Run two regressions in Excel using the provided Excel file “Layoffs”. The Excel file Layoffs provides data on 50 manufacturing workers who lost their jobs due to layoffs. The data includes the following list of variables: Weeks – the number of weeks a manufacturing worker has been without a job Age – the age of the worker Education – the number of years of education of the worker Married – a dummy variable, equal to 1 if the worker is married, 0 otherwise Head – a dummy variable, equal to 1 if the worker is a head of household, 0 otherwise Tenure – the number of years on the previous job Manager – a dummy variable, equal to 1 if the worker had a management occupation, 0 otherwise Sales – a dummy variable, equal to 1 if the worker had an occupation in sales, 0 otherwise 1. Run a simple regression with a dependent variable Weeks and an independent variable Age. Create the regular and standardized residual plots for the simple regression. 2. Run a multiple regression with a dependent variable Weeks and the following set of independent variables Age, Married, Head, Manager, Sales. Create the regular and standardized residual plots for the multiple regression.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Part I. Run two regressions in Excel using the provided Excel file “Layoffs”.
The Excel file Layoffs provides data on 50 manufacturing workers who lost their jobs due to layoffs. The
data includes the following list of variables:
Weeks – the number of weeks a manufacturing worker has been without a job
Age – the age of the worker
Education – the number of years of education of the worker
Married – a dummy variable, equal to 1 if the worker is married, 0 otherwise
Head – a dummy variable, equal to 1 if the worker is a head of household, 0 otherwise
Tenure – the number of years on the previous job
Manager – a dummy variable, equal to 1 if the worker had a management occupation, 0 otherwise Sales – a dummy variable, equal to 1 if the worker had an occupation in sales, 0 otherwise
1. Run a simple regression with a dependent variable Weeks and an independent variable Age. Create the regular and standardized residual plots for the simple regression.
2. Run a multiple regression with a dependent variable Weeks and the following set of independent variables Age, Married, Head, Manager, Sales. Create the regular and standardized residual plots for the multiple regression.
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