Please complete the following problems and show work, please and thank you.
The "cars" data include demand for passenger cars in the United States from 1971 to 1986. Estimate the following model using the "cars" data attached:
ln Yt =B0+B1lnX1t+B2lnX2t+B3lnX3t+B4lnX4t+B5lnX5t+t
Yt = the demand for passenger cars (years ranging from 1971 to 1986)
X1t = new cars consumer price index
X2t = consumer price index
X3t = personal disposable income (measured in billions of dollars)
X4t = interest rate
X5t = employed civilian labor force (measured in thousands of individuals)
First, please estimate, examine, and interpret the model. Put emphasis on the model's coefficient of determination, mean squared error, and parameter coefficients. Finally, is there multicollinearity in this problem? How do you know?
Explore the "grade" data using attached. Estimate a model that examines the relationship between grade and drinks consumed per week (drinks) and hours studied per week (hours). Please interpret your results w/ emphasis on the model's coefficient of determination, mean squared error, and parameter coefficients.
What are the main sources of bias in regression analysis as it relates to model estimation and specification? What can be done to address each form of bias w/ an emphasis on the advantages and disadvantages of each approach, if applicable?
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