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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このコースについて
習得するスキル
- Statistics
- Linear Regression
- R Programming
- Regression Analysis
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デューク大学(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.
シラバス - 本コースの学習内容
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.
レビュー
- 5 stars80.06%
- 4 stars16.13%
- 3 stars2.97%
- 2 stars0.25%
- 1 star0.56%
LINEAR REGRESSION AND MODELING からの人気レビュー
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.
A great primer on linear regression with labs that help to establish understanding and a project that is focused enough not to be overwhelming, and allows the learner to play around with the concepts
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.
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.
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