨέ΍Ω ΩέϮϣ ˺˹ ΖϟΎΣ ϦҨ΍ έΩ Ϫ̯ ΚϠΜϣ ˬϩήҨ΍Ω ˬ ϊΑήϣ ϢҨήϴ̴Α ήψϧ έΩ ήϴϐΘϣ Ϫγ ϡϭΩ ΖϟΎΣ

Ωή̯ ϩΩΎϔΘγ΍ ϥ΍ϮΗ ̶ϣ ϩ΍έϭΩ ί΍ ̶ϔϴλϮΗ ̵ΎϫέΎϣ΁ ̵΍ήΑ ˺ Analyze – Descriptive statistic- descriptive Analyze – Descriptive statistic- Explore objects ΖϤδϗ Ϫγ έΩ ΍έ ϥΎϣί ήϴϐΘϣ ϥ΍ϮΘϴϣ Explore ί΍ ϩΩΎϔΘγ΍ ΎΑ Ϫ̯ Ζγ΍ ϦҨ΍ ϩ΍έϭΩ ϦҨ΍ ϕήϓ ΩϮϤϧ ϩΪϫΎθϣ ϪϧΎ̳΍ΪΟ

Descriptive Statistics N

Range

Minimum

Maximum

Mean

Std. Deviation

Variance

square

10

110.00

190.00

300.00

256.0000

29.88868

893.333

triangle

10

90.00

190.00

280.00

241.0000

26.85351

721.111

circle

10

70.00

270.00

340.00

303.0000

23.59378

556.667

Valid N (listwise)

10

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:̵΍ ϪδҨΎϘϣ έ΍ΩϮϤϧ ˻ Graph – Legacy Dialogs- Bar – simple - Define –other statistic- variable ……time Category Axis……objects 400.00

Mean time

300.00

200.00

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100.00

0.00 circle

‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

square

triangle

ϝΎϣήϧ ϊҨίϮΗ ΩϮΟϭ ˼

Graph – Legacy Dialogs - Histogram-display normal curve-

variable…...time Panel…..….objects

3 4

square

2

1 3

Frequency

3 2

objects circle

Frequency

0

2

1

1

0 3 Mean =267.24 Std. Dev. =37.881 N =29

triangle

2

0 150.00

200.00

250.00

300.00

350.00

time 1

0 150.00

200.00

250.00

300.00

time

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Analyze – Nonparametric Tests – 1-sample k-s…

̵ΪϨΑ ϩϭή̳ ϥϭΪΑ ϝϭ΍ ΖϟΎΣ ϒϟ΍

One-Sample Kolmogorov-Smirnov Test time N

29

Normal Parameters(a,b) Most Extreme Differences

Mean

267.2414

Std. Deviation

37.88107

Absolute

.090

Positive

.090

Negative

-.083

Kolmogorov-Smirnov Z

.485

Asymp. Sig. (2-tailed)

.973

a Test distribution is Normal.b Calculated from data.

ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ 



Data- split files – variable ….objects Analyze – Nonparametric Tests – 1-sample k-s…

̵ΪϨΑ ϩϭή̳ ΎΑ ϡϭΩ ΖϟΎΣ Ώ

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Sig = p  value  0.973 ! 0.05 =

ϪΠϴΘϧ ί΍ p  value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˺

One-Sample Kolmogorov-Smirnov Test objects square

time N Normal Parameters(a,b) Most Extreme Differences

circle

256.6667

Std. Deviation

31.62278

Absolute

.194

Positive

.125

Negative

-.194

Kolmogorov-Smirnov Z

.655

Asymp. Sig. (2-tailed)

.784

N Normal Parameters(a,b) Most Extreme Differences

Triangle

10 Mean

10 Mean

303.0000

Std. Deviation

23.59378

Absolute

.251

Positive

.251

Negative

-.142

Kolmogorov-Smirnov Z

.792

Asymp. Sig. (2-tailed)

.556

N Normal Parameters(a,b) Most Extreme Differences

10 Mean

241.0000

Std. Deviation

26.85351

Absolute

.131

Positive

.083

Negative

-.131

Kolmogorov-Smirnov Z

.415

Asymp. Sig. (2-tailed)

.995

a Test distribution is Normal. b Calculated from data.

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ϊΑήϣ ϩϭή̳ έΩ ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ  Sig = p  value  0.784 ! 0.05 =

ϪΠϴΘϧ ί΍ p  value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˺



ϩήҨ΍Ω ϩϭή̳ έΩ ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ  ί΍ p  value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˻ Sig = p  value  0.556 ! 0.05 =



ΚϠΜϣ ϩϭή̳ έΩ ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ  ί΍ p  value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˼ Sig = p  value  0.995 ! 0.05 =



Boxplot έ΍ΩϮϤϧ ΎΑ ϩϭή̳ Ϫγ έΩ ϥΎϣί Ϧϴ̴ϧΎϴϣ ϑϼΘΧ΍ ˽ ΪηΎΒϧ split files ΖϟΎΣ έΩ ϪΘ̰ϧ

Graph – Legacy Dialogs Boxplots - variable……...time

350.00

time

300.00

250.00

200.00 5

150.00 square

circle

triangle

objects

̶ϓΎ̯ έ΍ΩϮϤϧ ΎΑ ΕϭΎπϗ ̶ϟϭ ΪϫΪϴϣ ϥΎθϧ ΕϭΎϔΘϣ ϝΎ̰η΍ ϩϭή̳ Ϫγ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ϪΠϴΘϧ Ωή̯ ϥϮϣί΁ ΪҨΎΑ Ζδϴϧ

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category…….objects

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ϥϮϣί΁ ˾

̵ήΘϣ΍έΎ̡Ύϧ ϝϭ΍ ΖϟΎΣ

H 0 : m1  m 2  m 3

Square ϊΑήϣ ϩϭή̳ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1 H 1 : m1  m 2  m 3 circle ϩήҨ΍Ω ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2 Triangle ΚϠΜϣ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m3

Analyze – Nonparametric Tests – K Independent samples- test variable………..... time

Grouping variable….…object(1,3)

Kruskal-Wallis Test Ranks objects square

N

Mean Rank 10

13.20

Chi-Square

circle

10

24.40

df

triangle

10

8.90

Total

30

time 16.683 2

Asymp. Sig.

.000

a Kruskal Wallis Test b Grouping Variable: objects

ϪΠϴΘϧ ΩϮη ̶ϣ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ  ί΍ p  value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˺ Sig = p  value  0.000 0.05 =

 ̵ήΘϣ΍έΎ̡ ϡϭΩ ΖϟΎΣ

Analyze -

Compare Means - One-Way Anova ANOVA

time

Between Groups

Sum of Squares 20720.000

Within Groups Total

df 2

Mean Square 10360.000

19510.000

27

722.593

40230.000

29

F 14.337

Sig. .000

Test of Homogeneity of Variances time Levene Statistic .067

df1

df2 2

Sig. 27

.936

ϪΠϴΘϧ ΩϮη ̶ϣ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ  ί΍ p  value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˺ Sig = p  value  0.000 0.05 =



Ζγ΍Ύϴη΍ ̵ΪϨΑ ϩϭή̳ ί΍ ̶ηΎϧ ϭ Ζγ΍ έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ ˻ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ Test of Homogeneity of Variances ϝϭΪΟ έΩ ΎϬδϧΎҨέ΍ϭ ϪδҨΎϘϣ ϥϮϟ ϥϮϣί΁ ί΍ ϩΪϣ΁ ΖγΪΑ ΞҨΎΘϧ ˼ p  value  0.936 = Sig !  ϭ Ϊϧέ΍Ϊϧ ̵έ΍Ω ̶Ϩόϣ ϑϼΘΧ΍ Ύϴη΍ήϴϐΘϣ ϩϭή̳ ˼ έΩ ϥΎϣί ήϴϐΘϣ ̵Ύϫ βϧΎҨέ΍ϭ ̵ήΑ΍ήΑ νήϓ ΎΑ ΎϬϨϴ̴ϧΎϴϣ ΎϬϓϼΘΧ΍ ϝΎΒϧΩ ϪΑ ΪҨΎΑ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ANOVA ϝϭΪΟ έΩ βϧΎҨέ΍ϭ ΰϴϟΎϧ΁ ί΍ ϩΪϣ΁ ΖγΪΑ ΞҨΎΘϧ ̊ ϢϴϨ̯ ϪδҨΎϘϣ Ϣϫ ΎΑ ϭΩ ϪΑϭΩ Post HOC ϪϨҨΰ̳ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϭ ϢϴηΎΑ ΎϬδϧΎҨέ΍ϭ

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time

Test Statistics(a,b)

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ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ postHoc έϮΘγΩ ί΍ ϑϼΘΧ΍ ΖϠϋ ϦΘϓΎҨ ̵΍ήΑ

Multiple Comparisons Dependent Variable: time Tukey HSD

(I) objects square circle triangle

(J) objects circle triangle square triangle square circle

Mean Difference (I-J) -46.00000* 16.00000 46.00000* 62.00000* -16.00000 -62.00000*

Std. Error 12.02159 12.02159 12.02159 12.02159 12.02159 12.02159

Sig. .002 .391 .002 .000 .391 .000

95% Confidence Interval Lower Bound Upper Bound -75.8065 -16.1935 -13.8065 45.8065 16.1935 75.8065 32.1935 91.8065 -45.8065 13.8065 -91.8065 -32.1935

.

m1  m3 m1  m2 m2  m3

ϭΩ ϪΑ ϭΩ ϪδҨΎϘϣ ϥϮϣί΁ ϪΠϴΘϧ Ζδϴϧ ήΑ΍ήΑ ϩήҨ΍Ω ϭ ϊΑήϣ ̵Ύϫ ϩϭή̳ έΩ ϥΎϣί Ϧϴ̴ϧΎϴϣ ˺ Ζδϴϧ ήΑ΍ήΑ ϩήҨ΍Ω ϭ ΚϠΜϣ ̵Ύϫ ϩϭή̳ έΩ ϥΎϣί Ϧϴ̴ϧΎϴϣ ˻ Ζγ΍ ήΑ΍ήΑ ΚϠΜϣ ϭ ϊΑήϣ ̵Ύϫ ϩϭή̳ έΩ ςϘϓ ϥΎϣί Ϧϴ̴ϧΎϴϣ ˼ ΪηΎΑ ̶ϣ ϩήҨ΍Ω ϩϭή̳ ϑϼΘΧ΍ ΖϠϋ ̊ Square ϊΑήϣ ϩϭή̳ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1 circle ϩήҨ΍Ω ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2 Triangle ΚϠΜϣ ϩϭή̳ έΩ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m3

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*. The mean difference is significant at the .05 level.

