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A Modern Approach to Regression with R

:	Simon Sheather
:	A Modern Approach to Regression with R
:	Springer-Verlag
:	9780387096087
:	1
:	CHF 63.40
:

:	Naturwissenschaft
:	English

:	398
:	DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on valid models.

	Preface	7
	Contents	11
	Introduction	15
	1.1 Building Valid Models	15
	1.2 Motivating Examples	15
	1.2.1 Assessing the Ability of NFL Kickers	15
	1.2.2 Newspaper Circulation	18
	1.2.3 Menu Pricing in a New Italian Restaurant in New York City	19
	1.2.4 Effect of Wine Critics’ Ratings on Prices of Bordeaux Wines	22
	1.3 Level of Mathematics	27
	Simple Linear Regression	29
	2.1 Introduction and Least Squares Estimates	29
	2.1.1 Simple Linear Regression Models	29
	2.2 Inferences About the Slope and the Intercept	34
	2.2.1 Assumptions Necessary in Order to Make Inferences About the Regression Model	35
	2.2.2 Inferences About the Slope of the Regression Line	35
	2.2.3 Inferences About the Intercept of the Regression Line	37
	2.3 Confidence Intervals for the Population Regression Line	38
	2.4 Prediction Intervals for the Actual Value of Y	39
	2.5 Analysis of Variance	41
	2.6 Dummy Variable Regression	44
	2.7 Derivations of Results	47
	2.7.1 Inferences about the Slope of the Regression Line	48
	2.7.2 Inferences about the Intercept of the Regression Line	49
	2.7.3 Confidence Intervals for the Population Regression Line	50
	2.7.4 Prediction Intervals for the Actual Value of Y	51
	2.8 Exercises	52
	Diagnostics and Transformations for Simple Linear Regression	58
	3.1 Valid and Invalid Regression Models: Anscombe’s Four Data Sets	58
	3.1.1 Residuals	61
	3.1.2 Using Plots of Residuals to Determine Whether the Proposed Regression Model Is a Valid Model	62
	3.1.3 Example of a Quadratic Model	63
	3.2 Regression Diagnostics: Tools for Checking the Validity of a Model	63
	3.2.1 Leverage Points	64
	3.2.2 Standardized Residuals	72
	3.2.3 Recommendations for Handling Outliers and Leverage Points	79
	3.2.4 Assessing the Influence of Certain Cases	80
	3.2.5 Normality of the Errors	82
	3.2.6 Constant Variance	84
	3.3 Transformations	89
	3.3.1 Using Transformations to Stabilize Variance	89
	3.3.2 Using Logarithms to Estimate Percentage Effects	92