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Case Study: Predicting Job Satisfaction - A Statistical Analysis, Assignments of Mathematics

This case study explores the relationship between various variables and job satisfaction. It utilizes statistical methods like regression analysis to identify predictors of job satisfaction and develop a mathematical model for prediction. The study examines the significance of teamwork, work environment, and hours worked per week in influencing job satisfaction. It also delves into the interpretation of regression results and the development of a predictive model.

Typology: Assignments

2023/2024

Available from 12/18/2024

Milestonee
Milestonee 🇺🇸

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Week 8: Case Study (graded)
Post 1:
Case Study Q1: Several variables are presented that may be related to job satisfaction.
Which variables are stronger predictors of job satisfaction? Might other variables not
mentioned here be related to job satisfaction?
Research shows that teamwork is positively related to job satisfaction. For example, Kruse
(1986) investigated the relationship of teamwork and job satisfaction among county staff.
There are two variables that do not appear to be good predictors of job satisfaction.
1. Overall quality of work environment: t- test static value is -.252 and p value
associated with t test is also very high (0.805).
2. Total hour worked per week: t- test static value is -.22 and p value associated with t
test is also very high (0.829).
Kruse, S. K. (1986). An analysis of job characteristics, leadership, teamwork, and job satisfaction
in the cooperative extension service. Dissertation Abstracts International, 224. (UMI No.
8627126).
Post 2:
In regard to the Discussion Question: To what degree can someone depend on the results of
the regression analysis? Why?
The regression hypothesis is given by
The null hypothesis is
H0: There is no statistically significant relationship between dependent variable and
independent variables .
The alternative hypothesis is
Ha: There is a statistically significant relationship between dependent variable and
independent variables .
ANOVA table provides us model fit or predictive power (estimate) of independent variable on
dependent variable.
From the ANOVA table we can observe p value associated with F test is less than 0.05(
p=0.000). Hence we reject null hypothesis and conclude that there is a statistically significant
relationship between dependent variable and independent variables.
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Week 8: Case Study (graded) Post 1: Case Study Q1: Several variables are presented that may be related to job satisfaction. Which variables are stronger predictors of job satisfaction? Might other variables not mentioned here be related to job satisfaction? Research shows that teamwork is positively related to job satisfaction. For example, Kruse (1986) investigated the relationship of teamwork and job satisfaction among county staff. There are two variables that do not appear to be good predictors of job satisfaction.

  1. Overall quality of work environment: t- test static value is -.252 and p value associated with t test is also very high (0.805).
  2. Total hour worked per week: t- test static value is -.22 and p value associated with t test is also very high (0.829). Kruse, S. K. (1986). An analysis of job characteristics, leadership, teamwork, and job satisfaction in the cooperative extension service. Dissertation Abstracts International, 224. (UMI No. 8627126). Post 2: In regard to the Discussion Question: To what degree can someone depend on the results of the regression analysis? Why? The regression hypothesis is given by The null hypothesis is
  • H0: There is no statistically significant relationship between dependent variable and independent variables. The alternative hypothesis is
  • Ha: There is a statistically significant relationship between dependent variable and independent variables. ANOVA table provides us model fit or predictive power (estimate) of independent variable on dependent variable. From the ANOVA table we can observe p value associated with F test is less than 0.05( p=0.000). Hence we reject null hypothesis and conclude that there is a statistically significant relationship between dependent variable and independent variables.

Post 3: Case Study Q2: Is it possible to develop a mathematical model to predict job satisfaction using the data given? If so, how strong is the model? With four independent variables, will we need to develop four different simple regression models and compare their results? From the coefficient table, The canonical form of regression line is Where Y = job satisfaction (Dependent variable) X1, X2, X3, and X 4 are independent variables. B0 = Y intercept B1, B2, B3, and B4 are slope of the four predictors.