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íì æ çî ± º Ä ¸m  2 Chi-square ƱƺƯŻō

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Ωϭήϴϣ έΎ̯ ϪΑ έϮψϨϣ ϭΩ ̵΍ήΑ ϥϮϣί΁ ϦҨ΍ ˮϪϧ ΎҨ ΪϨ̯ ̶ϣ ̵ϭήϴ̡ ̶λΎΧ ϊҨίϮΗ ί΍ έ΍ΩϮϤϧ ϊҨίϮΗ ϖΑΎτΗ ˺ ϪΘδδ̳ ̵Ύϫ ήϴϐΘϣ ̶ϓΩΎμΗ ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍ ˻

ϩΪη ϩΪϫΎθϣ ήҨΩΎϘϣ Oi έΎψΘϧ΍ ΩέϮϣ ήҨΩΎϘϣ Ei 2 ( O E )  i έΎψΘϧ΍ ΩέϮϣ ήҨΩΎϘϣ ϪϧϮϤϧ ̵Ϯγ ί΍ ϩΪη ϩΪϫΎθϣ ϑϼΘΧ΍ ςγϮΘϣ

2   i

Ei

H 0 : ΪϨ̯ ̶ϣ ΪϴҨΎΗ ΍έ ΖϴόϤΟ ήΘϣ΍έΎ̡ ϪϧϮϤϧ H 1 : ΪϨ̯ ̶Ϥϧ ΪϴҨΎΗ ΍έ ΖϴόϤΟ ήΘϣ΍έΎ̡ ϪϧϮϤϧ

2 ΖϴόϤΟ ϪϧϮϤϧ  έ΍ΪϘϣ ϥΩϮΑ Ϣ̯ ϭ ΩϮη ̶ϣ Ϣ̯ ΕΎϓϼΘΧ΍ β̡ Ζη΍Ω ΍έ ΖϴόϤΟ ϊҨίϮΗ ϥΎϤϫ ϪϧϮϤϧ ή̳΍ ΩϮη ̶ϣ ϪδҨΎϘϣ ̵έΎϣ΁ ϝϭΪΟ ΎΑ ϥ΁ έ΍ΪϘϣ ϪΘΒϟ΍ ΪϨ̯ ̶ϣ ΪϴҨΎΗ ΍έ

εί΍ήΑ ϲϳϮϜϴϧ ± ϝϼϘΘγ΍ ϥϮϣί΁ ΎϬοήϓ ϲϓΩΎμΗ ϪϧϮϤϧ ± Ωέ΍Ϊϧ ΖϴϤϫ΍ Ύϫ ϩΩ΍Ω ϊϳίϮΗ Ύϳ ϞϜη ± ˺ Ϟϗ΍ΪΣ ϩϭή̳ ήϫ έΩ ϲϧ΍ϭ΍ήϓ Ϟϗ΍ΪΣ ± ΪϨηΎΑ ϪΘη΍Ω ˾ ί΍ ζϴΑ ϲϧ΍ϭ΍ήϓ Ύϫ ϩϭή̳ ˻˹ ±

ΪϨϛϲϣ ϪδϳΎϘϣ έΎψΘϧ΍ ΩέϮϣ ήϳΩΎϘϣ ΎΑ ΍έ ϩΪη ϩΪ

ϫΎθϣ ήϳΩΎϘϣ ˻ ϲΧ ϩέΎϣ΁ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϥϮϣί΁ Ϧϳ΍ ϥϮϣί΁ ϪΑ ϲγήΘγΩ εϭέ - Descriptive Statistics - Crosstabs... ... Analyze

ΪϴϨϛ ΏΎΨΘϧ΍ ΍έ chi square ϪϨϳΰ̳ ϭ ΪϴϧΰΑ ΍έ statistic ϪϤϛΩ ΎϫήϴϐΘϣ ϦϴϴόΗί΍ΪόΑ

Analyze – nonparametric – chi-square ΎҨ

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˾ Ζγ΍ ήΑ΍ήΑ ̵ή̴ҨΩ ΎΑ Ύϫ ϩΩ΍Ω ί΍ ̮Ҩ ήϫ ϩΪϫΎθϣ ϝΎϤΘΣ΍ ϭ Ζγ΍ ΖΧ΍ϮϨ̰Ҩ ϊҨίϮΗ ΪϴϨ̯ νήϓ ϝΎΜϣ ϢҨέ΍Ω ̵ίΎΑ ΏΎΒγ΍ ϩϭή̳ ˮϪϧ ΎҨ Ζγ΍ ϥΎδ̰Ҩ ΎϬϨҨ΍ ϥΪҨήΧ ϪΑ Ύϫ Ϫ̪Α ϞҨΎϤΗ Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΧ ̶ϣ ϝϭ΍ ϝ΍ϮΌγ ΩϮη ϡΎΠϧ΍ ϝϭ΍ ϩϭή̳ Ϫγ ϦϴΑ ϪδҨΎϘϣ Ϣϴϫ΍ϮΧ ̶ϣ ± ϡϭΩ ϝ΍ϮΌγ ˮΖγ΍ ϡϮγ ϭΩ ϪΑ ϡϮγ ̮Ҩ ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ ϭ ̲ϨϔΗ ϦϴΑ ΪҨήΧ ΖΒδϧ ΎҨ΁ ϡϮγ ϝ΍ϮΌγ

̵ίΎΑ ΏΎΒγ΍ ωϮϧ ϩΪη ̵έ΍ΪҨήΧ έ΍ΪϘϣ

̲ϨϔΗ

ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ

̮γϭήϋ

ϦϴηΎϣ

ϪΧή̩ Ϫγ

˽˾

˻˹

˺˾

˺˾

˾

typeoftoy Observed N

Expected N

gun

45

20.0

25.0

camputer

20

20.0

.0

doll

15

20.0

-5.0

car

15

20.0

-5.0

5

20.0

-15.0

bycicle Total

Residual

2  

(Oi  Ei ) Ei



252  0 2  52  52  152 900   45 20 20

100

Ζγ΍ 0.05 ί΍ ήΘ̰̩Ϯ̯ ϭ Asymp. Sig. = 0.000 έ΍ΪϘϣ ̌ Ζδϴϧ ̶̰Ҩ ΖϴόϤΟ ήΘϣ΍έΎ̡ ΎΑ ϪϧϮϤϧ ϊҨίϮΗ ϭ ΪϨ̯ ̶ϣ Ωέ ήϔλ νήϓ ϪϧϮϤϧ ΍άϟ Test Statistics

Chi-Square(a)

typeoftoy 45.000

df

4

Asymp. Sig.

.000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 20.0.

ϡϭΩ Ώ΍ϮΟ

̵΍ήΑ ˬ Used specified range ϪϨҨΰ̳ ˬ Expected Range ΖϤδϗ έΩ Chi-Square ϥϮϣί΁ ϩήΠϨ̡ έΩ Ωήϴ̳ ΕέϮλ ˼ ΎΗ ˺ ϩϭή̳ ϦϴΑ ϪδҨΎϘϣ ΎΗ ϩΩή̯ ΏΎΨΘϧ΍ ΍έ upper=3 ̵΍ήΑ ϭ lower=1 ϢϴϨ̯ ϥΎθΒΗήϣ ϝϭ΍ ̶ΘδҨΎΑ ΩϮΒϧ ΐΗήϣ Ύϣ ήτϧ ΩέϮϣ ̵ΎϬϫϭή̳ ή̳΍ Ω΍Ω ϡΎΠϧ΍ ΍έ ϥϮϣί΁ ΪόΑ Ωή̯ ΏΎΨΘϧ΍ ϝϭ΍ select cases έϮΘγΩ ΎΑ ΍έΎϫ ϩΩ΍Ω ϥ΍ϮΘϴϣ Ϊη Ωέ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ ϥΎϨ̪Ϥϫ ϥϮϣί΁ ϦҨ΍ ϡΎΠϧ΍ ΎΑ

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ϝϭ΍ Ώ΍ϮΟ ϢϴϨ̯ ̶ϣ ϒҨήόΗ num ϭ Typeoftoy ϡΎϧ ϪΑ ήϴϐΘϣ ωϮϧ ϭΩ ˺ ϢϴϫΩ ̶ϣ Ϊ̯ ̵ίΎΑ ΏΎΒγ΍ ωϮϧ ̋ έΩ ΍έ ϝϭ΍ ήϴϐΘϣ ˻ ϢϴϫΩ ̶ϣ ϥίϭ num ήϴϐΘϣ ϪΑ ˼ Analyze – nonparametric – chi-square – Test variable ………Typeoftoy ̊ Ζγ΍ ϩΪη ̊̋ ΩΪϋ Chi-Square(a) έ΍ΪϘϣ ̋

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Frequencies typeoftoy Observed N

Expected N

1

gun

Category

45

26.7

2

camputer

20

26.7

-6.7

3

doll

15

26.7

-11.7

Total

Residual 18.3

80

Test Statistics typeoftoy ChiSquare(a) df

19.375 2

Asymp. Sig.

ϡϮγ Ώ΍ϮΟ ̌̋ ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ ϭ ̲ϨϔΗ ΪҨήΧ ωϮϤΠϣ Ϫ̯ ϢϴϨ̯ ̶ϣ ΏΎδΣ έϮτϨҨ΍ ΍έ ϡϮγ ϭΩ ϪΑ ϡϮγ ̮Ҩ ΖΒδϧ ϢϴϨ̯ ̶ϣ ϢϴδϘΗ ϡϮγ ϭΩ ϪΑ ϡϮγ ̮Ҩ ΖΒδϧ ϪΑ ΍έ ΩΪϋ ϦҨ΍ Ζγ΍ 1 65*  21.7 ˬ Expected Range ΖϤδϗ έΩ Chi-Square ϥϮϣί΁ ϩήΠϨ̡ έΩ 3 ̵΍ήΑ ϭ lower=1 ̵΍ήΑ ˬ Used specified range ϪϨҨΰ̳ 2 Ωήϴ̳ ΕέϮλ ˻, ˺ ϩϭή̳ ϦϴΑ ϪδҨΎϘϣ ΎΗ ϩΩή̯ ΏΎΨΘϧ΍ ΍έ upper=2 65*  43.3 3 Ω΍Ϊϋ΍ Expected Value ΖϤδϗ έΩ Chi-Square ϥϮϣί΁ ϩήΠϨ̡ έΩ ϢϴϨ̯ ̶ϣ Ωέ΍ϭ ΍έ ̊˼ ˼ϭ ˻˺ ̀

Frequencies typeoftoy Observed N

Expected N

1

gun

Category

45

21.7

23.3

2

camputer

20

43.3

-23.3

Total

Residual

65

Ζγ΍ 0.05 ί΍ ήΘ̳έΰΑ ϭ Asymp. Sig. = 0.655 έ΍ΪϘϣ ΪϨ̯ ̶Ϥϧ Ωέ ήϔλ νήϓ ϪϧϮϤϧ ΍άϟ Test Statistics

Chi-Square(a) df

typeoftoy .200 1

Asymp. Sig.

.655 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 21.7.

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.000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 26.7.

