RM-Tools for statistical analysis

1)      ANOVA: ANOVA can be uses to examine differences among the means of several different groups at once. It is a statistical technique for assessing how nominal independent variables influence a continuous dependent variable.

2)      Correlation analysis: The correlation is the study of finding the relationship between the variables. If there are only 2 variables in the study of correlations there it is called simple correlation. Otherwise the study will be in either partial or multiple correlations. In this study the simple inter-correlations analysis is performed between the selected variables and the results are presented in the form of correlation matrix. Further the significance of correlation was tested for is significance at 5% level of significance.

3)      Multiple regression analysis: The multiple regressions analysis is a functional relationship between a dependent variable and a set of independent variables. In this section the results of multiple regressions analysis is presented between the dependent variable and other independent variables.

4)      Chi square analysis: The Chi square test is used in any study on social science and management for testing the independence of two attributes. Each of the Personal factors is compared with the study fact and chi square test is applied and describes the results in terms of personal factors, chi-square values (c2), p values and their significance(S/NS) on the factor studied and the results are presented with suitable hypothesis and relevant interpretations.

5)      Average Score analysis: The Average score analysis is mainly used in any study is to assess the level of opinion/awareness/satisfaction of the different category of respondents on the various aspects relating to the study. First the opinion of the respondents are assessed through a scaling technique and then based on the consolidated opinion of the respondents, the average score is calculated.

6)  Percentage Analysis: It is the simple and common method to represent raw streams of data as a percentage for better understanding of collected data. Percentages are used in making comparison between two or more variables to find the efficacy of each variable.