VL1

Describe the simple linear regression model. Also define the relationship of the underlying variables.

What is the ceteris paribus definition and why is it important?

How does the ceteris paribus definition work in the linear regression model?

What is an important assumption for causality?

Describe the mean independence of error term.

Briefly explain the second assumption for causality?

Describe the population regression function.

Overview: Expected Values and Errors.

Overview: Fitted Values and Residuals.

What is Ordinary Least Square (OLS)?

Overview: Calculation of OLS Estimators.

Describe the algebraic properties for OLS and the impact on the b0(hat) and b1(hat).

What is the difference between Errors and Residuals?

Explain the basic goodness-of-fit formulas.

What does R-squared describe?

What are linear and non-linear transformations?

What are Log-Transformation in linear regressions?

Why do we log-transform variables?

How do we interpret a log transformed independent variables effect on the dependent variable?

How do we interpret Log-level models (logged dependent variable, unlogged independent variable)?

How do we interpret log-log models?

Log table overview

Why are log variables useful?

What are limitations of log variables?

How do we interpret quadratic terms?