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
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Карьерные результаты учащихся
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45%
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Приобретаемые навыки
StatisticsLinear RegressionR ProgrammingRegression Analysis
Карьерные результаты учащихся
33%
начал новую карьеру, пройдя эти курсы
45%
получил значимые преимущества в карьере благодаря этому курсу
12%
стал больше зарабатывать или получил повышение
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Курс 3 из 5 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Начальный уровень
Прибл. 14 часов на выполнение
Предполагаемая нагрузка: 4 weeks of study, 5-7 hours/week
Английский
Субтитры: Английский, Корейский
Программа курса: что вы изучите
22 минуты на завершение
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!
22 минуты на завершение
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
2 ч. на завершение
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.
2 ч. на завершение
8 видео ((всего 47 мин.)), 3 материалов для самостоятельного изучения, 2 тестов
8 видео
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 практических упражнения
Week 1 Practice Quiz8мин
Week 1 Quiz18мин
2 ч. на завершение
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!
2 ч. на завершение
3 видео ((всего 24 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
5 материалов для самостоятельного изучения
Lesson Learning Objectives10мин
Week 2 Suggested Readings and Exercises10мин
About Lab Choices10мин
Week 1 & 2 Lab Instructions (RStudio)10мин
Week 1 & 2 Lab Instructions (RStudio Cloud)10мин
3 практических упражнения
Week 2 Practice Quiz6мин
Week 2 Quiz16мин
Week 1 & 2 Lab20мин
3 ч. на завершение
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!
3 ч. на завершение
7 видео ((всего 57 мин.)), 5 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
5 материалов для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Week 3 Suggested Readings and Exercises10мин
Week 3 Lab Instructions (RStudio)10мин
Week 3 Lab Instructions (RStudio Cloud)10мин
3 практических упражнения
Week 3 Practice Quiz16мин
Week 3 Quiz20мин
Week 3 Lab20мин
2 ч. на завершение
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.
2 ч. на завершение
1 материал для самостоятельного изучения
1 материал для самостоятельного изучения
Project Files and Rubric10мин
Рецензии
Лучшие отзывы о курсе LINEAR REGRESSION AND MODELING
от партнера PKMay 23, 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.
от партнера DSMar 29, 2017
This course provides a very good introduction to basic linear regression, including simple multiple linear regression, model building and interpretation, model diagnostics, and application in R.
от партнера EBFeb 25, 2017
Good, but a little "smaller" than the Inferential statistics course (which is very complete). I would have liked to also learn Logistics regression, which I now have to learn elsewhere.
от партнера ANDec 11, 2018
Files for this course were broken and I faced a lot of trouble to find good one. This course may be made more comprehensive and not assuming that reader have also understanding.
от партнера VSSep 14, 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.
от партнера AADec 19, 2017
Nice course. The downside is that it only explains interpretation of linear regression, but not enough details about how linear regression is performed from math point of view.
от партнера OKSep 21, 2016
Very good course. while it does not cover everything. the teacher does a great job explaining things in a simple manner. My feed back would be to move ANOVA into this module.
от партнера CGJan 19, 2018
Good but I felt some gaps in the material made it difficult to learn. Also, the quiz questions are focused on attention to detail "gotcha" questions. This can be frustrating.
от партнера YOJan 1, 2017
Again like the first two courses, this course was great. The Lectures were excellent and the assignments very helpful in solidifying understanding. Thanks a lot Dr Mine
от партнера JMJan 12, 2017
This course was good. However, compared to the other courses in the specialisation had less content. I would have liked to have videos on logistic regression as well.
от партнера JSNov 3, 2018
I enjoyed this course. It was quick, but I learned a lot! I thought the assignments were well-thought-out, and the custom R package for the course was a nice touch.
от партнера MSJun 20, 2018
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.
от партнера ABNov 27, 2017
Great course! I've already taken a similar stats course using SPSS and this course was an excellent refresher, while increasing my familiarity with R.
от партнера HHJul 3, 2017
wonderful module! easy to understand. the labs are extremely helpful which enables you to have a good command of practicing linear regression with R
от партнера ACOct 29, 2016
Excellent Course. Mine, the teacher is a great great teacher. The mentors help a lot.\n\nTechnical parts, coursera platform should work better
от партнера HZOct 1, 2016
The structure of this course is really good. The slides demonstrates everything clearly. The speed that the instructor talks is good too.
от партнера BSJan 19, 2018
One of the most useful of the series, can be valuable as a standalone course on Regression and Correlation. It is also very accessible.
от партнера NHDec 18, 2018
Very informative for an introduction. Wish it was longer and more mathematical, but there are other courses on Coursera for that.
от партнера TGJan 28, 2017
Good overview of regression modeling. Would have liked to see more on logistic regression. But that's ok, can read it on my own.
от партнера DWJun 5, 2017
The Professor is a clear communicator and has a flair for finding interesting and engaging examples to illustrate the concepts.
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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.
Специализация Statistics with R: общие сведения
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.
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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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