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.
Этот курс входит в специализацию ''Специализация Statistics with R'
Linear Regression and Modeling
от партнера
Об этом курсе
4.7
Оценки: 781
Приобретаемые навыки
StatisticsLinear RegressionR ProgrammingRegression Analysis
Программа курса: что вы изучите
Неделя
1About 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 (всего 2 мин.), 2 материалов для самостоятельного изучения
1 видео
2 материала для самостоятельного изучения
About Statistics with R Specialization10мин
More about Linear Regression and Modeling10мин
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 (всего 47 мин.), 3 материалов для самостоятельного изучения, 2 тестов
8 видео
Introductionмин
Correlation9мин
Residuals1мин
Least Squares Line11мин
Prediction and Extrapolation3мин
Conditions for Linear Regression10мин
R Squared4мин
Regression with Categorical Explanatory Variables5мин
3 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 1 Suggested Readings and Practice10мин
2 практического упражнения
Week 1 Practice Quiz8мин
Week 1 Quiz18мин
Неделя
2More 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 (всего 24 мин.), 3 материалов для самостоятельного изучения, 3 тестов
3 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Week 2 Suggested Readings and Exercises10мин
Instructions for Week 1 & 2 Lab10мин
3 практического упражнения
Week 2 Practice Quiz6мин
Week 2 Quiz16мин
Week 1 & 2 Lab20мин
Неделя
3Multiple 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 (всего 57 мин.), 4 материалов для самостоятельного изучения, 3 тестов
7 видео
Introduction2мин
Multiple Predictors11мин
Adjusted R Squared10мин
Collinearity and Parsimony3мин
Inference for MLR11мин
Model Selection11мин
Diagnostics for MLR7мин
4 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 3 Suggested Readings and Exercises10мин
Instructions for Week 3 Lab10мин
3 практического упражнения
Week 3 Practice Quiz16мин
Week 3 Quiz20мин
Week 3 Lab20мин
Неделя
4Final 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, 1 тест
1 материал для самостоятельного изучения
Project Files and Rubric10мин
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20%
Лучшие рецензии
автор: PK•May 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.
автор: PS•Sep 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.
Преподаватель
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
О 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.
О специализации ''Statistics with R'
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.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
Что я получу, оформив подписку на специализацию?
Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
Will I receive a transcript from Duke University for completing this course?
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.
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