obtain a multiple linear regression model for your data set which meets the requirements:

R^2 >= 0.8, F significance < 0.01, all p-values < 0.05, at least 3 remaining independent variables.

Present a table with regression forecasts for your data and calculate the Mean Squared Error. Once you have the multiple linear regression model, you should check for MultiCollinearity. Comment on possible MultiCollinearity among all model variables using the correlation threshold of 0.7.

PART 2. Find a data set with at least 4 years of quarterly data. This set is different from the one in PART 1.

The multiple linear regression model based on these data should include 1 dependent variable and 4 independent variables: time period t and dummy variables Q1, Q2, Q3