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Ζγ΍ ϩΪη ϩΩέϭ΁ ϡΎϧ ΖΒΛ έΎϣ΁ ϩΪϜθϧ΍Ω ϚϴϜϔΗ ϪΑ ϥΎΘγήϬη ϭ ϥΎΘγ΍ ΰϛήϣ ϱΎϬϫΎ̴θϧ΍Ω έΩ ϝΎΜϣ ̶ϨόҨ Ϫϧ ΎҨ Ωέ΍Ω ΖΧ΍ϮϨ̰Ҩ ϊҨίϮΗ ̶ϟϮΒϗ ΖΒδϧ ˮϪϧΎҨ Ωέ΍Ω ήϴΛΎΗ ϮΠθϧ΍Ω ΏΎΨΘϧ΍ έΩ ϩΪ̰θϧ΍Ω ωϮϧ ΎҨ΁ ϩΪ̰θϧ΍Ω ϦϴΑ ̶ϟϮΒϗ ΖΒδϧ ΎҨ΁ ˮΪϧϮη ̶ϣ ϢϴδϘΗ ΎϬϫΎ̴θϧ΍Ω ϦϴΑ ΖΒδϧ ̮Ҩ ϪΑ ̶ϟϮΒϗ Ω΍ΪόΗ ΏΎΨΘϧ΍ ϥΎϳϮΠθϧ΍Ω ςγϮΗ ˻˹ ήϨϫ ϭ ́˹ ϲγΪϨϬϣ ϲϨόϳ ˮΖγ΍ ˻˹ ϪΑ ́˹ ήϨϫ ϭ ̶γΪϨϬϣ ΩϮη ϲϣ

ϩΪ̰θϧ΍Ω ωϮϧ

ϲγΪϨϬϣ

ήϨϫ

ΩΎμΘϗ΍

ήϳΎγ

ϥΎΘγ΍ ΰϛήϣ

˺˿

˺˽

˺˼

˺˼

ϥΎΘγήϬη

˺˽

˿

˺˹

́

Ύϫ ήϴϐΘϣ ΏΎΨΘϧ΍ Name

Width

Decimal

Label

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1

faculty

Numeric

8

-

-

place

Numeric

8

-

-

2

frequency

Numeric

8

-

-

Value 1=eng 2=art 3=eco 4=etc 1=center 2=outer -

Missing column Align

Measure

-

10

left

Scale

-

10

left

Scale

-

10

left

Scale

ϢϴϫΩ ̶ϣ ϥίϭ frequency ήϴϐΘϣ ϪΑ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ chi-square ϥϮϣί΁ ί΍ ΖΧ΍ϮϨ̰Ҩ ϊҨίϮΗ ϥϮϣί΁ ̵΍ήΑ

Analyze – nonparametric – chi-square – Test variable ……… faculty ΪϨ̯ ̶ϣ ϥϮϣί΁ ΍έ ϥ΁ ϊҨίϮΗ ϭ Ϊϧί ̶ϣ ϊϤΟ ΍έΎϫ ̶ϟϮΒϗ Ω΍ΪόΗ εΩϮΟ ΩϮη ̶ϣ ϩΪϫΎθϣ Ϫ̯ έϮτϧΎϤϫ ̶ϣ ΪϴҨΎΗ ΍έ ΖϴόϤΟήΘϣ΍έΎ̡ ϪϧϮϤϧ Ζϔ̳ ϥ΍ϮΗ ̶ϣ ϭ ΪϨ̯ ̶Ϥϧ Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̯ Ζγ΍ ϦҨ΍ ϪΠϴΘϧ ΪϨ̯ facaulty Observed N 30

Expected N 23.5

Residual 6.5

art

20

23.5

-3.5

ecoomic

23

23.5

-.5

etc

21

23.5

-2.5

Total

94

engineering

Test Statistics facaulty ChiSquare(a) df Asymp. Sig.

2.596 3 .458

a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 23.5.

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Type

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50*0.8  40 50*0.2  10

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˻˹ ϪΑ ́˹ ΖΒδϧ ϥϮϣί΁ ̵΍ήΑ Chi-Square ϥϮϣί΁ ϩήΠϨ̡ έΩ 1,2 ˬ Expected Range ΖϤδϗ έΩ Ω΍Ϊϋ΍ Expected Value ΖϤδϗ έΩ ϢϴϨ̯ Ωέ΍ϭ ΍έ ˹ ˻ ˹ ́ ΩϮΧ ̶ϨόҨ ΪλέΩ ϪΑ ΍έ ΎϬϧ΁ ΖΒδϧ ΎҨ ϢϴϨ̯ ̶ϣ Ωέ΍ϭ ΍έ˺˹ϭ ̊˹ Frequencies facaulty

Category 1 2

Observed N

Expected N

Residual

engineering

30

40.0

-10.0

art

20

10.0

10.0

Total

50

Chi-Square(a) df

facaulty 12.500 1

Asymp. Sig.

.000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 10.0.

Ϊη Ωέ ϪϧϮϤϧ ςγϮΗ νήϓ ϦҨ΍ Ϫ̰ϨҨ΍ ϪΠϴΘϧ

įř ƶƬưūƹŵ Śƿ Binomial ƱƺƯŻō ̵΍ ϪϠϤΟ ϭΩ ̶ϓΩΎμΗ ήϴϐΘϣ ϒҨήόΗ

ΪηΎΑ ϪΘη΍Ω ̶ΗϭΎϔΘϣ ΞҨΎΘϧ ΩϮη έ΍ή̰Η ΎϫέΎΑ ϥΎδ̰Ҩ ςҨ΍ήη έΩ ή̳΍ Ϫ̯ Ζγ΍ ̶θҨΎϣί΁ ̶ϓΩΎμΗ ζҨΎϣί΁ ˮϪϧ ΎҨ ΩϮη ̶ϣ ΪϴҨΎΗ ϪϧϮϤϧ ςγϮΗ ΖϴόϤΟ ̵ϭέ ί΍ ϩΪη ϩΩί αΪΣ P

Ω΍ΪΧέ ϡΪϋ Ύϳ Ω΍ΪΧέ Ϫϛ ϲϳΎΠϧ΁ ί΍ ΩϮηϲϣ ϩΪϴΠϨγ Ϊϣ΁ ζϴ̡ Ϛϳ Ω΍ΪΧέ ϝΎϤΘΣ΍ ΖΒδϧ ϥϮϣί΁ Ϧϳ΍ έΩ

ΩϮηϲϣ ϩΩΎϔΘγ΍ ϥϮϣί΁ ϱ΍ήΑ ϊϳίϮΗ Ϧϳ΍ ί΍

Binomial ϱ΍ ϪϠϤΟϭΩ ϊϳίϮΗ Ϊϣ΁ ζϴ̡

ϥϮϣί΁ Ϧϳ΍ ϪΑ ϲγήΘγΩ εϭέ Analyze - Nonparametric Tests - Binomial...

ˮΖγ΍ ˾˹ ϪΑ ˾˹ ΎϬϧΎΘγήϬη ϭ ϥΎΘγ΍ ΰ̯ήϣ έΩ ϥΎҨϮΠθϧ΍Ω ̶ϟϮΒϗ ΖΒδϧ ΎҨ΁ ϝΎΜϣ

1 H 0 : p 

2

1 H 1 : p 

2

Analyze - Nonparametric Tests - Binomial... variable…………………....place Test propertion………….0.5

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Test Statistics

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Binomial Test

place

Group 1

Category center

Group 2

outer

56

Observed Prop. .60

38

.40

94

1.00

N

Total

Asymp. Sig. (2-tailed) .079(a)

Test Prop. .50

a Based on Z Approximation.

Chi-Square ϥϮϣί΁ ί΍ ϩΩΎϔΘγ΍ ή̴ҨΩ ϩ΍έ 1,2 ˬ Expected Range ΖϤδϗ έΩ ϢϴϨ̯ ̶ϣ Ωέ΍ϭ ΍έ 0.5ϭ 0.5 Ω΍Ϊϋ΍ Expected Value ΖϤδϗ έΩ Ζγ΍ ήΗΩϭΪΤϣ ̵΍ ϪϠϤΟ ϭΩ ̶ϟϭ Ωέ΍Ω ̵ήΘθϴΑ ΖϴϣϮϤϋ Chi-Square ϥϮϣί΁ ϪΘ̰ϧ

̀˹ ΖΒδϧ ϪΑ ήҨΎγ ϭ ΩΎμΘϗ΍ ̵ΎϬϫϭή̳ ΎΑ ήϨϫ ϭ ̶γΪϨϬϣ ̵ΎϬϫϭή̳ ϥΎҨϮΠθϧ΍Ω ̶ϟϮΒϗ ΖΒδϧ ΎҨ΁ ϝΎΜϣ ˮϪϧ ΎҨ Ζδϫ ˼˹ ϪΑ H 0 : p  ΪϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ ήϨϫ ϭ ̶γΪϨϬϣ ϮΠθϧ΍Ω H 1 : q  ΪϨ̯ ̶ϣ ΏΎΨΘϧ΍ ΍έ ήҨΎγ ϭ ΩΎμΘϗ΍ ϮΠθϧ΍Ω

Analyze - Nonparametric Tests - Binomial... variable…………………....place

Test propertion………….0.7 Cutpoint ………………….2

ΪϨ̯ ΏΎδΣ ή̴ҨΩ ϩϭή̳ ̮Ҩ ΍έ ˻ ί΍ ΪόΑ ϭ ϩϭή̳ ̮Ҩ ΍έ ˻ϭ˺ ϩϭή̳ ̶ϨόҨ Ζγ΍ cutpoint=2 ̶Θϗϭ Binomial Test Category facaulty

Observed Prop.

N

Group 1

<= 2

50

.5

Group 2

>2

44

.5

Test Prop.

Asymp. Sig. (1-tailed)

.7

.000(a,b)

Total

94 1.0 a Alternative hypothesis states that the proportion of cases in the first group < .7. b Based on Z Approximation.

ΪϨ̯ ̶ϣ Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̰ϨҨ΍ ϪΠϴΘϧ

Runs ƱƺƯŻō

ΩϮηϲϣ ϥϮϣί΁ Ύϫ

ϩΩ΍Ω ϥΩϮΑ ϲϓΩΎμΗ ϥΎ Ϝϣ΍ Ύϫ ϩΩ΍Ω ϱΪϨΑ ϪΒΗέ ϭ ϥϮϣί΁ Ϧϳ΍ ί΍ ϩΩΎϔΘγ΍ ΎΑ ί΍ ήΗ ϦϴҨΎ̡ ̶̰Ҩ ϭ Ϧϴ̴ϧΎϴϣ ̵ϻΎΑ ̶̰Ҩ ̶ϟ΍ϮΘϣ ϩΩ΍Ω ϭΩ Ϫ̯ ΩϮη ̶ϣ ϞλΎΣ ̶ϧΎϣί Ύϫ ϩΩ΍Ω ϥΩϮΑ ̶ϓΩΎμΗ ΪηΎΑ ϥ΁ ϥϮϣί΁ Ϧϳ΍ ϪΑ ϲγήΘγΩ εϭέ Analyze – Nonparametric Tests – Runs Ϧϳ΍ Ϫϛ ΪϴϨϛ ϥϮϣί΁ spss ΕΎϧΎϜϣ΍ ΎΑ ϭ ΪϴϨϛ ΩΎΠϳ΍ ϲϓΩΎμΗ ΩΪϋ ˺˹˹ Rand() ϊΑΎΗ Ϟδϛ΍ ί΍ ϩΩΎϔΘγ΍ ΎΑ ΪϨΘδϫ ϲϓΩΎμΗ Ω΍Ϊϋ΍ έΩ data ϲΗΎϋϼσ΍ ΪϨγ ϥΩήϛ ίΎΑ ϡΎ̴Ϩϫ έΩ Ζγ΍ ϲϓΎϛ ΪϴϨϛ έϭήϣ ΍έ Ϟδϛ΍ ί΍ ΕΎϋϼσ΍ Ωϭέϭ ϩϮΤϧ ΪϴϨϛ ΏΎΨΘϧ΍ ΍έ excel ϪϨϳΰ̳ file type ΖϤδϗ

