Statistics: Multiple Linear Regression

Learning Objectives

After completing this pre-class module, you should be able to:

1) State the difference between simple linear regression and multiple linear regression.

2) Discuss why multiple linear regression may be preferable or necessary relative to simple linear regression.

3) Express mathematically the multiple linear regression model.

4) Define multicollinearity and how it is investigated.


Title: V39 – Multiple Linear Regression (Introduction & Multicollinearity)

Summary: This video serves as a very brief introduction to multiple linear regression (MLR), including its mathematical model and comparison to simple linear regression. The problematic issue of multicollinearity, or correlations between the independent variables in a MLR, is discussed and assessed.

Learning Objectives

1) State the difference between simple linear regression and multiple linear regression.

2) Discuss why multiple linear regression may be preferable or necessary relative to simple linear regression.

3) Express mathematically the multiple linear regression model.

4) Define multicollinearity and how it is investigated.



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