This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
Offered By
Duke UniversityAbout this Course
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Beginner Level
Approx. 10 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Skills you will gain
- Statistics
- Linear Regression
- R Programming
- Regression Analysis
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Beginner Level
Approx. 10 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Offered by
Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Syllabus - What you will learn from this course
22 minutes to complete
About Linear Regression and Modeling
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Please take several minutes to browse them through. Thanks for joining us in this course!
22 minutes to complete
1 video (Total 2 min), 2 readings
2 hours to complete
Linear Regression
In this week we’ll introduce linear regression. Many of you may be familiar with regression from reading the news, where graphs with straight lines are overlaid on scatterplots. Linear models can be used for prediction or to evaluate whether there is a linear relationship between two numerical variables.
2 hours to complete
8 videos (Total 47 min), 3 readings, 2 quizzes
3 hours to complete
More about Linear Regression
Welcome to week 2! In this week, we will look at outliers, inference in linear regression and variability partitioning. Please use this week to strengthen your understanding on linear regression. Don't forget to post your questions, concerns and suggestions in the discussion forum!
3 hours to complete
3 videos (Total 24 min), 5 readings, 3 quizzes
5 hours to complete
Multiple Regression
In this week, we’ll explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and categorical). We will also cover inference for multiple linear regression, model selection, and model diagnostics. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.
5 hours to complete
7 videos (Total 57 min), 6 readings, 3 quizzes
Reviews
- 5 stars80.11%
- 4 stars16.06%
- 3 stars3.01%
- 2 stars0.25%
- 1 star0.56%
TOP REVIEWS FROM LINEAR REGRESSION AND MODELING
by JSNov 4, 2018
I enjoyed this course. It was quick, but I learned a lot! I thought the assignments were well-thought-out, and the custom R package for the course was a nice touch.
by MMSep 27, 2021
linear regression is well taught throughout the course, but I think learning other types of regression modeling would be useful as well and adding them to the course materials is really good.
by MSJun 21, 2018
This was the first course where I started noticing that I'm really learning and was able to apply some of the earned knowledge at work.Totally recommended.
by JMJan 13, 2017
This course was good. However, compared to the other courses in the specialisation had less content. I would have liked to have videos on logistic regression as well.
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