Variable Selection, Model Validation, Nonlinear Regression
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Variable Selection, Model Validation, Nonlinear Regression
This course is part of Advanced Statistical Techniques for Data Science Specialization
Instructor: Kiah Ong
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There are 4 modules in this course
If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.
This course will focus on getting you acquainted with the generalized linear model (GLM) through the examples of logistic and Poisson regression. You will also see how simple and multiple linear regression relates to GLM using the link function. We will also study a regression technique that is robust to having outliers in the data. Finally, we will learn how to perform model validation involving GLM. After this course, students will be able to: - Determine which regression models to use based on the nature of the response variable. - Use regression technique which is robust to the presence of outliers. - Perform generalized linear regression using R by identifying the correct link function. - Interpret and draw conclusions on the regression model. - Use R to perform statistical inference based on the regression models.
In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x’s. There is a lot to read, watch, and consume in this module so, let’s get started!
What's included
7 videos4 readings3 assignments1 discussion prompt
7 videos•Total 33 minutes
- Course Welcome•2 minutes
- Module 1 Introduction•1 minute
- Lesson 1 Introduction•1 minute
- Logistic Regression - Part 1•11 minutes
- Lesson 2 Introduction•1 minute
- Logistic Regression Part II - Part 1•10 minutes
- Logistic Regression Part II - Part 2•8 minutes
4 readings•Total 80 minutes
- Syllabus•10 minutes
- Video 22 Slides - Introduction to Logistic Regression Part I (pdf)•30 minutes
- Video 23 Slides - Introduction to Logistic Regression Part II (pdf)•30 minutes
- Module 1 Summary•10 minutes
3 assignments•Total 240 minutes
- Module 1 Summative Assessment•180 minutes
- Introduction to Logistic Regression Part I•30 minutes
- Intro to Logistic Regression Part II•30 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet Discussion•10 minutes
In this module, you will learn the difference between Poisson regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, use R to compute the estimators of a Poisson regression model and the generalized linear model, and the similarities between the linear, logistic, and Poisson regressions. There is a lot to read, watch, and consume in this module so, let’s get started!
What's included
6 videos3 readings3 assignments
6 videos•Total 26 minutes
- Module 2 Introduction•1 minute
- Lesson 3 Introduction•1 minute
- Poisson Regression - Part 1•9 minutes
- Poisson Regression - Part 2•4 minutes
- Lesson 4 Introduction•1 minute
- GLM•10 minutes
3 readings•Total 70 minutes
- Video 24 Slides - Poisson Regression (pdf)•30 minutes
- Video 25 Slides - Generalized Linear Models (pdf)•30 minutes
- Module 2 Summary•10 minutes
3 assignments•Total 240 minutes
- Module 2 Summative Assessment•180 minutes
- Poisson Regression •30 minutes
- Generalized Linear Models•30 minutes
In this module, you will learn how to modify the ordinary least squares method to make the regression model more robust to the effect of outliers and use R to compute the robust regression parameters using different M-estimators and perform model validations involving logistic regression. There is a lot to read, watch, and consume in this module so, let’s get started!
What's included
7 videos4 readings3 assignments
7 videos•Total 45 minutes
- Module 3 Introduction•1 minute
- Lesson 5 Introduction•1 minute
- Robust Regression - Part 1•10 minutes
- Robust Regression - Part 2•12 minutes
- Lesson 6 Introduction•1 minute
- Model Validations - Part 1•11 minutes
- Model Validations - Part 2•10 minutes
4 readings•Total 80 minutes
- Video 26 Slides - Robust Regression (pdf)•30 minutes
- Video 27 Slides - Variable Selection and Model Validation (pdf)•30 minutes
- Module 3 Summary•10 minutes
- Insights from an Industry Leader: Learn More About Our Program•10 minutes
3 assignments•Total 240 minutes
- Module 3 Summative Assessment•180 minutes
- Robust Regression•30 minutes
- Variable Selection and Model Validation•30 minutes
This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.
What's included
1 assignment
1 assignment•Total 180 minutes
- Summative Course Assessment•180 minutes
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