Regressing Meaning: Definition, Examples, Uses
Explore what regression analysis is, the difference between correlation and causation, and how you can use regression analysis in different industries.
October 21, 2024
Article
This course is part of Statistical Modeling for Data Science Applications Specialization
Instructor: Brian Zaharatos
7,311 already enrolled
Included with
(30 reviews)
Recommended experience
Intermediate level
Calculus, linear algebra, and probability theory.
(30 reviews)
Recommended experience
Intermediate level
Calculus, linear algebra, and probability theory.
Articulate some recommended practices for ethical behavior and communication in statistics and data science.
Interpret important components of the MLR model, including the “systematic” and “random” components of the model.
Describe and implement testing-based procedures for model selections and select a “best” model based on a given procedure.
Add to your LinkedIn profile
2 quizzes, 9 assignments
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.
8 videos3 readings1 quiz1 assignment2 programming assignments1 peer review1 discussion prompt1 ungraded lab
In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.
9 videos2 assignments1 programming assignment1 peer review1 ungraded lab
In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
8 videos1 reading2 assignments1 programming assignment2 peer reviews1 ungraded lab
In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.
6 videos1 assignment1 programming assignment1 peer review1 ungraded lab
In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is appropriate for the data at hand.
6 videos1 quiz1 assignment1 programming assignment1 peer review1 ungraded lab
In this module, we will study methods for model selection and model improvement.. In particular, we will learn when and how to apply model selection techniques such as forward selection and backward selection, criterion-based methods, and will learn about the problem of multicollinearity (also called collinearity).
10 videos2 assignments1 programming assignment1 peer review1 ungraded lab
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
CU Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
University of Colorado Boulder
Build toward a degree
Course
University of Colorado Boulder
Build toward a degree
Specialization
University of Colorado Boulder
Build toward a degree
Course
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
University of Colorado Boulder
Degree · 2 years
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.
30 reviews
76.66%
10%
0%
6.66%
6.66%
Showing 3 of 30
Reviewed on Apr 29, 2024
A lot of work with several peer reviews, but it get you into R for Regression Analysis. Well laid out course. need knowledge of Linear algrebra for this course.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Earn a degree from world-class universities - 100% online
Upskill your employees to excel in the digital economy
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:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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.