Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable.
An explanatory variable (X) is a type of independent variable. The two terms are often used interchangeably. But there is a subtle difference between the two. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’s an explanatory variable.
An explained variable (Y) is a type of dependent variable. It is the variable in which we are interested. The effects of the explanatory variables are present in the explained variable.
Ho: X is not a significant factor in explaining Y
Ha: X is a significant factor in explaining Y
P Value — This is the probability of making a Type I error.
Rejection rule — If P <= alpha, we reject the Ho.
How to Solve the Case Study
- Which factors are significant in predicting wages?
- How much explanatory power do these factors have together?
- Of those factors that are significant, which has the highest impact on wages?
- Use those factors to predict the wages for each of the following individuals
- Joe is a 48 year old married man with 22 years education, 25 years experience, and lives in Louisiana.
- Monica is a 50 year old single woman with 18 years education, 35 years experience, and lives in Texas.
- Jamal is a 58 year old married man with 16 years education, 40 years experience, and lives in NYC.
- Izzy is a 35 year old married man with 12 years education, 20 years experience, and lives in Florida.
- Input your own stats and see what your wages would be.