Model Diagnostics and Remedial Measures
- Offered byCoursera
Model Diagnostics and Remedial Measures at Coursera Overview
Duration | 19 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course |
Credential | Certificate |
Model Diagnostics and Remedial Measures at Coursera Highlights
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Model Diagnostics and Remedial Measures at Coursera Course details
- 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.
- 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.
- This course is the continuation of MAT764. If you have not yet taken the MAT764 course, it is recommended that you complete that course prior to this course. The foundational knowledge to support the project are carried through in this deeper dive into using core ideas behind simple and multiple linear regression assuming that all basic assumptions of the model have been met.
- 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.
Model Diagnostics and Remedial Measures at Coursera Curriculum
Module 1: Model Diagnostics and Remediation Part I
Instructor Welcome and Course Overview
Module 1 Introduction
Regression Diagnostics Part 1
Regression Diagnostics Part 2
Regression Diagnostics Part 3
Variance-Stabilizing Transformation Part 1
Variance-Stabilizing Transformation Part 2
Box-Cox Transformation
Transformations to Linearize the Model
Syllabus
Video 14 Slides - Regression Diagnostics (pdf)
Video 15 Slides - Variance-Stabilizing Transformation
Video 16 Slides - Box-Cox Transformation
Video 17 Slides - Transformations to Linearize the Model (pdf)
Module 1 Summary
Regression Diagnostics
Variance-Stabilizing Transformation
Box-Cox Transformation
Transformations to Linearize the Model
Module 1 Summative Assessment
Meet and Greet Discussion
Module 2: Model Diagnostics and Remediation Part II
Module 2 Introduction Video
Weighted Least Squares Part 1
Weighted Least Squares Part 2
Autocorrelation Part 1
Autocorrelation Part 2
Autocorrelation Part 3
Multicollinearity Part 1
Multicollinearity Part 2
Multicollinearity Part 3
Video Selection and Model Validation Part 1
Video Selection and Model Validation Part 2
Video Selection and Model Validation Part 3
Video 18 Slides - Weighted Least Squares (pdf)
Video 19 Slides - Autocorrelation (pdf)
Video 20 Slides - Multicollinearity (pdf)
Video 21 Slides - Variable Selection and Model Validation (pdf)
Module 2 Summary
Weighted Least Squares
Autocorrelation
Multicollinearity
Variable Selection and Model Validation
Module 2 Summative Assessment
Summative Course Assessment