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ϢϴϨϴΒΑ Ϣϴϫ΍ϮΧ ̶ϣ Ϊϧ΍ ϪΘϓή̳ ϩί΍Ϊϧ΍ ΍έ ϩΪη ϪΘΧΎγ ̵Ύϫ ϪϟϮϟ ήτϗ ςγϮΘϣ ̵έΎ̯ Ζϔϴη έΎϬ̩ έΩ ϝΎΜϣ ΪҨΎΑ Ύϫ ϪϟϮϟ ήτϗ Ωέ΍Ω ϥΪη ϩήΒϴϟΎ̯ ϪΑ ίΎϴϧ ϩΎ̴ΘγΩ ΎҨ ΪϨΘδϫ ̶ϓΩΎμΗ ϥΎθϨϴ̴ϧΎϴϣ ϪΑ ΖΒδϧ Ω΍Ϊϋ΍ ϦҨ΍ ΪηΎΑ ̊ Ύϫήτϗ ςγϮΘϣ 3.8 4.2 3.3 4.5 ˺Ζϔϴη ˻Ζϔϴη

˼Ζϔϴη ̊Ζϔϴη

İĮŤƀŞưƷ ƱƺƯŻō

̶ϣ ϪΒγΎΤϣ ΍έ ϥ΁ ΐҨήο ϭ Ωί΍Ωή̡ ̶ϣ ήϴϐΘϣ ΪϨ̩ ΎҨ ϭΩ ϦϴΑ ρΎΒΗέ΍ ̶γέήΑ ϪΑ ̶̴ΘδΒϤϫ ϪτΑ΍έ ήϴϐΘϣ Ε΍ήϴϴϐΗ ΎΑ ήϴϐΘϣ ̮Ҩ Ε΍ήϴϴϐΗ ή̳΍ ΪηΎΑ ̶ϔϨϣ ΎҨ ΖΒΜϣ Ζγ΍ Ϧ̰Ϥϣ ΎϫήϴϐΘϣ ϦϴΑ ̶̴ΘδΒϤϫ ΪϨ̯ ϩ΍ήϤϫ ̵ή̴ҨΩ ζϫΎ̯ ΎΑ ̶̰Ҩ ζϫΎ̯ β̰όϟΎΑ ΎҨ ̵ή̴ҨΩ ζҨ΍ΰϓ΍ ΎΑ ̶̰Ҩ ζҨ΍ΰϓ΍ ϭ ΪηΎΑ ϩ΍ήϤϫ ̵ή̴ҨΩ +1 ΎΗ 0 ί΍ Ϫ̯ ΪҨήΧ ̵΍ήΑ ΎοΎϘΗ ϭ Ϊϣ΁έΩ ζҨ΍ΰϓ΍ ϞΜϣ Ζγ΍ ΖΒΜϣ ΎϬϧ΁ ϦϴΑ ̶̴ΘδΒϤϫ ϢϴҨϮ̳ ̶ϣ ΩϮθΑ Ωέ΍Ω ϥΎγϮϧ ϦϴΑ ̶̴ΘδΒϤϫ Ϫ̯ ΩϮη ̶ϣ ϪΘϔ̳ ΩϮη ϩ΍ήϤϫ ̵ή̴ҨΩ ήϴϐΘϣ ζϫΎ̯ ΎΑ ήϴϐΘϣ ̮Ҩ ζҨ΍ΰϓ΍ ϭ ήϴϴϐΗ ή̳΍ ΪϨ̯ ̶ϣ ήϴϴϐΗ -1 ΎΗ 0 ί΍ ϭ Ζγ΍ ̶ϔϨϣ ΎϬϧ΁ Ζγ΍ ήϔλ ΎϬϧ΁ ϦϴΑ ̶̴ΘδΒϤϫ ΐҨήο ΪηΎΑ ϪΘη΍Ϊϧ ΩϮΟϭ ̵΍ ϪτΑ΍έ ήϴϐΘϣ ϭΩ ϦϴΑ ή̳΍ Ωέ΍Ω ΩϮΟϭ ήϴϐΘϣ ϭΩ ϦϴΑ ϲτΧ ϪτΑ΍έ ΪηΎΑ ̮ϳΩΰϧ -1 Ύϳ1 ϪΑ ϲ̴ΘδΒϤϫ ΐϳήο ή̳΍

̶̴ΘδΒϤϫ ΐҨήο Ζγ΍ ήϴϐΘϣ ϭΩ ϦϴΑ ρΎΒΗέ΍ ΩϮΟϭ ϩΪϨϫΩ ϥΎθϧ ΐϳήο Ϧϳ΍ Ζγ΍ ήΘϜϳΩΰϧ ˺ Ύϳ ϭ ˺ ϪΑ ΐϳήο έ΍ΪϘϣ ΪηΎΑ ΪϳΪη ήϴϐΘϣ ϭΩ ρΎΒΗέ΍ Ϫ̩ήϫ ΩϮηϲϣ ϚϳΩΰ

ϧ ήϔλ ϪΑ ΐϳήο έ΍ΪϘϣ ήϴϐΘϣ ϭΩ ϦϴΑ ρΎΒΗέ΍ ζϫΎϛ ΎΑ ΩϮΑ Ϊϫ΍ϮΧ ήϔλ ήΑ΍ήΑ ϲ̴ΘδΒϤϫ ΐϳήο έ΍ΪϘϣ ΪϨηΎΑ ϞϘΘδϣ ήϴϐΘϣ ϭΩ ή̳΍ έΩ Ζϓή̳ ϪΠϴΘϧ ΍έ ϝϼϘΘγ΍ ϥ΍ϮΗϲϣ ΪηΎΑ ήϔλ ήΑ΍ήΑ ϲ̴ΘδΒϤϫ ΐϳήο ή̳΍ ΪϨηΎΑ ϝΎϣήϧ ήϴϐΘϣ ϭΩ ή̳΍

ήϴΧ ΕέϮλ Ϧϳ΍ ήϴϏ

̶̴ΘδΒϤϫ ΐҨήο ϪΒγΎΤϣ ΩϮηϲϣ ϩΩΎϔΘγ΍

Covariance έ΍ΪϘϣ ί΍ ϥϮγήϴ̡ ϲ̴ΘδΒϤϫ ΐϳήο ϪΒγΎΤϣ ϱ΍ήΑ αΎϴϘϣ ϭ ΪΣ΍ϭ ΎΑ Ϫϛ ϲϳΎΠϧ΁ ί΍ ϲϟϭ Ζγ΍ ΐγΎϨϣ ρΎΒΗέ΍ ϥ΍ΰϴϣ ϱήϴ̳ϩί΍Ϊϧ΍ ϱ΍ήΑ

Covarianceέ΍ΪϘϣ ϥϭΪΑ ΐҨήο ̮ϳ ί΍ ̶̴ΘδΒϤϫ ϥ΍ΰϴϣ ϪδҨΎϘϣ ϱ΍ήΑ ΪϳΎΑ Ϊϳ΁ϲϣ ΖγΪΑ ϩΪη ϱήϴ̳

ϩί΍Ϊϧ΍ ϪμΨθϣ Ωήϛ ϩΩΎϔΘγ΍ ΪλέΩ ΪΣ΍ϭ Ωέ΍Ω ΍έ ΖϴλΎΧ Ϧϳ΍ ̶̴ΘδΒϤϫ ΐϳήο Ύϳ Correlation ΩϮηϲϣ ϩΩΎϔΘγ΍ ϱ΍

ϪτϘϧ έ΍ΩϮϤϧ ί΍ ήϴϐΘϣ ϭΩ ρΎΒΗέ΍ ϩΪϫΎθϣ ϱ΍ήΑ ϻϮϤόϣ p ( y / x )  p ( y ) ̶ϨόҨ ̶ϓΩΎμΗ ήϴϐΘϣ ϭΩ Independent ϝϼϘΘγ΍ Ϣϫ ΎΑ ϥΎθϧΩ΍ΪҨϭέ Ϫ̰ϠΑ ϥίϭ ϭ Ϊϗ ϞΜϣ ΪϨηΎΑ Ϣϫ ϝϮϠόϣ ϭ ΖϠϋ ϪΘδΒϤϫ ˱ϼϣΎ̯ ήϴϐΘϣ ϭΩ Ζδϴϧ ϡίϻ Ωέ΍Ω ρΎΒΗέ΍

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̶̴ΘδΒϤϫ ςΧ έ΍ΩϮϤϧ

ϥϮγήϴ̡ ̶̴ΘδΒϤϫ ΐҨήο ϪΒγΎΤϣ E ( xy )  E ( x ).E ( y ) Co var iance  n 1

E ( xy )  E ( x).E ( y ) E ( xy )  E ( x).E ( y ) n 1  Corrolation    Var ( x) Var ( y ) Var ( x)Var ( y ) n 1 n 1

  0 ϭ E ( xy )  E ( x ) E ( y ) ϩΎ̴ϧ΁ ΪηΎΑ ϪΘη΍Ω ΩϮΟϭ ̶̴ΘδΒϤϫ ή̳΍ Analyze –Corrolate – Bivariate … ΪϴϨϛ ϲσ ΍έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ΍ ϪΑ ϲγήΘγΩ ϱ΍ήΑ Ϊϳέ΍Ω ΝΎϴΘΣ΍ ϱΩΪϋ ήϴϐΘϣ ϭΩ ϪΑ Ϟϗ΍ΪΣ έϮΘγΩ Ϧϳ΍ ϱ΍ήΟ΍ ϱ΍ήΑ

ϲ̴ΘδΒϤϫ ΐϳήο ω΍Ϯϧ΍ ί΍ Ϊ˰ҨΎΑ ̶Ϥ˰γ΍ ϭ ̵΍ ϪΒΗέ ˬ ̵ΩΪϋ ̵ΎϬγΎϴϘϣ ί΍ ̮Ҩ ήϫ ̵΍ήΑ Ωή̯ ϩΩΎϔΘγ΍ ϥΎηΩϮΧ ϪΒγΎΤϣ εϭέ

Covariance Correlation ϲ̴ΘδΒϤϫ ϱήΘϣ΍έΎ̡ ϲ̴ΘδΒϤϫ ΐϳήο Pearson ± ϥϮγήϴ̡ ϲ̴ΘδΒϤϫ ΐϳήο ϲτΧ ϲ̴ΘδΒϤϫ

ϝΎ˰ϣήϧ ϊ˰ϳίϮΗ ϱ΍έ΍Ω ϱΩΪ˰ϋ ήϴϐΘϣ ϭΩ ή˰ϫ Ϫϛ Ζγ΍ Ϧϳ΍ ήΑ νήϓ ΪϨΘδϫ ϩήϴϐΘϣ ϭΩ

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Ʊřźƿř żƿř ƺŤǀŤƀƳř ϱήΘϣ΍έΎ̡Ύϧ ϲ̴ΘδΒϤϫ ΐϳήο

