Duke University

Linear Regression and Modeling

This course is part of Data Analysis with R Specialization

Taught in English

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Course

Gain insight into a topic and learn the fundamentals

4.8

(1,697 reviews)

|

94%

Beginner level
No prior experience required
10 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(1,697 reviews)

|

94%

Beginner level
No prior experience required
10 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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 quizzes

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 quizzes

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 quizzes

Instructor

Instructor ratings
4.9 (131 ratings)
Mine Çetinkaya-Rundel
Duke University
8 Courses391,092 learners

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Duke University

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