Analysis of variance

Analysis of variance The statistical anyalysis from the EMG data can be observed on table

below and

analyzed by using Minitab statistical software package ( ANOVA). we can see that the main effects of the two factor taking break and higth table were no significant which is further supported by the fact that the P-Values of these two factors were more than 0.05. Moreover, the interactions effect between the factors are no significant on EMG based since P- values are more than 0.05 .

For this reason ,they have been includein aregression model to build a matmematical formulation between two factor and EMG.The Predicted R-Squared value of zero which is not reasonable agreement with the Adjusted R Squared value of zero that means the model explains none of the variability of the response data around its mean .

After the analysis of variance, we studied the regression model that as obtained from Minitab software. The regression model equation was found to be as given below: EMG = -0.000048 + 0.000002 A + 0.000000 B - 0.000000 A*B From the above equations, we cannot predict the EMG for all treatment combination. It can be noticed that the coefficients of the factors in the equation are zero which there are no relation between EMG and high of table and break factor. . From regression equation, we can see a bad fit of the model. overally, no interaction effect is present in the musical 1.

Table (

) : Analysis of Variance

Source Model Linear A B 2-Way Interactions A*B Error Total

DF 3 2 1 1 1 1 8 11

Adj SS 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Adj MS 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

F-Value 0.34 0.50 1.00 0.01 0.00 0.00

P-Value 0.800 0.622 0.346 0.935 0.973 0.973

Model Summary S 0.0000065

R-sq 11.21%

R-sq(adj) 0.00%

R-sq(pred) 0.00%

Coded Coefficients Term Constant A B A*B

Effect 0.000004 0.000000 -0.000000

Coef -0.000048 0.000002 0.000000 -0.000000

SE Coef 0.000002 0.000002 0.000002 0.000002

T-Value -25.68 1.00 0.08 -0.04

P-Value 0.000 0.346 0.935 0.973

VIF 1.00 1.00 1.00

Regression Equation in Uncoded Units EMG = -0.000048 + 0.000002 A + 0.000000 B - 0.000000 A*B

Plotting our date with their residuals we can see a bad fit of the model. The graph follow pattern and it is not scattered all over the place which is a bad signal Versus Fits

(response is EMG) 0.000010

Residual

0.000005

0.000000

-0.000005

-0.000010 -0.000050

-0.000049

-0.000048

Fitted Value

Figure ( ) : Residuals plot

-0.000047

-0.000046

Conclusions: From the Analysis of Variance table (ANOVE), we can conclude that: •

The assessment of muscle activation patterns revealed no significant effect in level of table , brak time factors and Emg which show nor support effect for any muscle,

•

.The model is not significant. The level of table and beak time does not have effect on EMG.

•

The analysis regreastion shows that the model can not be predicted by initial levels of muscle and EMG.

•

Analysis of anove showed that the collect of date was not precision which was resulate a bad model. .

General discussion: •

The results of anove showed that there was no an obvious increase in the neck fatigue ratings after the experiment for each tasks While The subjects were felt the neck fatigue ratings through the experiment. that may due to a lack of experience of participants to do in the experimental study.

•

lower performing of participants leads to lower fatigue in the neck and shoulder region, partly supported by EMG which gave us bad results

•

It can be concluded that failing of experiment was because of an incorrect muscle

location (see figer

)

Recommendations for future research: Therefore in the future, it is possible to develop a quantitative model for predicting time to muscle and mental fatigue after repeating experiment, which would be potentially applicable to the management of fatigue.