About this Course
37,519

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 14 hours to complete

Suggested: 4 weeks of study, 5-7 hours/week...

English

Subtitles: English

Skills you will gain

StatisticsLinear RegressionR ProgrammingRegression Analysis

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Approx. 14 hours to complete

Suggested: 4 weeks of study, 5-7 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
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!...
1 video (Total 2 min), 2 readings
2 readings
About Statistics with R Specialization10m
More about Linear Regression and Modeling10m
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. ...
8 videos (Total 47 min), 3 readings, 2 quizzes
8 videos
Correlation9m
Residuals1m
Least Squares Line11m
Prediction and Extrapolation3m
Conditions for Linear Regression10m
R Squared4m
Regression with Categorical Explanatory Variables5m
3 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice10m
2 practice exercises
Week 1 Practice Quiz8m
Week 1 Quiz18m
Week
2
2 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 videos (Total 24 min), 5 readings, 3 quizzes
3 videos
Inference for Linear Regression11m
Variability Partitioning5m
5 readings
Lesson Learning Objectives10m
Week 2 Suggested Readings and Exercises10m
About Lab Choices10m
Week 1 & 2 Lab Instructions (RStudio)10m
Week 1 & 2 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 2 Practice Quiz6m
Week 2 Quiz16m
Week 1 & 2 Lab20m
Week
3
3 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. Hope you enjoy!...
7 videos (Total 57 min), 5 readings, 3 quizzes
7 videos
Multiple Predictors11m
Adjusted R Squared10m
Collinearity and Parsimony3m
Inference for MLR11m
Model Selection11m
Diagnostics for MLR7m
5 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 3 Suggested Readings and Exercises10m
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 3 Practice Quiz16m
Week 3 Quiz20m
Week 3 Lab20m
Week
4
2 hours to complete

Final Project

In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment. ...
1 reading, 1 quiz
1 reading
Project Files and Rubric10m
4.7
166 ReviewsChevron Right

33%

started a new career after completing these courses

47%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Top Reviews

By PKMay 24th 2017

Very good course taught by Dr. Mine who is as always a very good teacher. The videos are very eloquent and easy to understand. Highly recommend it if you are looking for a basic refresher course.

By PSSep 15th 2017

fantastic course on linear regression, concepts are well explained followed by quiz and practical exercises.\n\nthough you need to complete the prior courses to understand this.

Instructor

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

About 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....

About the Statistics with R Specialization

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions....
Statistics with R

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • 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.

More questions? Visit the Learner Help Center.