1997TechnometricsRequires access

Applied Linear Regression Models

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Abstract

Part1 Simple Linear Regression 1Linear Regression with One Predictor Variable 2Inferences in Regression and Correlation Analysis 3Diagnostics and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5Matrix Approach to Simple Linear Regression Analysis Part 2Multiple Linear Regression 6Multiple Regression I 7 Multiple Regression II 8Building the Regression Model I: Models for Quantitative and Qualitative Predictors 9 Building the Regression Model II: Model Selection and Validation 10Building the Regression Model III: Diagnostics 11Remedial Measures and Alternative Regression Techniques 12Autocorrelation in Time Series Data Part 3Nonlinear Regression 13Introduction to Nonlinear Regression and Neural Networks 14Logistic Regression, Poisson Regression, and Generalized Linear Models

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Part1 Simple Linear Regression 1Linear Regression with One Predictor Variable 2Inferences in Regression and Correlation Analysis 3Diagnostics and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5Matrix Approach to Simple Linear Regression Analysis Part 2Multiple Linear Regression 6Multiple Regression I 7 Multiple Regression II 8Building the Regression Model I: Models for Quantitative and Qualitative Predictors 9 Building the Regression Model II: Model Selection and Validation 10Building the Regression Model III: Diagnostics 11Remedial Measures and Alternative Regression Techniques 12Autocorrelation in Time Series Data Part 3Nonlinear Regression 13Introduction to Nonlinear Regression and Neural Networks 14Logistic Regression, Poisson Regression, and Generalized Linear Models

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Available abstract

Part1 Simple Linear Regression 1Linear Regression with One Predictor Variable 2Inferences in Regression and Correlation Analysis 3Diagnostics and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5Matrix Approach to Simple Linear Regression Analysis Part 2Multiple Linear Regression 6Multiple Regression I 7 Multiple Regression II 8Building the Regression Model I: Models for Quantitative and Qualitative Predictors 9 Building the Regression Model II: Model Selection and Validation 10Building the Regression Model III: Diagnostics 11Remedial Measures and Alternative Regression Techniques 12Autocorrelation in Time Series Data Part 3Nonlinear Regression 13Introduction to Nonlinear Regression and Neural Networks 14Logistic Regression, Poisson Regression, and Generalized Linear Models

Key concepts: Linear regression, Statistics, Proper linear model, Mathematics, Regression analysis, Regression, Bayesian multivariate linear regression, Econometrics

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