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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 modeling and programming concepts are intuitively described using the R programming language.
- Author
- David Lilja
- Publisher
- University of Minnesota
- Source
- LibreTexts
- License
- CC BY-NC
- Language
- EN
- Category
- Information Technology
Contents· 33 sections
- 01: Introduction
- 1.01: Prelude to Linear Regression
- 1.02: What is a Linear Regression Model
- 1.03: What is R
- 1.04: What's Next
- 02: Understand Your Data
- 2.01: Missing Values
- 2.02: Sanity Checking and Data Cleaning
- 2.03: The Example Data
- 2.04: Data Frames
- 2.05: Accessing a Data Frame
- 03: One-Factor Regression
- 3.01: Visualize the Data
- 3.02: The Linear Model Function
- 3.03: Evaluating the Quality of the Model
- 3.04: Residual Analysis
- 04: Multi-factor Regression
- 4.01: Visualizing the Relationships in the Data
- 4.02: Identifying Potential Predictors
- 4.03: The Backward Elimination Process
- 4.04: An Example of the Backward Elimination Process
- 4.05: Residual Analysis
- 4.06: When Things Go Wrong
- 05: Predicting Responses
- 5.01: Data Splitting for Training and Testing
- 5.02: Training and Testing
- 5.03: Predicting Across Data Sets
- 5.04: Section 5-
- 5.05: Section 6-
- 06: Reading Data into the R Environment
- 6.01: Reading CSV files
- 07: Summary
- 08: A Few Things to Try Next