Spearman Ϧϣήϴ̢γ΍ ϲ̴ΘδΒϤϫ ΐϳήο ϱ΍ϪΒΗέ ϲ̴ΘδΒϤϫ

Kendall’s Tau_b ±ϝ΍ΪϨϛ ϲ̴ΘδΒϤϫ ΐϳήο ϲΒϴΗήΗ ϲ̴ΘδΒϤϫ

H0 :   0 H1 :   0

ϲ̴ΘδΒϤϫ ΐϳήο ϥϮϣί΁

ϲ̴ΘδΒϤϫ ΐϳήο έ΍ΪϘϣ ϱ΍ήΑ Ϫϓήσ ϭΩ ϥϮϣί΁ Ϛϳ two tail ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ ΩϮη ϲϣ ϡΎΠϧ΍

H0 :   0

H0 :   0

H1 :   0

H1 :   0

ΩϮηϲϣ ϩΩ΍Ω ϲΟϭήΧ έΩ ϲ̴ΘδΒϤϫ ΐϳήο έ΍ΪϘϣ ϥΩϮΑ έ΍ΩΎϨόϣ ϦϴϴόΗ ϥΎϜϣ΍

flag… ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ-

Ϧϴ̴ϧΎϴϣ ϲϔϴλϮΗ έΎϣ΁ ζϳΎϤϧ ϥΎϜϣ΍ option ΏΎΨΘϧ΍ ΎΑ Ώήπϣ ϭ βϧΎϳέ΍Ϯϛ έ΍ΪϘϣ ζϳΎϤϧ ϭ Ωέ΍ΪϧΎΘγ΍ ϑ΍ήΤϧ΍ ϭ ΕέϮλ ϥΎϤϫ ΕϼοΎϔΗ ΐϳήο Ωέ΍Ω ΩϮΟϭ ΕϼοΎϔΗ Ζγ΍ βϧΎϳέ΍Ϯϛ ϪΒγΎΤϣ ϱ΍ήΑ ϪΒγΎΤϣ Exclude Cases pairwise ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ ή̴ϳΩ έΩ ϩΪθϤ̳ ϩΩ΍Ω ϦΘϓή̳ ήψϧ έΩ ϥϭΪΑ Ύϫ ΖϔΟ ΩϮη ϲϣ ϪΒγΎΤϣ ΩέϮϣ ήϫ ϱΎϫήϴϐΘϣ Ϛϳ ΩϮΟϭ ΎΑ Exclude cases listwise ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ Ϊη Ϊϫ΍ϮΧ ϪΘϓή̳ ϩΪϳΩΎϧ ΩέϮϣ Ϟϛ Ωέ΍Ω ϲγέήΑ έΩ ϩΪη Ϣ̳ έ΍ΪϘϣ Ϫϛ ήϴϐΘϣ ϱ΍ήΑ ϱΪόΑ ϭΩ βϳήΗΎϣ Ϛϳ ΐϴΗήΗ Ϧϳ΍ ϪΑ Ωέ΍Ω ΩϮΟϭ ϲγέήΑ Ϧϳ΍ έΩ ΰϴϧ ήϴϐΘϣ ϭΩ ί΍ ζϴΑ ΏΎΨΘϧ΍ ϥΎϜϣ΍ Ϊη Ϊϫ΍ϮΧ ΩΎΠϳ΍ ΎϫήϴϐΘϣ ΖϔΟ ΖϔΟ ϲ̴ΘδΒϤϫ ΐϳήο έ΍ΪϘϣ ϪΒγΎΤϣ ϭ ϲγέήΑ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ήҨί έϮΘγΩ ί΍ ̶̴ΘδΒϤϫ έ΍ΩϮϤϧ Ϣγέ ̵΍ήΑ Graph – Legacy dialogs – scatter plot– simple ΎҨ Matrix

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One ϪϓήτϜϳ ϥϮϣί΁ ί΍ Ζγ΍ ήΘϬΑ Ζγ΍ ήψϧ έΩ ΰϴϧ ϥ΁ ΖϬΟ ϲ̴ΘδΒϤϫ ΐϳήο έ΍ΪϘϣ ήΑ ϩϭϼϋ ή̳΍ ΩϮη ϩΩΎϔΘγ΍ Tail ϪϓήτϜϳ ϥϮϣί΁ ϪϓήτϜϳ ϥϮϣί΁

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íì ç ê ± ºf¨Å Ä ¸m ϭΩ ϝϼϘΘγ΍ ϥϮϣί΁ ̵΍ήΑ Ϣϫ ϭ ϊҨίϮΗ εί΍ήΑ ̵΍ήΑ ϢϬΑ Chi-square ϥϮϣί΁ Ϫ̯ Ϊη ϪΘϔ̳ ϞΒϗ ϪδϠΟ έΩ ήϴϐΘϣ ϭΩ ϦϴΑ ϪτΑ΍έ ΩϮΟϭ ϪϧΎθϧ ΩϮΑ   0 ή̳΍ (x,y) ΐΗήϣ Νϭί ̵΍ήΑ ̶ϨόҨ Ωϭέ ̶ϣ έΎ̰Α ̶ϓΩΎμΗ ήϴϐΘϣ ΪηΎΑ ̶ϣ ϭ ϥΎΘγ΍ ΰ̯ήϣ ί΍ ϒϠΘΨϣ ̵Ύϫ ϩΪ̰θϧ΍Ω έΩ ϥΎҨϮΠθϧ΍Ω ̶ϟϮΒϗ ΖΒδϧ ϪδҨΎϘϣ ϞΒϗ ϪδϠΟ ϝΎΜϣ έΩ ̶Ϥγ΍ ήϴϐΘϣ ϭΩ ήϫ Ϫ̯ ϒϠΘΨϣ ̵Ύϫ ϪΘηέ ϭ ϩΪ̰θϧ΍Ω ϞΤϣ Ϫ̯ Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΧ ̶ϣ ΎϬϧΎΘγήϬη ˮϪϧ ΎҨ ΪϨϠϘΘδϣ ΪϨΘδϫ

H0 :   0 H1 :   0

Analyze –Descriptive Statistics- Crosstabs- statistic……chi-square facaulty * place Crosstabulation Count

Chi-Square Tests

facaulty

Total

center 16

outer 14

center 30

art

14

6

20

ecoomic

13

10

23

etc

13

8

21

56

38

94

engineering

Total

Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Asymp. Sig. (2-sided)

df

1.524(a) 1.551

3 3

.677 .671

.153

1

.696

94

a 0 cells (.0%) have expected count less than 5. The minimum expected count is 8.09.

ή̳΍ ϭ Ϊϧ΍ ϩΩή̯ ̶ΜϨΧ ΍έ ή̴ҨΪϤϫ ΕΎϓϼΘΧ΍ ̶ϨόҨ Ζγ΍ ̮̩Ϯ̯ Pearson Chi-Square=1.524 έ΍ΪϘϣ ̶Θϗϭ Ζγ΍ ήϴϐΘϣ ϭΩ ϦϴΑ ϪτΑ΍έ ϩΪϨϫΩ ϥΎθϧ ΪηΎΑ ̱έΰΑ

Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̯ ΪϫΩ ̶ϣ ϥΎθϧ ϭ ϩΩϮΑ 0.05 ί΍ ήΘ̳έΰΑ Ϫ̯ Asymp. Sig. (2-sided)=0.677 ήϴϐΘϣ ϭΩ ϦϴΑ ϝϼϘΘγ΍ ΩϮΟϭ ̶ϨόҨ ΪϨ̯ ̶Ϥϧ

Ζγ΍ Ρήη ϦҨ΍ ϪΑ ϪΒγΎΤϣ ϝϮϣήϓ ˮΖγ΍ ϩΪϣ΁ ΖγΩ ϪΑ ΎΠ̯ ί΍ 1.524 ΩΪϋ Ϫ̯ Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΧ ̶ϣ outer ϥϮΘγ ϭ center ϥϮΘγ ϩΪη ϩΪϫΎθϣ ήҨΩΎϘϣ Oi έΎψΘϧ΍ ΩέϮϣ ήҨΩΎϘϣ Ei (Oi  Ei ) 2  2   έΎψΘϧ΍ ΩέϮϣ ήҨΩΎϘϣ ϪϧϮϤϧ ̵Ϯγ ί΍ ϩΪη ϩΪϫΎθϣ ϑϼΘΧ΍ ςγϮΘϣ

Ei

outer ϥΎΘγήϬη ϥϮΘγ ̵΍ήΑ center ϥΎΘγ΍ ΰ̯ήϣ ϥϮΘγ ̵΍ήΑ

30*38 30*56 E1   12.13 E1   17.87

94 94

20*38 20*56

E2  E2   8.09  11.91 94 94

23*38 23*56

E3  E3   9.30  13.7 94 94

21*38 21*56 E4  E4   8.49  12.51 94 94 64 www.ircdvd.com

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place

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̶ϣ ϩΩΎϔΘγ΍ ήҨί ϝϮϣήϓ ί΍ ΕϮΘγ ϭ ήτγ ̵Ύϫ ̶ϧ΍ϭ΍ήϓ ωϮϤΠϣ ί΍ έΎψΘϧ΍ ΩέϮϣ ̶ϧ΍ϭ΍ήϓ ϪΒγΎΤϣ ̵΍ήΑ ΩϮη ήτγ έΩ ϩΪη ϩΪϫΎθϣ ̶ϧ΍ϭ΍ήϓ ϊϤΟ A A* B Ei  ϥϮΘγ έΩ ϩΪη ϩΪϫΎθϣ ̵Ύϫ ̶ϧ΍ϭ΍ήϓ ϊϤΟ B N ϩΪη ϩΪϫΎθϣ ̶ϧ΍ϭ΍ήϓ ϊϤΟ N ϡ΍ i έΎψΘϧ΍ ΩέϮϣ ̶ϧ΍ϭ΍ήϓ Ei

2  

Ei

 (O  E )  i

i

Ei



(16  17.87) 2 (14  11.91) 2 (13  13.7) 2 (13  12.51) 2     0.62 17.87 11.91 13.7 12.51



(14  12.13) 2 (6  8.09)2 (10  9.30)2 (8  8.49) 2     0.91 12.13 8.09 9.30 8.49

2

ϥΎΘγ΍ ΰ̯ήϣ

ϥΎΘγήϬη

Pearson Chi  Square=0.62+0.91=1.524

ΪϨ̯ ΏΎδΣ ΍έ Ύϫ EI ̶ϨόҨ έΎψΘϧ΍ ΩέϮϣ ήҨΩΎϘϣ Ϊϧ΍ϮΗ ̶ϣ SPSS έ΍ΰϓ΍ ϡήϧ

Analyze –Descriptive Statistics- Crosstabs- statistic……chi-square Cell ……..….observed  , expected 

place * facaulty Crosstabulation facaulty place

center

Count Expected Count

outer

Count Expected Count

Total

Count Expected Count

engineering 16

art

Total

14

ecoomic 13

etc 13

engineering 56

17.9

11.9

13.7

12.5

56.0

14

6

10

8

38

12.1

8.1

9.3

8.5

38.0

30

20

23

21

94

30.0

20.0

23.0

21.0

94.0

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

2

(Oi  Ei ) 2

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ΪϴϨ̯ ϲγέήΑ ΍έ ed,carcat ϦϴηΎϣ ωϮϧ ϭ ΕϼϴμΤΗ ϥ΍ΰϴϣ ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍ demo ϞϳΎϓ έΩ ϝΎΜϣ

H0 :   0

H1 :   0

Analyze –Descriptive Statistics- Crosstabs- statistic……chi-square  correlation  Level of education * Primary vehicle price category Crosstabulation Count Total

Primary vehicle price category Economy

Standard

Luxury

Economy

Did not complete high school

483

481

426

1390

High school degree

587

689

660

1936

Some college

390

485

485

1360

College degree

312

508

535

1355

69

112

178

359

1841

2275

2284

6400

Post-undergraduate degree Total

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Level of education

Chi-Square Tests

Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Value 85.689(a) 85.520

df

75.149

8 8

Asymp. Sig. (2-sided) .000 .000

1

.000

6400

a 0 cells (.0%) have expected count less than 5. The minimum expected count is 103.27.

