Linear Regression and Modeling

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Opleiderscore: starstarstarstar_halfstar_border 7,2 Coursera (CC) heeft een gemiddelde beoordeling van 7,2 (uit 6 ervaringen)

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About this course: 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.

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  Duke University
  • Taught by:  Mine Çetinkaya-Rundel, Assistant Professor of the Practice

    Department of Statistical Science
Basic Info Course 3 of 5 in the Statistics with R Specialization Level Beginner Commitment 4 weeks of study, 5-7 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.7 stars Average User Rating 4.7See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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.

Syllabus


WEEK 1


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, 2 readings expand


  1. Video: Introduction to Statistics with R
  2. Reading: About Statistics with R Specialization
  3. Reading: about Linear Regression and Modeling


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, 3 readings, 1 practice quiz expand


  1. Reading: Lesson Learning Objectives
  2. Video: Introduction
  3. Video: Correlation
  4. Video: Residuals
  5. Video: Least Squares Line
  6. Reading: Lesson Learning Objectives
  7. Video: Prediction and Extrapolation
  8. Video: Conditions for Linear Regression
  9. Video: R Squared
  10. Video: Regression with Categorical Explanatory Variables
  11. Reading: Week 1 Suggested Readings and Practice
  12. Practice Quiz: Week 1 Practice Quiz

Graded: Week 1 Quiz

WEEK 2


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, 3 readings, 1 practice quiz expand


  1. Reading: Lesson Learning Objectives
  2. Video: Outliers in Regression
  3. Video: Inference for Linear Regression
  4. Video: Variability Partitioning
  5. Reading: Week 2 Suggested Readings and Exercises
  6. Practice Quiz: Week 2 Practice Quiz
  7. Reading: Instructions for Week 1 & 2 Lab

Graded: Week 2 Quiz
Graded: Week 1 & 2 Lab

WEEK 3


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, 4 readings, 1 practice quiz expand


  1. Video: Introduction
  2. Reading: Lesson Learning Objectives
  3. Video: Multiple Predictors
  4. Video: Adjusted R Squared
  5. Video: Collinearity and Parsimony
  6. Reading: Lesson Learning Objectives
  7. Video: Inference for MLR
  8. Video: Model Selection
  9. Video: Diagnostics for MLR
  10. Reading: Week 3 Suggested Readings and Exercises
  11. Practice Quiz: Week 3 Practice Quiz
  12. Reading: Instructions for Week 3 Lab

Graded: Week 3 Quiz
Graded: Week 3 Lab

WEEK 4


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 expand


  1. Reading: Project Files and Rubric

Graded: Data Analysis Project

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