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

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. All of the necessary resources are freely available online.

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In English.

Description based on online resource