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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Об этом курсе
Приобретаемые навыки
- Statistics
- Linear Regression
- R Programming
- Regression Analysis
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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.
Программа курса: что вы изучите
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!
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.
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!
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. There is also a final project included in this week. You will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.
Рецензии
- 5 stars80,06 %
- 4 stars16,13 %
- 3 stars2,97 %
- 2 stars0,25 %
- 1 star0,56 %
Лучшие отзывы о курсе LINEAR REGRESSION AND MODELING
It is a very nice course. The instructor's way of delivering the content is flawless and very precise. The course has been designed very nicely.
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.
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.
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.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
Will I receive a transcript from Duke University for completing this course?
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