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Module 2 - Regression.md

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Module Introduction

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

Learning Objectives

  • To understand the basics of regression.
  • To apply Simple and Multiple, Linear and Non-Linear Regression on a data set for estimation.

Simple Linear Regression

Simple Linear Regression

Multiple Linear Regression

Multiple Linear Regression

Polynomial Regression

Polynomial Regression

Non-linear Regression

Non-linear Regression

Module 2 - Graded Quiz

Question 1: Multiple Linear Regression is appropriate for:

  • A. [X] Predicting tomorrow's rainfall amount based on the wind speed and temperature.
  • B. [ ] Predicting the sales amount based on month.
  • C. [ ] Predicting whether a drug is effective for a patient based on their characteristics.

Question 2: Which of the following is the meaning of "Out of Sample Accuracy" in the context of evaluation of models?

  • A. [X] "Out of Sample Accuracy" is the percentage of correct predictions that the model makes on data that the model has NOT been trained on.
  • B. [ ] "Out of Sample Accuracy" is the accuracy of an overly trained model (which may capture noise and produce a non-generalized model).

Question 3: When should we use Multiple Linear Regression? (Select all that apply)

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

Question 4: Which of the following options is TRUE about Polynomial Regression?

  • A. [ ] Polynomial regression can use the same mechanism as Multiple Linear Regression to find the parameters.
  • B. [ ] Polynomial regression models can fit using the method of Least Square method.
  • C. [ ] Polynomial regression fits a curve line to your data.
  • D. [X] All of the above

Question 5: Which sentence is NOT TRUE about Non-linear Regression?

  • A. [X] Non-linear regression must have more than one dependent variable.
  • B. [ ] Non-linear regression is a method to model non-linear relationship between the dependent variable and a set of independent variables.
  • C. [ ] For a model to be considered non-linear, it must be a non-linear function of the parameters.