In this module, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.

Learning Objectives

• Demonstrate understanding of the basics of regression.
• Demonstrate understanding of simple linear regression.
• Describe approaches for evaluating regression models.
• Describe evaluation metrics for determining accuracy of regression models.
• Demonstrate understanding of multiple linear regression.
• Demonstrate understanding of non-linear regression.
• Apply Simple and Multiple, Linear and Non-Linear Regression on a dataset for estimation.

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• Linear Regression

  • Lab 2-1-1: Simple Linear Regression
  • Lab 2-1-2: Multiple Linear Regression

• Non-Linear Regression

  • Lab 2-2-1: Polynomial Regression
  • Lab 2-2-2: Non-linear Regression

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Quiz: Regression

Regression

TOTAL POINTS 9

Question 1

Which of the following is the meaning of "Out of Sample Accuracy" in the context of evaluation of models?

• "Out of Sample Accuracy" is the percentage of correct predictions that the model makes on data that the model has NOT been trained on.
• "Out of Sample Accuracy" is the accuracy of an overly trained model (which may captured noise and produced a non-generalized model)

Correct

Question 2

When should we use Multiple Linear Regression?

• When there are multiple dependent variables
• When we would like to predict impacts of changes in independent variables on a dependent variable.
• When we would like to identify the strength of the effect that the independent variables have on a dependent variable.

Correct

Question 3

Which sentences are TRUE about Non-linear Regression?

• Nonlinear regression is a method to model non linear relationship between the dependent variable and a set of independent variables.
• For a model to be considered non-linear, y must be a non-linear function of the parameters.
• Non-linear regression must have more than one dependent variable.

Correct