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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.
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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.
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.