Variable Selection, Model Validation, Nonlinear Regression

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

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

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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.

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

Introduction to Logistic Regression Part I 30 minutes
Intro to Logistic Regression Part II 30 minutes
Module 1 Summative Assessment 180 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

Poisson Regression 30 minutes
Generalized Linear Models 30 minutes
Module 2 Summative Assessment 180 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

Robust Regression 30 minutes
Variable Selection and Model Validation 30 minutes
Module 3 Summative Assessment 180 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

Instructor

Kiah Ong

Illinois Tech

3 Courses 3,422 learners

Offered by

Illinois Tech

Explore more from Probability and Statistics

Duke University
Linear Regression and Modeling
Course
Packt
Regression Analysis for Statistics & Machine Learning in R
Course
Illinois Tech
Linear Regression
Course
Coursera
Predict and Validate Regression Models in R
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.