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Variable Selection, Model Validation, Nonlinear Regression
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  2. Data Science
  3. Probability and Statistics
Illinois Tech

Variable Selection, Model Validation, Nonlinear Regression

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

Kiah Ong

Instructor: Kiah Ong

Included with Coursera Plus

•Learn more
4 modules
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

Recommended experience

Intermediate level

Have taken undergraduate courses in Probability and Statistics.

20 hours to complete
3 weeks at 6 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

Recommended experience

Intermediate level

Have taken undergraduate courses in Probability and Statistics.

20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
Learn more
  • About
  • Outcomes
  • Modules
  • Recommendations
  • Testimonials
  • Reviews

Skills you'll gain

  • Statistical Inference
  • Predictive Modeling
  • Probability
  • Statistical Analysis
  • R Programming
  • Advanced Analytics
  • Probability & Statistics
  • Regression Analysis
  • Statistical Modeling
  • Statistical Hypothesis Testing
  • Statistical Methods
  • Mathematical Modeling
  • Data Validation

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments¹

AI Graded see disclaimer
Taught in English

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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.

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 32 minutes
  • Course Welcome•1 minute•Preview module
  • Module 1 Introduction•1 minute
  • Lesson 1 Introduction•0 minutes
  • Logistic Regression - Part 1•10 minutes
  • Lesson 2 Introduction•0 minutes
  • Logistic Regression Part II - Part 1•9 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 25 minutes
  • Module 2 Introduction•1 minute•Preview module
  • Lesson 3 Introduction•0 minutes
  • Poisson Regression - Part 1•8 minutes
  • Poisson Regression - Part 2•4 minutes
  • Lesson 4 Introduction•0 minutes
  • 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 44 minutes
  • Module 3 Introduction•1 minute•Preview module
  • Lesson 5 Introduction•0 minutes
  • Robust Regression - Part 1•9 minutes
  • Robust Regression - Part 2•11 minutes
  • Lesson 6 Introduction•0 minutes
  • Model Validations - Part 1•10 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

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

 

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

I

Illinois Tech

Master of Data Science

Degree · 12-15 months

¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.

Instructor

Kiah Ong
Kiah Ong
Illinois Tech
3 Courses•2,599 learners

Offered by

Illinois Tech

Offered by

Illinois Tech

Illinois Tech is a top-tier, nationally ranked, private research university with programs in engineering, computer science, architecture, design, science, business, human sciences, and law. The university offers bachelor of science, master of science, professional master’s, and Ph.D. degrees—as well as certificates for in-demand STEM fields and other areas of innovation. Talented students from around the world choose to study at Illinois Tech because of the access to real-world opportunities, renowned academic programs, high value, and career prospects of graduates.

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Frequently asked questions

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

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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. If you only want to read and view the course content, you can audit the course for free.

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.

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