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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

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Advanced Statistical Techniques for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 33 minutes

Course Welcome2 minutes
Module 1 Introduction1 minute
Lesson 1 Introduction1 minute
Logistic Regression - Part 111 minutes
Lesson 2 Introduction1 minute
Logistic Regression Part II - Part 110 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

Module 1 Summative Assessment180 minutes
Introduction to Logistic Regression Part I30 minutes
Intro to Logistic Regression Part II30 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 26 minutes

Module 2 Introduction1 minute
Lesson 3 Introduction1 minute
Poisson Regression - Part 19 minutes
Poisson Regression - Part 24 minutes
Lesson 4 Introduction1 minute
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

Module 2 Summative Assessment180 minutes
Poisson Regression 30 minutes
Generalized Linear Models30 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 45 minutes

Module 3 Introduction1 minute
Lesson 5 Introduction1 minute
Robust Regression - Part 110 minutes
Robust Regression - Part 212 minutes
Lesson 6 Introduction1 minute
Model Validations - Part 111 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

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

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

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