Applied Linear Regression

Name: Applied Linear Regression
Availability: OutOfStock
ISBN: 9780471704096

ISBN-10: 0471704091

ISBN-13: 9780471704096

Edition: 3rd 2005

Authors: Sanford Weisberg

List price: $89.95

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Applied Linear Regression, Third Edition is thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, this Third Edition stresses using graphical methods to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. This text is an excellent tool for learning how to…

Book details

List price: $89.95
Edition: 3rd
Copyright year: 2005
Publisher: Wiley & Sons, Incorporated, John
Binding: Online Resource
Pages: 352
Language: English

Preface. 1 Scatterplots and Regression. 1.1 Scatterplots. 1.2 Mean Functions. 1.3 Variance Functions. 1.4 Summary Graph. 1.5 Tools for Looking at Scatterplots. 1.5.1 Size. 1.5.2 Transformations. 1.5.3 Smoothers for the Mean Function. 1.6 Scatterplot Matrices. Problems. 2 Simple Linear Regression. 2.1 Ordinary Least Squares Estimation. 2.2 Least Squares Criterion. 2.3 Estimating 2. 2.4 Properties of Least Squares Estimates. 2.5 Estimated Variances. 2.6 Comparing Models: The Analysis of Variance. 2.6.1 The F-Test for Regression. 2.6.2 Interpreting p-values. 2.6.3 Power of Tests. 2.7 The Coefficient of Determination, R2. 2.8 Confidence Intervals… and Tests. 2.8.1 The Intercept. 2.8.2 Slope. 2.8.3 Prediction. 2.8.4 Fitted Values. 2.9 The Residuals. Problems. 3 Multiple Regression. 3.1 Adding a Term to a Simple Linear Regression Model. 3.1.1 Explaining Variability. 3.1.2 Added-Variable Plots. 3.2 The Multiple Linear Regression Model. 3.3 Terms and Predictors. 3.4 Ordinary Least Squares. 3.4.1 Data and Matrix Notation. 3.4.2 Variance-Covariance Matrix of e. 3.4.3 Ordinary Least Squares Estimators. 3.4.4 Properties of the Estimates. 3.4.5 Simple Regression in Matrix Terms. 3.5 The Analysis of Variance. 3.5.1 The Coefficient of Determination. 3.5.2 Hypotheses Concerning One of the Terms. 3.5.3 Relationship to the t -Statistic. 3.5.4 t-Tests and Added-Variable Plots. 3.5.5 Other Tests of Hypotheses. 3.5.6 Sequential Analysis of Variance Tables. 3.6 Predictions and Fitted Values. Problems. 4 Drawing Conclusions. 4.1 Understanding Parameter Estimates. 4.1.1 Rate of Change. 4.1.2 Signs of Estimates. 4.1.3 Interpretation Depends on Other Terms in the Mean Function. 4.1.4 Rank Deficient and Over-Parameterized Mean Functions. 4.1.5 Tests. 4.1.6 Dropping Terms. 4.1.7 Logarithms. 4.2 Experimentation Versus Observation. 4.3 Sampling from a Normal Population. 4.4 More on R2. 4.4.1 Simple Linear Regression and R2. 4.4.2 Multiple Linear Regression. 4.4.3 Regression through the Origin. 4.5 Missing Data. 4.5.1 Missing at Random. 4.5.2 Alternatives. 4.6 Computationally Intensive Methods. 4.6.1 Regression Inference without Normality. 4.6.2 Nonlinear Functions of Parameters. 4.6.3 Predictors Measured with Error. Problems. 5 Weights, Lack of Fit, and More. 5.1 Weighted Least Squares. 5.1.1 Applications of Weighted Least Squares. 5.1.2 Additional Comments. 5.2 Testing for Lack of Fit, Variance Known. 5.3 Testing for Lack of Fit, Variance Unknown. 5.4 General F Testing. 5.4.1 Non-null Distributions. 5.4.2 Additional Comments. 5.5 Joint Confidence Regions. Problems. 6 Polynomials and Factors. 6.1 Polynomial Regression. 6.1.1 Polynomials with Several Predictors. 6.1.2 Using the Delta Method to Estimate a Minimum or a Maximum. 6.1.3 Fractional Polynomials. 6.2 Factors. 6.2.1 No Other Predictors. 6.2.2 Adding a Predictor: Comparing Regression Lines. 6.2.3 Additional Comments. 6.3 Many Factors. 6.4 Partial One-Dimensional Mean Functions. 6.5 Random Coefficient Models. Problems. 7 Transformations. 7.1 Transformations and Scatterplots. 7.1.1 Power