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OCT 15, 2019 Research paper

SHORT NOTES • Statistical data analysis is a process of performing numerous statistical functions involving collection of data, interpretation of data and lastly, validation of the data. • Statistics stated that the descriptive or summary statistics are used to summarize/describe the sample data and the inferential statistics are used to infer conclusions from the hypotheses framed.

“Statistics is the only science where the experts may come up with different conclusion with the same data” Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics |

STATISTICAL DATA ANALYSIS • Statistics are the branch of mathematics used to analyse the data that can describe, summarize and compare. • Statistical data analysis is a process of performing numerous statistical functions involving collection of data, interpretation of data and lastly, validation of the data. • Numerous statistical tools such as SAS, SPSS, STATA, etc., are available nowadays to analyse the statistical data from simple to complex problems based on the nature of the study. Statswork experts develop new tools and new approaches on improving the marketing for your business and predict future trends. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics |

TYPES OF STATISTICAL DATA ANALYSIS

Summary or Descriptive statistics

Inferential statistics

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SUMMARY OR DESCRIPTIVE STATISTICS Descriptive statistics are used to summarize data from a sample. Eg. Mean, Median, Standard deviation, Variance, etc.

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INFERENTIAL STATISTICS Inferential statistics are used to make conclusions from data through the null and alternative hypotheses that are subject to random variation. Simply, it can be stated that the descriptive or summary statistics are used to summarize/describe the sample data and the inferential statistics are used to infer conclusions from the hypotheses framed.

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USES OF STATISTICS

Provides a better understanding from the data and precise description of a state of art under study. Assist in presenting complex data in a appropriate tabular and graphical format for easy and clear knowledge of the sampled data. Assist in understanding the pattern and trends of variations in the sampled data.

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Assist in the appropriate and effective planning of a statistical analysis in any field of study. Helps to make valid inferences, by measuring the reliability parameters for the sampled data towards the population.

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MAJOR ROLE OF STATISTICS

Statistical analysis inmarket rese arch

Data analytics in Big data, Machine Learning and Deep learning, etc

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BI -Business intelligence

Financial and economic studies.

DATA MEAN IN STATISTICS 01

The nature of the data plays a vital role in the field of statistics.

In statistics, there are various kinds of data are available: Discrete data and continuous data are grouped as numerical, Categorical data involving nominal and ordinal

Mostly, every sampled data belong to any one of two groups: categorical or numerical and are described in the following table for easy understanding.

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DATA CLASSIFICATIONS 1.CATEGORIAL DATA Categorical Values/observations that can be grouped into categories with no natural ordering and having some sort of ordering. Table shows the gender of the respondents

2. NUMERICAL DATA Numerical Values/observations that can be measured and these numbers can be placed in ascending or descending order. Table shows the age of the respondents

Figure shows the percentage of gender of the respondents

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1.CATEGORIAL DATA CLASSIFICATIONS Nominal These are values/observations with no natural ordering. Examples: Gender, eye colour, etc.

Ordinal Values or observations put in order or ranked or containing a rating scale. You can order and count these variables but it cannot be measured. Example: Likert scale, etc

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2. NUMERICAL DATA CLASSIFICATIONS Discrete Values or observations that can be counted as separate and distinct. It can take only particular values. Examples: number of pens in a box; number of students in a class, etc.

Continuous Values that can be measured are considered as continuous data. They can be further divided into two types: Finite and Infinite. And, it can take any value from minus infinity to plus infinity. Examples: height, time and temperature.

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PMF (Probability Mass Function) and PDF (Probability Density Function) • In statistical data analysis, continuous data are scattered under continuous distribution function, also called as te PDF or Probability Density Function. • The discrete data are scattered under discrete distribution function, also called as PMF or Probability Mass Function. • The phrase ‘density’ is used for data in continuous form because density cannot be counted, but can be measured. Normal distribution, Poisson distribution, Binomial distribution, etc., are most commonly used distribution in statistical analysis. • Statistical data analysis are broadly classified into two types: Univariate and Multivariate. To analyse the data which contains only one variable, the univariate statistical analyses such as t-test, z-test, f test, one way ANOVA, etc., can be performed. • If the data contains two or more variables, multivariate techniques such as factor analysis, regression analysis, discriminant analysis, etc., can be performed depends on the nature of the study. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics |

STATISTICAL ANALYSIS Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics |

â&#x20AC;˘ t-TEST t-test analysis is a statistical model which compares the values in two different groups to determine when there is enough difference between the data.

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2.ANALYSIS OF VARIANCE Analysis of Variance (ANOVA) is a method utilised to decide whether the mean values of dependent variables remain constant when implemented in different groups which are independent of each other.

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3. Predictive Analytics – Predictive analytics in statistics uses predictive algorithms and ML techniques to define the probability of future results, behaviour, and patterns based on the existing data.

4. Causal Analysis – This type of analysis searches the data for the elementary reason to understand the causes.

5. Exploratory Data Analysis (EDA) – EDA is an alternative to inferential statistics, emphases on detecting general trends and patterns in the data and to track the strange associations. It is widely used by the data scientist to check the assumptions of the hypotheses, to detect outliers, to handle missing data, etc.

6. Mechanistic Analysis – This kind of analysis is not a usual type of statistical analysis. Though, it is worth stating here because, it is used in the big data analysis in some industries, it has an important role in this big data era.

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SUMMARY Primary step involves the identification of nature of the data to be analysed. Secondly, explore the association between data and underlying population in study. Build a suitable model to summarize the data and proceed for further analysis.

Check the validity of the model and take decisions about the hypotheses.

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