Home > Regression Anlysis > Multiple linear regression
Module: Multiple linear regression
Topic: Python Fundementals
- Lesson: Introduction
- Lesson: Model Assumptions
- Lesson: Parameter estimation
- Lesson: Inferences about the model
- Lesson: Predictions
- Lesson: Interpretations of regression coefficients
- Lesson: Varaible selection methods
- Lesson: Evaluating model adequacy
- Lesson: Use of categorical variables as predictors
- Lesson: Multiocollinearity remedies
Lesson 1
Introduction
- Self Learning Duration
- 30 mins
- Lecture Duration
- 20 mins
Self learning content
Lecture content
Introduction to multiple linear regression and when and where to use it.
Lab and tutorials
None
Lesson 2
Model Assumptions
- Self Learning Duration
- 30 mins
- Lecture Duration
- 15 mins
Self learning content
Lecture content
Lab and tutorials
None
Lesson 3
Parameter estimation
- Self Learning Duration
- 30 mins
- Lecture Duration
- 30 mins
Self learning content
Lecture content
Theortical Estimation along with Practical Example.
Lab and tutorials
None
Lesson 4
Inferences about the model
- Self Learning Duration
- 30 mins
- Lecture Duration
- 60 mins
Self learning content
Lecture content
How to use hypothesis testing and confidence intervals in multiple regression to estimate parameters, small introduction to ANOVA (without going deeper in to the mathematical side)
Lab and tutorials
None
Lesson 5
Predictions
- Self Learning Duration
- 30 mins
- Lecture Duration
- 30 mins
Self learning content
Lecture content
Practical Example on how to do a prediction using multiple linear regression.
Lab and tutorials
None
Lesson 6
Interpretations of regression coefficients
- Self Learning Duration
- 30 mins
- Lecture Duration
- 120 mins
Self learning content
Lecture content
How to interpret the multiple regression estimates.
Lab and tutorials
Lesson 7
Varaible selection methods
- Self Learning Duration
- 30 mins
- Lecture Duration
- 120 mins
Self learning content
Lecture content
How to interpret the multiple regression estimates.
Lab and tutorials
None
Lesson 8
Evaluating model adequacy
- Self Learning Duration
- 30 mins
- Lecture Duration
- 30 mins
Self learning content
Lecture content
How to use R2, MSE and Cp Statistics to evaluate models
Lab and tutorials
None
Lesson 9
Use of categorical variables as predictors
- Self Learning Duration
- 30 mins
- Lecture Duration
- 30 mins
Self learning content
Lecture content
How to deal when there is a categorical variable among the predictors.
Lab and tutorials
None
Lesson 10
Multiocollinearity remedies
- Self Learning Duration
- 30 mins
- Lecture Duration
- 30 mins
Self learning content
Lecture content
What is multicollinearity and how to overcome it (use an example).
Lab and tutorials
Get a suitable dataset from kaggle and build a Multiple regression model to slove a specific problem. (Case study)