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
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Linear Regression and Modeling
This course is part of Data Analysis with R Specialization
Instructor: Mine Çetinkaya-Rundel
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4.8
(1,758 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 10 hours
Learn at your own pace
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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
When you enroll in this course, you'll also be enrolled in this 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
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4.9 (144 ratings)Offered by
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Showing 3 of 1758
AN
4
Reviewed on Dec 11, 2018
Files for this course were broken and I faced a lot of trouble to find good one. This course may be made more comprehensive and not assuming that reader have also understanding.
LD
5
Reviewed on May 14, 2020
It has been a great adventure so far. I still greatly appreciate how final projects are constructed that gives us freedom to choose our approach to the problems within the data set.
MS
5
Reviewed on Jun 20, 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.
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Frequently asked questions
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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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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