Linear Regression Analysis: Theory and ComputingThis volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields. |
Contents
1 Introduction | 1 |
2 Simple Linear Regression | 9 |
3 Multiple Linear Regression | 41 |
4 Detection of Outliers and Inuential Observations in Multiple Linear Regression | 129 |
5 Model Selection | 157 |
6 Model Diagnostics | 195 |
8 Generalized Linear Models | 269 |
9 Bayesian Linear Regression | 297 |
| 317 | |
| 325 | |
Other editions - View all
Common terms and phrases
algorithm assumption b₁ collinearity confidence interval Consider correlation data points data set degrees of freedom denote density DPROS dummy variables error term error variance example F test fitted values function given heteroscedasticity hypothesis idempotent idempotent matrix independent variables influential observation interaction Intercept ith observation lasso least squares estimation likelihood linear model Linear Regression Analysis linear regression model logistic mean shift outlier method model selection multiple linear regression multiple regression nonlinear P-Value Parameter Estimates population posterior predictors PRESS residual proc reg quadratic form random forests regression analysis regression coefficients regression model regression parameters regressors residual plot response variable ridge regression sample simple linear regression standard studentized residual sum of squares Table test statistic Theorem Theory and Computing variance inflation vector space versus Wald test β₁ βο λβ σ² Χβ