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: Duke University
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 | Начинающий |
| 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 материала для самостоятельного изучения
- Leyendo: About Statistics with R Specialization
- Leyendo: 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 материала для самостоятельного изучения, 1 тренировочный тест
- Leyendo: Lesson Learning Objectives
- Видео: Correlation
- Видео: Residuals
- Видео: Least Squares Line
- Leyendo: Lesson Learning Objectives
- Видео: Prediction and Extrapolation
- Видео: Conditions for Linear Regression
- Видео: R Squared
- Видео: Regression with Categorical Explanatory Variables
- Leyendo: Week 1 Suggested Readings and Practice
- Cuestionario de práctica: 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 материала для самостоятельного изучения, 1 тренировочный тест
- Leyendo: Lesson Learning Objectives
- Видео: Inference for Linear Regression
- Видео: Variability Partitioning
- Leyendo: Week 2 Suggested Readings and Exercises
- Cuestionario de práctica: Week 2 Practice Quiz
- Leyendo: 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 материала для самостоятельного изучения, 1 тренировочный тест
- Leyendo: Lesson Learning Objectives
- Видео: Multiple Predictors
- Видео: Adjusted R Squared
- Видео: Collinearity and Parsimony
- Leyendo: Lesson Learning Objectives
- Видео: Inference for MLR
- Видео: Model Selection
- Видео: Diagnostics for MLR
- Leyendo: Week 3 Suggested Readings and Exercises
- Cuestionario de práctica: Week 3 Practice Quiz
- Leyendo: 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
- Leyendo: Project Files and Rubric
Оцениваемый: Data Analysis Project
Часто задаваемые вопросы
Как это работает
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
Авторы
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.
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
Рейтинги и отзывы
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.
Good way to startCovered all basics stuffSimple to understand Good structuring
Nicely Explained Concepts :)
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ST
Really enjoyed this course! It teaches you the theory you need to understand how a linear regression model works, how to check that your model fulfils certain conditions so that it is valid, and how to build and implement your model in practice!