REGRESSION ANALYSIS

Regression Analysis

Abstract

The regression analysis will be used to analyze the job satisfaction among the employees and for that purpose, different variables like intrinsic satisfaction, extrinsic satisfaction and overall satisfaction will be regressed against the benefits of the employees. The regression with the more significant variables will be applied for the policy making by the manager.

Regression Analysis

Introduction

Regression analysis is the most important tool that is used in statistics for predicting future results. The purpose of the study is to find out the job satisfaction among the employees depending on various factors through regression analysis and to find the best strategy that should be used to maximize the job satisfaction.

Chosen Variables

The Intrinsic, Extrinsic and overall job satisfaction has been taken as a dependent variable in different regressions and Benefit has been taken as the independent variable in all the regressions. The reason of choosing the benefit as an independent variable is that the amount of benefit given to the employee always helps to increase or decrease the job satisfactions.

Difference in variable types

The difference between qualitative and quantitative data is that the qualitative data is the type for which data collection is not possible in numbers i.e. in an interview of a survey one cannot measure the behavior of a person therefore, qualitative data collection technique will be used. Similarly, quantitative data consists of numbers for which can be measured numerically. The mode is considered as the best statistics for qualitative data because though it cannot be measured numerically but it can be calculated that how many of the people shows different behaviors.

Descriptive statistics: Qualitative and Quantitative variables

Gender

Job Satisfaction

Intrinsic

Extrinsic

Benefits

Mean

Mean

Mean

Mean

Mean

1.415457211

5.039117996

4.90650687

5.246070253

4.7021529

Median

Median

Median

Median

Median

1

5.263095238

5.2

5.6

5.094047619

Mode

Mode

Mode

Mode

Mode

1

5.5

5.2

5.6

6.2

Standard Deviation

Standard Deviation

Standard Deviation

Standard Deviation

Standard Deviation

0.576059565

1.058881602

1.152975497

1.082312746

1.503070775

Explanation of descriptive statistics

The results of the descriptive statistics show some important results to be analyzed. First is that the best descriptive for qualitative data is mode that we can clearly see that mean and standard deviations i.e. 1.41 and 0.57 respectively for genders does not make any sense. The quantitative data on the other hand give appropriate results in all the descriptive methods which can be seen in the above table. The standard deviations for all the quantitative variables lies in between the range of 1 which shows that the employees are mostly satisfied with the benefits given to them and because of which their job satisfaction has been increased. The means of the quantitative variables also have an average value ranging from 4 to 5 showing that the employees are satisfied with their jobs and benefits given to them.

TEST #1: Regression Analysis- Benefits & Intrinsic

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

3.976233

0.378672

10.50045

0

Benefits

0.230225

0.075709

3.040923

0.0041

R-squared

0.187771

F-statistic

9.247211

 

Adjusted R-squared

0.167466

Prob(F-statistic)

0.004149

 

R-square

0.187771

 

 

 

Description of Chart The results of the regression analysis keeping intrinsic job satisfaction as a dependent variable on benefits obtained from the job shows an overall significant model because of highly significant f - statistic because the significance level is less than 0.05. The model has both significant coefficients i.e. constant and the coefficient of benefits because of significant t - value. The model results show that intrinsic job satisfaction is almost rated to be 4 ...