Linear Regression Using R An Introduction to Data Modeling
Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key...
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| Format: | Electronic eBook |
| Language: | English |
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[Place of publication not identified]
University of Minnesota Libraries Publishing
[2016]
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| Series: | Open textbook library.
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| Subjects: | |
| Online Access: | Access online version |
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Table of Contents:
- 1 Introduction
- 1.1 What is a Linear Regression Model?
- 1.2 What is R?
- 1.3 What's Next?
- 2 Understand Your Data
- 2.1 Missing Values
- 2.2 Sanity Checking and Data Cleaning
- 2.3 The Example Data
- 2.4 Data Frames
- 2.5 Accessing a Data Frame
- 3 One-Factor Regression
- 3.1 Visualize the Data
- 3.2 The Linear Model Function
- 3.3 Evaluating the Quality of the Model
- 3.4 Residual Analysis
- 4 Multi-factor Regression
- 4.1 Visualizing the Relationships in the Data
- 4.2 Identifying Potential Predictors
- 4.3 The Backward Elimination Process
- 4.4 An Example of the Backward Elimination Process
- 4.5 Residual Analysis
- 4.6 When Things Go Wrong
- 5 Predicting Responses
- 5.1 Data Splitting for Training and Testing
- 5.2 Training and Testing
- 5.3 Predicting Across Data Sets
- 6 Reading Data into the R Environment
- 6.1 Reading CSV files
- 7 Summary8 A Few Things to Try NextBibliographyIndex