Об этом курсе: 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.
Автор: Университет Дьюка
Преподаватели: Mine Çetinkaya-Rundel, Associate Professor of the Practice
Department of Statistical Science
| Основные сведения | Course 3 of 5 in the Statistics with R Specialization |
| Уровень | Beginner |
| Выполнение | 4 weeks of study, 5-7 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 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 видео, 2 материалов для самостоятельного изучения
- Видео: Introduction to Statistics with R
- Reading: About Statistics with R Specialization
- Reading: More 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 видео, 3 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Lesson Learning Objectives
- Видео: Introduction
- Видео: Correlation
- Видео: Residuals
- Видео: Least Squares Line
- Reading: Lesson Learning Objectives
- Видео: Prediction and Extrapolation
- Видео: Conditions for Linear Regression
- Видео: R Squared
- Видео: Regression with Categorical Explanatory Variables
- Reading: Week 1 Suggested Readings and Practice
- Practice Quiz: Week 1 Practice Quiz
Оцениваемый: Week 1 Quiz
НЕДЕЛЯ 2
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 видео, 3 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Lesson Learning Objectives
- Видео: Outliers in Regression
- Видео: Inference for Linear Regression
- Видео: Variability Partitioning
- Reading: Week 2 Suggested Readings and Exercises
- Practice Quiz: Week 2 Practice Quiz
- Reading: Instructions for Week 1 & 2 Lab
Оцениваемый: Week 2 Quiz
Оцениваемый: Week 1 & 2 Lab
НЕДЕЛЯ 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 видео, 4 материалов для самостоятельного изучения, 1 practice quiz
- Видео: Introduction
- Reading: Lesson Learning Objectives
- Видео: Multiple Predictors
- Видео: Adjusted R Squared
- Видео: Collinearity and Parsimony
- Reading: Lesson Learning Objectives
- Видео: Inference for MLR
- Видео: Model Selection
- Видео: Diagnostics for MLR
- Reading: Week 3 Suggested Readings and Exercises
- Practice Quiz: Week 3 Practice Quiz
- Reading: Instructions for Week 3 Lab
Оцениваемый: Week 3 Quiz
Оцениваемый: Week 3 Lab
НЕДЕЛЯ 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 материал для самостоятельного изучения
- Reading: Project Files and Rubric
Оцениваемый: Data Analysis Project
Часто задаваемые вопросы
Как это работает
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
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Certificates
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Авторы
Университет Дьюка
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.
Стоимость
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| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
Рейтинги и отзывы
One of my favorite course.
presenting linear regression concepts is amazing and worth to spend time for it.
Good course, gives you a solid foundation.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
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Really good course as the previous ones in this specialization. Could have included something more on checking for collinearity with categorical variables.