Symmetric Measures

Interval by Interval

Pearson's R

Ordinal by Ordinal

Spearman Correlation

N of Valid Cases

Value .108

Asymp. Std. Error(a) .012

Approx.

T(b) 8.720

Approx. Sig. .000(c)

.105

.012

8.453

.000(c)

6400

a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal approximation.

ϥ΍ΰϴϣ ΩϮη ̶ϣ Ωέ ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍ ϭ ΪϨ̯ ̶ϣ Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ β̡ p  value  0 ϪΠϴΘϧ ϥΎθϧ ΍έ 0.105 ΩΪϋ Ϫ̯ Ζγ΍ ΐγΎϨϣ Ϧϣήϴ̢γ΍ ΪϨΘδϫ ϲϤγ΍ ήϴϐΘϣ ϭΩ ϥϮ̩ ϝϭΪΟ έΩ ϲ̴ΘδΒϤϫ ΪϫΩ ϲϣ

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ήϴϐΘϣ Ϛϳ ϝήΘϨϛ ΕέϮλ έΩ ήϴϐΘϣ ϭΩ ϦϴΑ ϲτΧ ϲ̴ΘδΒϤϫ ϥ΍ΰϴϣ ζΠϨγ ϥΎϜϣ΍ ΐϳήο Ϧϳ΍ ί΍ ϩΩΎϔΘγ΍ ΎΑ Ωέ΍Ω ΩϮΟϭ ϥ΁ ήϴΛΎΗ ϑάΣ Ύϳ ϡϮγ

ϲ̴ΘδΒϤϫ ΐϳήο ϪΒγΎΤϣ ΎΑ ΪϨηΎΑ ϪΘη΍Ω ϪτΑ΍έ ΰϴϧ ϱή̴ϳΩ ήϴϐΘϣ Ϫτγ΍ϭ ϪΑ ήϴϐΘϣ ϭΩ Ζγ΍ ϦϜϤϣ Ϊη Ϊϫ΍ϮΧ ϪΒγΎΤϣ ϡϮγ ήϴϐΘϣ ήΛ΍ ϑάΣ ΎΑ ήϴϐΘϣ ϭΩ ϦϴΑ ϲτΧ ϪτΑ΍έ ϥ΍ΰϴϣ ϲ΋ΰΟ Ζγ΍ ήϳί ΕέϮλ ϪΑ ϪΒγΎΤϣ εϭέ rAB  rAC rBC rABC  (1  r 2 AC )(1  r 2 BC )

Ζγ΍ ϱΩΪϋ ήϴϐΘϣ ˼ Ϟϗ΍ΪΣ ϪΑ ΝΎϴΘΣ΍ έϮΘγΩ Ϧϳ΍ ϡΎΠϧ΍ ϱ΍ήΑ ΪϴϨϛ ϲσ ΍έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ΍ ϪΑ ϲγήΘγΩ ϱ΍ήΑ Analyze - Correlate - Partial...

ϩΩΎγ ϲ̴ΘδΒϤϫ ΐϳήο ϝϭΪΟ ζϳΎϤϧ ϭ ϲϔϴλϮΗ ϱΎϫ ϩέΎϣ΁ ζϳΎϤϧ ϥΎϜϣ΍ Option ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ Ωέ΍Ω ΩϮΟϭ ΰϴϧ Zero-order…

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ή˰˰ψϧ έΩ ΍έ age,income,car Ϧϴ˰˰ηΎϣ Ϊ˰˰ϳήΧ Ϫ˰˰Ϩϳΰϫ ϭ Ϊ˰˰ϣ΁έΩ ˬ Ϧ˰˰γ ή˰˰ϴϐΘϣ Ϫ˰˰γ demo Ϟ˰˰ϳΎϓ έΩ ϝΎ˰˰Μϣ Ϊϴθ̰Α ήϴϐΘϣ Ϫγ Ϧϳ΍ ϱ΍ήΑ ϲδϳήΗΎϣ έϮσ ϪΑ ΍έ scatterplot έ΍ΩϮϤϧ ΍ΪΘΑ΍Ϊϳήϴ̴Α Grafh - scatterplot - matrix ϢϴϨ̯ ϲϣ ΏΎδΣ ΍έ ήϴϐΘϣ Ϫγ ϦϴΑ ϲ̴ΘδΒϤϫ ΐϳήο Analyze - correlate- bivariate … ή˰˰ϴϐΘϣ ϭΩ Ϧϴ˰˰Α Ϫ˰˰τΑ΍έ ΍ΩΪ˰˰Πϣ ϭ Ϫ˰˰Θϓή̳ Ζ˰˰ΑΎΛ ΍έ income ή˰˰ϴϐΘϣ ϲ˰˰ϳΰΟ ϲ̴Θδ˰˰ΒϤϫ ΐϳή˰˰ο ί΍ ϩΩΎϔΘ˰˰γ΍ Ύ˰Α Ϣϳέϭ΁ ϲϣ ΖγΪΑ ΍έ age,car Correlations

Age in years

Pearson Correlation

Household income in thousands .335(**)

Price of primary vehicle .376(**)

.000

.000

6400

6400

6400

.335(**)

1

.792(**)

Age in years 1

Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed)

.000

N Price of primary vehicle

.000

6400

6400

6400

.376(**)

.792(**)

1

Sig. (2-tailed)

.000

.000

N

6400

6400

Pearson Correlation

6400

** Correlation is significant at the 0.01 level (2-tailed).

0.335 income ϭ age ̶̴ΘδΒϤϫ ΐҨήο 0.376 ....... car ϭ age ̶̴ΘδΒϤϫ ΐҨήο 0.792 « income ϭ car ̶̴ΘδΒϤϫ ΐҨήο

income ϝήΘϨ̯

Correlations

Control Variables Household income in thousands

Age in years

Price of primary vehicle

Correlation

Age in years 1.000

Price of primary vehicle .193

Significance (2-tailed)

.

.000

df

0

6397

Correlation

.193

1.000

Significance (2-tailed)

.000

.

df

6397

0

0.193 ....... car ϭ age ̶̴ΘδΒϤϫ ΐҨήο

ή˰ϴϐΘϣ ϝή˰ΘϨ̯ ί΍ β˰̡ ϲ˰ϟϭ ΩϮ˰Α 0.376 ˬ age,car ή˰ϴϐΘϣ ϭΩ Ϧϴ˰Α ϲ̴Θδ˰ΒϤϫ ΐϳήο ϝϭ΍ ΖϟΎΣ έΩ ϪΠϴΘϧ ΪϫΩ ϲϣ ϥΎθϧ ΍έ κϟΎΧ ρΎΒΗέ΍ ΩΪϋ Ϧϳ΍ Ϫ̯ Ωή̯ ΍Ϊϴ̡ ήϴϴϐΗ 0.193 ϪΑ ˬ income 68 www.ircdvd.com

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Household income in thousands

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Ωέ΍Ω ΩϮΟϭ ϩ΍έ Ϫγ ήϴϐΘϣ ϭΩ ρΎΒΗέ΍ ζΠϨγ ϱ΍ήΑ

scatterplot έ΍ΩϮϤϧ Ϣγέ ˺ Corrolation ϲ̴ΘδΒϤϫ ΐϳήο ϱήϴ̳ ϩί΍Ϊϧ΍ ˻ Ζθ̳ήΑ ςΧ ϥϮϴγή̳έ ϪΒγΎΤϣ ˼

Ωϭέ ϲϣ έΎ̰Α ήϴϐΘϣ ϭΩ ρΎΒΗέ΍ ϝΪϣ Ϫ΋΍έ΍ ϭ ζΠϨγ ϱ΍ήΑ Ϫ̯ Ζγ΍ ϱέΎϣ΁ εϭέ ϦϳήΗΩήΑέΎ̯ή̡ ϥϮϴγή̳έ Ζγ΍ ΍έ΍Ω ΍έ ϩΪη ϩΪϫΎθϣ ϱΎϫϩΩ΍Ω ΎΑ ϲ̴ϨϫΎϤϫ ϦϳήΘϜϳΩΰϧ Ϫϛ ϲτΧ ϪϟΩΎόϣ

ΐϳ΍ήο ˬϲτΧ ϥϮϴγή̳έ ΪϨϛϲϣ Ωέϭ΁ήΑ

Ωέ΍Ω ΩϮΟϭ ΰϴϧ ϱΪόΑ ήϳΩΎϘϣ ϲϳϮ̴θϴ̡ ϥΎϜϣ΍ Ζθ̳ήΑ ςΧ ϪϟΩΎόϣ ί΍ ϩΩΎϔΘγ΍ ΎΑ Ϣϳέ΍Ω Dependent ϪΘδΑ΍ϭ ήϴϐΘϣ Ϛϳ ϭ Independent ϞϘΘδϣ ήϴϐΘϣ Ϛϳ ϥϮϴγή̳έ ϪϟΩΎόϣ έΩ Ζγ΍ ϲϜϳ ί΍ ζϴΑ ϞϘΘδϣ ϱΎϫήϴϐΘϣ Ω΍ΪόΗ ϩήϴϐΘϣ ΪϨ̩ ϥϮϴγή̳έ ϪϟΩΎόϣ έΩ

ϝΎΜϣ έϮθϛ Ϛϳ έ΍ϮϧΎΧ Ϊϣ΁έΩ αΎγ΍ήΑ βϛϮϟ ϱϻΎϛ Ϛϳ εϭήϓ ϥ΍ΰϴϣ Ωέϭ΁ήΑ ΕϼϴμΤΗ ϥ΍ΰϴϣ ˬϪΑήΠΗ ˬϦγ ˬαΎγ΍ήΑ ϩΎ̴ηϭήϓ Ϛϳ ϥΎ̳ΪϨηϭήϓ εϭήϓ ϥ΍ΰϴϣ Ωέϭ΁ήΑ ιϮμΨϣ ϱΎϬηϭέ ί΍ ϩΩΎϔΘγ΍ ϭ έάΑ ωϮϧ ˬΩϮϛ ωϮϧ ˬϲ̳ΪϧέΎΑ ϥ΍ΰϴϣ αΎγ΍ήΑ ϝϮμΤϣ ϥ΍ΰϴϣ Ωέϭ΁ήΑ ΕΎϓ΁ ϊϓέ ήϴΗ ωΎϔΗέ΍ αΎγ΍ήΑ ϕήΑ ύ΍ή̩ ήϴΗ ςγϮΗ Ύπϓ Ϛϳ ϥΪη Ϧηϭέ ΖΣΎδϣ Ωέϭ΁ήΑ

