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.
Linear Regression and Modeling
Linear Regression and Modeling
This course is part of Data Analysis with R Specialization
Instructor: Mine Çetinkaya-Rundel
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4.8
(1,730 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 10 hours
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Assessments
8 assignments
Taught in English
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This course is part of the Data Analysis with R Specialization
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There are 4 modules in this course
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!
What's included
1 video2 readings
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.
What's included
8 videos3 readings2 assignments
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!
What's included
3 videos5 readings3 assignments
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.
What's included
7 videos6 readings3 assignments
Instructor
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4.9 (138 ratings)Offered by
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4.8
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Showing 3 of 1730
CG
4
Reviewed on Jan 19, 2018
Good but I felt some gaps in the material made it difficult to learn. Also, the quiz questions are focused on attention to detail "gotcha" questions. This can be frustrating.
DS
4
Reviewed on Mar 29, 2017
This course provides a very good introduction to basic linear regression, including simple multiple linear regression, model building and interpretation, model diagnostics, and application in R.
JM
4
Reviewed on Jan 12, 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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