Using this dataset use R or R studio to answer these.

1.1. Access the Salaries dataset by using the code: library(car); library(carData); data(Salaries)

1.6. Run a linear regression model that predicts salary (DV: salary) based on the number of service years (IV: yrs.service). And interpret your results of the above regression analysis. Hint: Focus on (1) estimated coefficient and (2) R-squared.

1.7. If a new professor who has 10-year experience of service joins ISU next semester, then your prediction based on the model of her/his salary is...?

1.8. Run a linear regression model (model.1) that predicts salary (DV: salary) based on the following independent variables, yrs.since.phd, yrs.service, sex, rank, discipline. And interpret your results of the above regression analysis. Hint: Focus on (1) estimated coefficients, (2) R-squared, and (3) Adjusted R-squared.

1.9. Check "multicollinearity" in the model.1 by using Variance Inflation Factor (VIF).

1.10. Run a linear regression model (model.2) by dropping a variable whose VIF or GVIF is the highest from model.1. And interpret your results of the above regression analysis. Hint: Focus on (1) estimated coefficients, (2) R-squared, and (3) Adjusted R-squared.

1.11. Compare the two models (i.e., model.1 and model.2) by using (1) R-squared & Adjusted R-squared, (2) visualization, and (3) formal statistical test.