ϩΩΎγ ϲτΧ ϝΪϣ Y / x  a  bX  

 2  (Y  Yˆ ) 2 ΖγΎϣ ϝήΘϨ̯ ΖΤΗ ϭ ϞϘΘδϣ ήϴϐΘϣ x ϥ΁ έΩ Ϫϛ ΩϮηϲϣ ϩΪϴϣΎϧ

constant Ύϳ ΍ΪΒϣ ί΍ νήϋ έ΍ΪϘϣ a ΩϮηϲϣ ϩΪϴϣΎϧ ϞϘΘδϣ ήϴϐΘϣ ΐϳήο Ύϳ ςΧ ΐϴη έ΍ΪϘϣ

b Ζγ΍ ϥϮϴγή̳έ ϝΪϣ ϭ ϩΪϫΎθϣ ϱΎτΧ e Ζγ΍  2 βϧΎϳέ΍ϭ ϭ a+bx Ϧϴ̴ϧΎϴϣ ϱ΍έ΍Ω ϭ Ζγ΍ x ϪΑ ϪΘδΑ΍ϭ ϲϓΩΎμΗ ήϴϐΘϣ Y|x Ζγ΍  2 βϧΎϳέ΍ϭ ϭ ήϔλ Ϧϴ̴ϧΎϴϣ ϱ΍έ΍Ω ϭ Ζγ΍ ϲϓΩΎμΗ ϱΎτΧ  ϲϓΩΎμΗ ήϴϐΘϣ a, b,  2 ί΍ ΪϨΗέΎΒϋ ϝΪϣ Ϧϳ΍ ϱΎϫήΘϣ΍έΎ̡ Ζγ΍ ϩΪη Ωέϭ΁ήΑ έ΍ΪϘϣ Yˆ ϭ ϲόϗ΍ϭ έ΍ΪϘϣ Y

ΕΎοϭήϔϣ ΎϫϩΪϧΎϣ ϲϗΎΑ ϊϳί

ϮΗ Ύϳ Ζγ΍ ϝΎϣήϧ ϊϳίϮΗ Ϛϳ ϪΘδΑ΍ϭ ήϴϐΘϣ ϊϳίϮΗ ˬϞϘΘδϣ ήϴϐΘϣ έ΍ΪϘϣ ήϫ ϱ΍ήΑ ΪηΎΑ ή̴ϳΩ Ε΍ΪϫΎθϣ ί΍ ϞϘΘδϣ ϭ ϝΎϣήϧ ΪϳΎΑ ΪηΎΑ ΖΑΎΛ ϪΘδΑ΍ϭ ήϴϐΘϣ βϧΎϳέ΍ϭ ΪϳΎΑ ϞϘΘδϣ ήϴϐΘϣ ήϳΩΎϘϣ ϪϤϫ ϱ΍ήΑ ΪϨηΎΑ ϲϓΩΎμΗ ΪϳΎΑ Ε΍ΪϫΎθϣ ϪϤϫ ΪηΎΑ ϲτΧ ΪϳΎΑ ϞϘΘδϣ ϭ ϪΘδΑ΍ϭ ήϴϐΘϣ ϭΩ ρΎΒΗέ΍

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̮Ϥϧ ϑήμϣ ϭ ϞϘΘδϣ ήϴϐΘϣ ϥϮΧ έΎθϓ Ϫ̯ Ϊϳήϴ̴Α ήψϧ έΩ ΍έ ̮Ϥϧ ϑήμϣ ϭ ϥϮΧέΎθϓ ήϴϐΘϣ ϭΩ ˱ϼΜϣ Ζγ΍ ϪΘδΑ΍ϭ ήϴϐΘϣ Ζγ΍ ϩΩήϛ ϝΪϣ ΍έ Ε΍ΪϫΎθϣ ήΘϬΑ ϥϮϴγή̳έ ϪϟΩΎόϣ ΪηΎΑ ήΘϤϛ ΎτΧ ΕΎόΑήϣ ςγϮΘϣ ϩί΍Ϊϧ΍ Ϫ̩ήϫ sy ΪϧϮηϲϣ ϪΒγΎΤϣ ήϳί ΕέϮλ ϪΑ ϥϮϴγή̳έ ΐϳ΍ήο Ωέϭ΁ήΑ

ˆ ˆ

br

eˆ

sx

aˆ  y  b x

ΪϧϮηϲϣ ϪΒγΎΤϣ ήҨί ΕέϮλ ϪΑ

Residual ΎτΧ έ΍ΪϘϣ Ωέϭ΁ήΑ

 yi  yˆ  y i  aˆ  bˆxi

Ϊϧ΍ϩΪϣ΁ ΖγΪΑ εϭέ ϦϴϤϫ ϪΑ ΰϴϧ ΎϫΩέϭ΁ήΑ Ϫϛ

Ζγ΍ ΎτΧ ΕΎόΑήϣ ωϮϤΠϣ ϥΩήϛ ϪϨϴϤϛ ϑΪϫ ΪϴϨϛ ϲσ ΍έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ΍ ϪΑ ϲγήΘγΩ ϱ΍ήΑ

΍έ ήϴϐΘϣ ϦϳΪϨ̩ ϞϘΘδϣ ήϴϐΘϣ ϥ΍ϮϨϋ ϪΑ Ϫϛ ϲΗέϮλ έΩ ΪϨ̩ ϥϮϴγή̳έ ϪΑ ρϮΑήϣ ΕΎΒγΎΤϣ spss ΪϴϨϛ Ωέ΍ϭ Ωήϛ Ϊϫ΍ϮΧ ϪΒγΎΤϣ ΍έ ϩήϴϐΘϣ

ΪϴϨϛ Ωέ΍ϭ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ dependent ΖϤδϗ έΩ ϱΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ independent ΖϤδϗ έΩ ΪϴϨϛ Ωέ΍ϭ ΍έ ϞϘΘδϣ ί΍ ϱέ΍ΪϘϣ ϭ ήϴϐΘϣ selection variable ΖϤδϗ έΩ Ωέ΍ϭ Ζγ΍ ΎϤη ήψϧ ΩέϮϣ ϲγέήΑ ϱ΍ήΑ Ϫϛ ϥ΁ ήϴϐΘϣ ϥ΍ϮϨϋ ϪΑ ϥϮϴγή̳έ έΩ ΪϳΎΒϧ ήϴϐΘϣ Ϧϳ΍ ΪϴϨϛ ΪηΎΑ ϪΘϓέ έΎϛ ϪΑ ϞϘΘδϣ ϥ΍ϮϨϋ ϪΑ Ϫϛ ϱήϴϐΘϣ Case lable ΖϤδϗ έΩ ΪϴϨϛ κΨθϣ Ζγ΍ ϪΘϓέ έΎϛ ϪΑ ΎϫΩέϮϣ ϲϣΎγ΍ ϪΑ ϲϫΩ ϥίϭ ήϴϐΘϣ WLS Wieght ΖϤδϗ έΩ Ζϓέ Ϊϫ΍ϮΧ έΎϛ ϪΑ βϧΎϳέ΍ϭ έ΍ΪϘϣ ϱίΎγ ϥΎδϜϳ ϱ΍ήΑ ϥίϭ Ϧϳ΍ ΪϴϨϛ Ωέ΍ϭ ΍έ ΎϫΩέϮϣ Ωέ΍Ω ΩϮΟϭ ϲΟϭήΧ έΩ ϱή̴ϳΩ ϱΎϫϩέΎϣ΁ ήϳΩΎϘϣ ζϳΎϤϧ ϥΎ

Ϝϣ΍ Statistic ϪϤϛΩ ΏΎΨΘϧ΍ ΎΑ Ωέ΍Ω ΩϮΟϭ ϥϮϴγή̳έ ϪϟΩΎόϣ ϲγέήΑ ϱΎϫέ΍ΩϮϤϧ ζϳΎϤϧ ϥΎϜϣ΍Plot ϪϤϛΩ ΏΎΨΘϧ΍ ΎΑ Ωέ΍ΪϧΎΘγ΍ ϩΪϧΎϣ ϲϗΎΑ ϭ ϪΘδΑ΍ϭ ήϴϐΘϣ ί΍ ϩΪη ϩΩέϭ΁ήΑ ήϳΩΎϘϣ ϱίΎγ ϩήϴΧΫ ϥΎϜϣ΍ Save ϪϤϛΩ ΎΑ Ωέ΍Ω ΩϮΟϭ ϲόϗ΍ϭ

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Analyze - Regression - Linear...

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H 0 : Ωέ΍Ϊϧ X ϪΑ ΖΒδϧ Y Ε΍ήϴϴϐΗέΩ ϱήϴΛΎΗϭ Ζδϴϧ ΐγΎϨϣ ϝΪϣ

H 1 : Ζγ΍ ΐγΎϨϣ ϥϮϴγή̳έ ϝΪϣ

ςϘϓ ή̴ҨΩ έΎΒ̰Ҩ ΍έ ϝΪϣ ΪҨέϭ΁ ΖγΪΑ car ϭ income ̵Ύϫ ήϴϐΘϣ ̵΍ήΑ ΍έ ϥϮϴγή̳έ ̶τΧ ϪϟΩΎόϣ ϝΎΜϣ ΪϴϨ̯ ΏΎδΣ Ύϫ ΩήΠϣ ̵΍ήΑ

Analyze - Regression - Linear...... Dependent……………car Independent………..income

Model Summary

Model 1

R .792(a)

R Square .627

Adjusted R Square .627

Std. Error of the Estimate 13.38386

Model 1

Sum of Squares

df

Mean Square

Regression

1930513.413

1

1930513.413

Residual

1146059.754

6398

179.128

F

Sig.

10777.296

.000(a)

Total

3076573.167 6399 a Predictors: (Constant), Household income in thousands b Dependent Variable: Price of primary vehicle Coefficients(a) Unstandardized Model Coefficients

1

(Constant) Household income in thousands

Standardized Coefficients

B 14.799

Std. Error .223

.221

.002

Beta

.792

t

Sig.

