# Mean Squared Error and multiple linear regression

PART 1. Find a data set with 15-50 rows and at least 8 columns of data (based on Chapter 7 regression models).
The columns should include 1 dependent and at least 7 independent variables (including at least 2 dummy variables). Describe the variables (what they represent) and identify them as dependent/independent/dummy variables. You should consult the three posted examples of Mini-Project 2 which are similar to Part 1.

THE GOAL: 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 (based on Chapter 8 regression models).
The model should include 1 dependent variable and 4 independent variables: time period t and dummy variables Q1, Q2, Q3.

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

R^2 >= 0.9, F significance < 0.01, all 4 independent variables have p-values < 0.05.

Present a table with regression forecasts for your data and calculate the Mean Squared Error.

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