Variable Selection, Model Validation, Nonlinear Regression

Variable Selection, Model Validation, Nonlinear Regression

Instructor: Kiah Ong

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Assessments

10 assignments

Taught in English

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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 videosTotal 32 minutes

Course Welcome1 minutePreview module
Module 1 Introduction1 minute
Lesson 1 Introduction0 minutes
Logistic Regression - Part 110 minutes
Lesson 2 Introduction0 minutes
Logistic Regression Part II - Part 19 minutes
Logistic Regression Part II - Part 28 minutes

4 readingsTotal 80 minutes

Syllabus10 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 Summary10 minutes

3 assignmentsTotal 240 minutes

Introduction to Logistic Regression Part I30 minutes
Intro to Logistic Regression Part II30 minutes
Module 1 Summative Assessment180 minutes

1 discussion promptTotal 10 minutes

Meet and Greet Discussion10 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 videosTotal 25 minutes

Module 2 Introduction1 minutePreview module
Lesson 3 Introduction0 minutes
Poisson Regression - Part 18 minutes
Poisson Regression - Part 24 minutes
Lesson 4 Introduction0 minutes
GLM10 minutes

3 readingsTotal 70 minutes

Video 24 Slides - Poisson Regression (pdf)30 minutes
Video 25 Slides - Generalized Linear Models (pdf)30 minutes
Module 2 Summary10 minutes

3 assignmentsTotal 240 minutes

Poisson Regression 30 minutes
Generalized Linear Models30 minutes
Module 2 Summative Assessment180 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 videosTotal 44 minutes

Module 3 Introduction1 minutePreview module
Lesson 5 Introduction0 minutes
Robust Regression - Part 19 minutes
Robust Regression - Part 211 minutes
Lesson 6 Introduction0 minutes
Model Validations - Part 110 minutes
Model Validations - Part 210 minutes

4 readingsTotal 80 minutes

Video 26 Slides - Robust Regression (pdf)30 minutes
Video 27 Slides - Variable Selection and Model Validation (pdf)30 minutes
Module 3 Summary10 minutes
Insights from an Industry Leader: Learn More About Our Program10 minutes

3 assignmentsTotal 240 minutes

Robust Regression30 minutes
Variable Selection and Model Validation30 minutes
Module 3 Summative Assessment180 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.