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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Recommended experience
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Recommended experience
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
- Plot (Graphics)
- Statistical Analysis
- Statistical Hypothesis Testing
- Correlation Analysis
- Scatter Plots
- Probability Distribution
- Verification And Validation
- Statistical Inference
- Statistical Methods
- Feature Engineering
- Time Series Analysis and Forecasting
- Model Evaluation
- Data Transformation
- Regression Analysis
- Statistical Modeling
Tools you'll learn
Details to know
11 assignments
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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 videos•Total 61 minutes
- Instructor Welcome and Course Overview •1 minute
- Module 1 Introduction•1 minute
- Regression Diagnostics Part 1•6 minutes
- Regression Diagnostics Part 2•4 minutes
- Regression Diagnostics Part 3•10 minutes
- Variance-Stabilizing Transformation Part 1•11 minutes
- Variance-Stabilizing Transformation Part 2•10 minutes
- Box-Cox Transformation•8 minutes
- Transformations to Linearize the Model•10 minutes
6 readings•Total 120 minutes
- Syllabus•10 minutes
- Video 14 Slides - Regression Diagnostics (pdf)•30 minutes
- Video 15 Slides - Variance-Stabilizing Transformation•30 minutes
- Video 16 Slides - Box-Cox Transformation•30 minutes
- Video 17 Slides - Transformations to Linearize the Model (pdf)•10 minutes
- Module 1 Summary•10 minutes
5 assignments•Total 300 minutes
- Module 1 Summative Assessment•180 minutes
- Regression Diagnostics•30 minutes
- Variance-Stabilizing Transformation•30 minutes
- Box-Cox Transformation•30 minutes
- Transformations to Linearize the Model •30 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet Discussion•10 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 videos•Total 105 minutes
- Module 2 Introduction Video•1 minute
- Weighted Least Squares Part 1•9 minutes
- Weighted Least Squares Part 2•11 minutes
- Autocorrelation Part 1•10 minutes
- Autocorrelation Part 2•9 minutes
- Autocorrelation Part 3•10 minutes
- Multicollinearity Part 1•12 minutes
- Multicollinearity Part 2•9 minutes
- Multicollinearity Part 3•7 minutes
- Variable Selection and Model Validation Part 1•12 minutes
- Variable Selection and Model Validation Part 2•7 minutes
- Variable Selection and Model Validation Part 3•9 minutes
6 readings•Total 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 Summary•10 minutes
- Insights from an Industry Leader: Learn More About Our Program•10 minutes
5 assignments•Total 300 minutes
- Module 2 Summative Assessment•180 minutes
- Weighted Least Squares•30 minutes
- Autocorrelation •30 minutes
- Multicollinearity•30 minutes
- Variable Selection and Model Validation•30 minutes
What's included
1 assignment
1 assignment•Total 180 minutes
- Summative Course Assessment •180 minutes
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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
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Reviewed on Jul 16, 2025
very interesting contents and the lecture make them easier to grasp
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