# Linear Regression

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a) Develop a Multiple Regression model with the dependent variable being the Overall Job Satisfaction. Use gender, age, department, position and tenure with company as the independent variables. Comment on your analysis (Rsq, Rsq adj, etc). You are to provide not only the output of the regression procedure but also the model equation.

b) Develop a Linear Regression model using intrinsic job satisfaction and extrinsic job satisfaction. You can decide which you want to make the dependent variable and which the independent. Comment on your analysis (Rsq, Rsq adj, etc.). You are to provide not only the output of the regression procedure but also the model equation.

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Using the database

a) Develop a Multiple Regression model with the dependent variable being the Overall Job Satisfaction. Use gender, age, department, position and tenure with company as the independent variables. Comment on your analysis (Rsq, Rsq adj, etc). You are to provide not only the output of the regression procedure but also the model equation.

b) Develop a Linear Regression model using intrinsic job satisfaction and extrinsic job satisfaction. You can decide which you want to make the dependent variable and which the independent. Comment on your analysis (Rsq, Rsq adj, etc.). You are to provide not only the output of the regression procedure but also the model equation.

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a) Develop a Multiple Regression model with the dependent variable being the Overall Job Satisfaction. Use gender, age, department, position and tenure with company as the independent variables. (Rsq, Rsq adj, etc). Show output of your procedure.

In ordinary least square regression, we should notice the setting of the variables. We cannot directly regress Satisfaction over gender, age, department, position and tenure. Because they are grouped into several categories, the numbers "1,2,3" are not numerical, but simply indicators of subgroups. Therefore, we need to create some dummy variables.

We generate:

Male = 1 for males, and =0 for females

AGE21 = 1 for age 21 and under, = 0 otherwise

AGE2249 = 1 for age 22-49, = 0 otherwise

HR = 1 for department of Human Resources, = 0 otherwise

IT = 1 for department of Information Technology, = 0 otherwise

POSITION = 1 for Hourly Employee, = 0 otherwise

TENURE2 = 1 for tenure less than 2 years, = 0 otherwise

TENURE25 = 1 for tenure 2 to 5 years, = 0 otherwise

Then we run regression and ...

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