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Model Diagnostics and Remedial Measures

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Model Diagnostics and Remedial Measures

This course is part of Advanced Statistical Techniques for Data Science Specialization

Instructor: Kiah Ong

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

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.

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Advanced Statistical Techniques for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 1 minute
Module 1 Introduction1 minute
Regression Diagnostics Part 16 minutes
Regression Diagnostics Part 24 minutes
Regression Diagnostics Part 310 minutes
Variance-Stabilizing Transformation Part 111 minutes
Variance-Stabilizing Transformation Part 210 minutes
Box-Cox Transformation8 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

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

What's included

12 videos6 readings5 assignments

12 videosTotal 105 minutes

Module 2 Introduction Video1 minute
Weighted Least Squares Part 19 minutes
Weighted Least Squares Part 211 minutes
Autocorrelation Part 110 minutes
Autocorrelation Part 29 minutes
Autocorrelation Part 310 minutes
Multicollinearity Part 112 minutes
Multicollinearity Part 29 minutes
Multicollinearity Part 37 minutes
Variable Selection and Model Validation Part 112 minutes
Variable Selection and Model Validation Part 27 minutes
Variable Selection and Model Validation Part 39 minutes

6 readingsTotal 120 minutes

Video 18 Slides - Weighted Least Squares (pdf)30 minutes
Video 19 Slides - Autocorrelation (pdf)30 minutes
Video 20 Slides - Multicollinearity (pdf)30 minutes
Video 21 Slides - Variable Selection and Model Validation (pdf)10 minutes
Module 2 Summary10 minutes
Insights from an Industry Leader: Learn More About Our Program10 minutes

5 assignmentsTotal 300 minutes

Weighted Least Squares30 minutes
Autocorrelation 30 minutes
Multicollinearity30 minutes
Variable Selection and Model Validation30 minutes
Module 2 Summative Assessment180 minutes

What's included

1 assignment

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

This course is part of the following degree program(s) offered by Illinois Tech. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