You are given the following regression model that relates a firm’s revenue (REV)

You are given the following regression model that relates a firm’s revenue (REV) to advertising expenditure (ADV), price of its product (PRICE), and an error term:
REV = β0 + β1ADV + β2PRICE + ε
You are provided a dataset of 100 observations for the variables REV, ADV, and PRICE.
Part A:
Estimate the parameters β0, β1, and β2 using OLS regression. Interpret each parameter estimate.
Construct 95% confidence intervals for each parameter estimate.
Conduct hypothesis tests for each parameter at the 5% significance level. Clearly state your null and alternative hypotheses and conclude whether to reject or fail to reject the null based on your test results.
Calculate and interpret the R-squared statistic.
Determine whether multicollinearity is a problem based on examination of correlations between independent variables and calculation of variance inflation factors.