B 66.319

Std. Error .000

103.814

.000

a Dependent Variable: Price of primary vehicle

ϪΠϴΘϧ

ΪϨ̯ ̶ϣ ΏΎδΣ ΍έ ϥϮϴγή̳έ ̵ΎτΧ έ΍ΪϘϣ βϧΎҨέ΍ϭ ΰϴϟΎϧ΁ ϝϭΪΟ H0 : a  0 H0 : b  0 Ζγ΍ ϩΩ΍Ω ϡΎΠϧ΍ ΍έ ϥϮϣί΁ ϦҨ΍ ΐ΋΍ήο ί΍ ̮Ҩ ήϫ ̵΍ήΑ H1 : a  0 H1 : b  0 ΪϨ̯ ̶ϣ Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ β̡ p  value  0.000 ϥϮ̩ ̶ϣ έ΍ή̰Η ΍έ ϥϮϴγή̳έ ςΧ ϪΒγΎΤϣ ϞΣ΍ήϣ ΪόΑ ϢϴϨ̯ ̶ϣ ΍ΪΟ ΍έ martial ήϴϐΘϣ split file ΎΑ ϝϭ΍ Ύϫ ΩήΠϣ ̵΍ήΑ ϢϴϨ̯

(car )Y  14.799  0.221* X (income)

Coefficientsa

Marital status Unmarried

Married

Model 1

1

(Constant) Household income in thousands (Constant) Household income in thousands

Unstandardized Coefficients B Std. Error 14.755 .318 .223

.003

14.840

.314

.219

.003

Standardized Coefficients Beta .791

.794

t 46.468

Sig. .000

73.323

.000

47.327

.000

73.504

.000

a. Dependent Variable: Price of primary vehicle

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a Predictors: (Constant), Household income in thousands ANOVA(b)

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΍έ ϦϴηΎϣ ϪϨҨΰϫ έ΍ΪϘϣ x=73 Ϊϣ΁έΩ έ΍ΪϘϣ ̵΍ήΑ ϩΪϣ΁ ΖγΪΑ ϝΪϣ ϪΑ ϪΟϮΗ ΎΑ Ϣϴϫ΍ϮΧ ̶ϣ ΪϴϨ̯ νήϓ ΪηΎΑ ̶Ϥϧ Ύϣ ̵Ύϫ ϩΩ΍Ω ˯ΰΟ ϩΩ΍Ω ϦҨ΍ Ϫ̯ ̶ϟΎΣ έΩ ϢϴϨ̯ ΏΎδΣ ϢϴϨ̯ ̶ϣ Ωέ΍ϭ income ϥϮΘγ έΩ ϒҨΩέ ϦҨήΧ΁ Dataview ϪΤϔλ έΩ ΍έ ̀˼ ΩΪϋ ˺ ˻ Analyze - Regression - Linear...... Dependent……………car Independent………..income Save ……predicted value…..standardized



y=0.3208 ϒҨΩέ ϦҨήΧ΁ έΩ ZPR ϡΎϧ ϪΑ ΪҨΪΟ ϥϮΘγ έΩ ϩΪη ̶ϨϴΑ ζϴ̡ έ΍ΪϘϣ ϩΪϫΎθϣ ˼

Ύϫ ϩΪϧΎϣ ̶ϗΎΑ ϩΪϫΎθϣ ̵΍ήΑ ̊

Save ………Residuals ..…..standardized  ΩϮη ̶ϣ ϩΩ΍Ω ϥΎθϧ Dataview ϪΤϔλ έΩ Ύϫ ϩΪϧΎϤϴϗΎΑ ί΍ ̶ϧϮΘγ

ΖΑΎΛ έ΍ΪϘϣ ϥϭΪΑ ϥϮϴγή̳έ ςΧ ΐҨ΍ήο ϪΒγΎΤϣ

Analyze - Regression - Linear......option…..Include constant equation 

Coefficients(a,b) Unstandardized Coefficients Model 1

B Household income in thousands

Standardized Coefficients

t

Sig.

Beta

B

Std. Error

Std. Error .314

.002

.885

151.668

.000

a Dependent Variable: Price of primary vehicle b Linear Regression through the Origin

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Ε΍ήϴϴϐΗ ΚϋΎΑ ϥ΁ ̮̩Ϯ̯ ήϴϴϐΗ ̶ϨόҨ Ωέ΍Ω ̵ήΘθϴΑ ΖϴϤϫ΍ ήϴϐΘϣ ϡ΍Ϊ̯ ϩήϴϐΘϣ ΪϨ̩ ̶τΧ ϝΪϣ έΩ ϝ΍ϮΌγ ˮΩϮη ̶ϣ ϪϟΩΎόϣ ̱έΰΑ Y  a  b1 X 1  b2 X 2  ...  bk X k  

νϮϋ ΰϴϧ ΐҨήο ϢϴϨ̯ νϮϋ΍έΎϫ ήϴϐΘϣ ΪΣ΍ϭ ή̳΍ ϥϮ̩ Ζδϴϧ ΎϬϧ΁ ΖϴϤϫ΍ ϪϧΎθϧ ΐҨ΍ήο ̶̰̩Ϯ̯ ΎҨ ̶̳έΰΑ ΩϮη ̶ϣ ΖΤΗ Standardized Coefficients ϥϮΘγ ,Coefficients ϝϭΪΟ έΩ ϥ΁ ΐҨήο ΪηΎΑ ΪΣ΍ϭ ϥϭΪΑ ή̳΍ ήϴϐΘϣ ΪҨ΁ ̶Ϥϧ ϝΪϣ έΩ Ϫ̯ Ζγ΍ Ε΍ήϴϴϐΗ ΖϴϤϫ΍ ΐҨήο ϦҨ΍ ΩϮη ̶ϣ ΝέΩ Beta ϥ΍ϮϨϋ ή̳΍ Ϫ̯ Ζγ΍ ϩΪη ΝέΩ ϝϭΪΟ έΩ Ϣϫ Beta ̮Ҩ Ωέ΍Ω ΩϮΟϭ ϞϘΘδϣ ήϴϐΘϣ ̮Ҩ ςϘϓ Ϫ̯ ϻΎΑ ϝΎΜϣ έΩ ˱ϼΜϣ Ϊη ̶ϣ ϢϴδϘΗ ϒϠΘΨϣ ̵Ύϫ Beta ϪΑ ΪηΎΑ ήϴϐΘϣ ΪϨ̩

Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΨϴϣ Ϊϧ΍ ϪΘϓή̳ ϩί΍Ϊϧ΍ ΍έ Ϊϧ΍ ϩΩή̯ ϑΩΎμΗ Ϫ̯ ̶ϧΎ̳ΪϨϧ΍έ ϥϮΧ Ϟ̰ϟ΍ ϥ΍ΰϴϣ ϝΎΜϣ ΪϴϫΩ ϥΎθϧ έ΍ΩϮϤϧ ΎΑ ˮϪϧ ΎҨ Ζδϫ ΕΎϓΩΎμΗ Ω΍ΪόΗ ϭ Ϟ̰ϟ΍ ϥ΍ΰϴϣ ϦϴΑ ̶σΎΒΗέ΍ ΎҨ΁ ˺ ΪϴϨ̯ ΪϴҨΎΗ ΍έ ΕΎϴοήϓ ϭ ΪϴϨ̯ ΏΎδΣ ΍έ ήϴϐΘϣ ϭΩ ϦϴΑ ̶τΧ ̶̴ΘδΒϤϫ ΐҨήο ˻ Ω΍ΪόΗ ϥϮΧ Ϟ̰ϟ΍ ϒϠΘΨϣ ΡϮτγ ̵΍ήΑ ϭ ΪϴϨ̯ Ωέϭ΁ήΑ ΍έ ήϴϐΘϣ ϭΩ ϦϴΑ ̶τΧ ϪτΑ΍έ spss ί΍ ϩΩΎϔΘγ΍ ΎΑ ˼ ΪϴϨ̯ Ωέϭ΁ήΑ ΍έ ΕΎϓΩΎμΗ ˮΪϨϟΎϣήϧ ϊҨίϮΗ ̵΍έ΍Ω Ύϫ ϩΪϧΎϤϴϗΎΑ ΎҨ΁ ̊ ˮΪϨΘδϫ ̶ϓΩΎμΗ Ε΍ΪϫΎθϣΎҨ΁ ̋ ϥϮΧ Ϟ̰ϟ΍ ̋ ˺˹ ˺̋ ˻˹ ˻̋ ˼˹ ˼̋ ΕΎϓΩΎμΗ Ω΍ΪόΗ ˺˹ ˺̀ ˻̌ ˼˹ ˼˻ ˼́ ̊˻

˺Ώ΍ϮΟ

Ωέ΍Ω ΩϮΟϭ ̶τΧ ϪτΑ΍έ Ϊγέ ̶ϣ ήψϧ ϪΑ ϪΠϴΘϧ

Graph – Legacy dialogs – scatter plot– simple 50.00

accidentno

40.00

X Axis…..alckol Y Axis…..accidentno

Ζγ΍ ΕΎϓΩΎμΗ Ω΍ΪόΗ ϪΘδΑ΍ϭ ήϴϐΘϣ Y ϭ Ϟ̰ϟ΍ ϞϘΘδϣ ήϴϐΘϣ X

˻ Ώ΍ϮΟ

30.00

20.00

10.00 5.00

10.00

15.00

20.00

25.00

35.00

30.00

alckol

Correlations alckol alckol

Pearson Correlation

1

accidentno .984(**)

Sig. (2-tailed) N accidentno

Pearson Correlation Sig. (2-tailed) N

̶ϨόҨ ήϔλ νήϓ p  value  0.000 ΎΑ ΩϮη ̶ϣ Ωέ ϪϧϮϤϧ ςγϮΗ ήϴϐΘϣ ϭΩ ϝϼϘΘγ΍

.000 7

7

.984(**)

1

.000 7

7

ΖγϻΎΑ ̶̴ΘδΒϤϫ ϪϧΎθϧ Ϫ̯   0.984

** Correlation is significant at the 0.01 level (2-tailed).

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Analyze - Regression - Linear............ Dependent……………accidentno

Independent………..alckol

ANOVA(b)

Model 1

Regression Residual Total

Sum of Squares 740.571

df 1

Mean Square 740.571

24.286

5

4.857

764.857

6

F 152.471

Sig. .000(a)

a Predictors: (Constant), alckol b Dependent Variable: accidentno Coefficients(a)

Model 1

Standardized Coefficients

B 7.286

Std. Error 1.863

1.029 a Dependent Variable: accidentno

.083

(Constant) alckol

Beta .984

t

Sig.

B 3.912

Std. Error .011

12.348

.000

(accidentno )Y  7.286  1.029 * X ( alckol )

H0 : a  0 H1 : a  0

ϪΠϴΘϧ

H0 : b  0

H1 : b  0

Ζγ΍ ϩΩ΍Ω ϡΎΠϧ΍ ΍έ ϥϮϣί΁ ϦҨ΍ ΐ΋΍ήο ί΍ ̮Ҩ ήϫ ̵΍ήΑ βϧΎҨέ΍ϭ ΰϴϟΎϧ΁ ϝϭΪΟ έΩ ΪϨΘδϴϧ ήϔλ ΐҨ΍ήο ̶ϨόҨ ΪϨ̯ ̶ϣ Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ β̡ p  value  0.000 ϥϮ̩

Ύϫ ϩΪϧΎϤϴϗΎΑ ϥϮΘγ ΩΎΠҨ΍ ̵΍ήΑ ̊ Ώ΍ϮΟ

Analyze - Regression - Linear...... Dependent……………accidentnor

Independent………..alckol Save …… Residuals …..standardized



Runs Test 2 ̋Ώ΍ϮΟ Runs ϥϮϣί΁ ί΍ ϩΩΎϔΘγ΍ ΎΑ Test Value(a)

Standardized Residual .0000000

Cases < Test Value

5

Cases >= Test Value

2

Total Cases

7

Number of Runs

3

Z

-.380

Asymp. Sig. (2-tailed)

.704

p  value  0.704  0.05

̶ϓΩΎμΗ Ύϫ ϩΪϧΎϤϴϗΎΑ ϭ ΩϮη ̶Ϥϧ Ωέ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ β̡ Ϊϧ΍  N (0,1) Standard Residual Unstandard Residual  N (0,  2 ) a Mean

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Unstandardized Coefficients