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
(1,753 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 video3 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
Instructor ratings
4.9 (144 ratings)Offered by
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4.8
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OK
5
Reviewed on Sep 22, 2016
Very good course. while it does not cover everything. the teacher does a great job explaining things in a simple manner. My feed back would be to move ANOVA into this module.
TM
5
Reviewed on Jul 22, 2020
A great primer on linear regression with labs that help to establish understanding and a project that is focused enough not to be overwhelming, and allows the learner to play around with the concepts
MN
5
Reviewed on Aug 3, 2023
Amazing course! Learned so much and can't wait to apply it as a (hopeful) Duke student. Makes me even more thrilled to apply as a statistical science major this fall 2024!
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