# Statistics

Mini-Project 2 requirements.
PART 1. Find a data set with 15-50 rows and at least 8 columns of data. Describe the variables in the columns (what they represent) and identify them as dependent/independent/dummy variables.
The multiple linear regression model based on these data should include 1 dependent variable and at least 7 independent variables (including at least 2 dummy variables) (similar to the multiple linear regression models in the three posted examples of Mini-Project 2).
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. 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 (similar to the multiple linear regression regression model in Table 8.12 or problem 8.24 part d in Chapter 8).
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.
There is a penalty of 10% for submitting the project after the 12/6 due date and there will deductions for not meeting the above requirements

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