Model Diagnostics and Remedial Measures

Model Diagnostics and Remedial Measures

Instructor: Kiah Ong

What you'll learn

Describe the assumptions of the linear regression models.
Use diagnostic plots to detect violations of the assumptions of a linear regression model.
Perform variable selections and model validations.

Details to know

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Assessments

11 assignments

Taught in English

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There are 3 modules in this course

This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.

This course is part of the Performance Based Admission courses for the Data Science program. In this course, we will learn what happens to our regression model when these assumptions have not been met. How can we detect these discrepancies in model assumptions and how do we remediate the problems will be addressed in this course. Upon successful completion of this course, you will be able to: -describe the assumptions of the linear regression models. -use diagnostic plots to detect violations of the assumptions of a linear regression model. -perform a transformation of variables in building regression models. -use suitable tools to detect and remove heteroscedastic errors. -use suitable tools to remediate autocorrelation. -use suitable tools to remediate collinear data. -perform variable selections and model validations.

Welcome to Model Diagnostics and Remediation Measures! In this course, we will cover the topics of: Regression Diagnostics, Variance Stabilizing Transformations, Box-Cox Transformation, Transformations to Linearized the Model, Weighted Least Squares, Autocorrelation, Multicollinearity, Variable Selection and Model Validation. In Module 1, we will cover four topics including: Regression Diagnostics, Variance Stabilizing Transformations, Box-Cox Transformation and Transformations to Linearize the model. There is a lot to read, watch, and consume in this module so, let’s get started!

What's included

9 videos6 readings5 assignments1 discussion prompt

9 videosTotal 61 minutes

Instructor Welcome and Course Overview 0 minutesPreview module
Module 1 Introduction1 minute
Regression Diagnostics Part 16 minutes
Regression Diagnostics Part 23 minutes
Regression Diagnostics Part 310 minutes
Variance-Stabilizing Transformation Part 110 minutes
Variance-Stabilizing Transformation Part 210 minutes
Box-Cox Transformation7 minutes
Transformations to Linearize the Model10 minutes

6 readingsTotal 120 minutes

Syllabus10 minutes
Video 14 Slides - Regression Diagnostics (pdf)30 minutes
Video 15 Slides - Variance-Stabilizing Transformation30 minutes
Video 16 Slides - Box-Cox Transformation30 minutes
Video 17 Slides - Transformations to Linearize the Model (pdf)10 minutes
Module 1 Summary10 minutes

5 assignmentsTotal 300 minutes

Module 1 Summative Assessment180 minutes
Regression Diagnostics30 minutes
Variance-Stabilizing Transformation30 minutes
Box-Cox Transformation30 minutes
Transformations to Linearize the Model 30 minutes

1 discussion promptTotal 10 minutes

Meet and Greet Discussion10 minutes

Welcome to Module 2 – This module will cover four topics including: Weighted Least Squares, Autocorrelation, Multicollinearity, and Variable Selection and Model Validation. There is a lot to read, watch, and consume in this module so, let’s get started!