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.
Model Diagnostics and Remedial Measures
Model Diagnostics and Remedial Measures
Instructor: Kiah Ong
Sponsored by Louisiana Workforce Commission
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
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
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
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
What's included
1 assignment
Instructor
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