Regression Analysis By Example Using R 6th Edition by Ali S Hadi, Samprit Chatterjee ISBN 1119830877 9781119830870 by Ali S. Hadi, Samprit Chatterjee 9781119830870, 1119830877 instant download after payment.

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ISBN 10: 1119830877 
ISBN 13: 9781119830870
Author: Ali S Hadi, Samprit Chatterjee

Regression Analysis By Example Using R
A STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS

In the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R.

Readers will also find:

Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis
Updated data sets and examples throughout the book
Complimentary access to a companion website that provides data sets in xlsx, csv, and txt format
Perfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.

Regression Analysis By Example Using R 6th Table of contents:

CHAPTER 1: INTRODUCTION
1.1 WHAT IS REGRESSION ANALYSIS?
1.2 PUBLICLY AVAILABLE DATA SETS
1.3 SELECTED APPLICATIONS OF REGRESSION ANALYSIS
1.4 STEPS IN REGRESSION ANALYSIS
1.5 SCOPE AND ORGANIZATION OF THE BOOK
NOTES
CHAPTER 2: A BRIEF INTRODUCTION TO R
2.1 WHAT IS R AND RSTUDIO?
2.2 INSTALLING R AND RSTUDIO
2.3 GETTING STARTED WITH R
2.4 DATA VALUES AND OBJECTS IN R
2.5 R PACKAGES (LIBRARIES)
2.6 IMPORTING (READING) DATA INTO R WORKSPACE
2.7 WRITING (EXPORTING) DATA TO FILES
2.8 SOME ARITHMETIC AND OTHER OPERATORS
2.9 PROGRAMMING IN R
2.10 BIBLIOGRAPHIC NOTES
NOTE
CHAPTER 3: SIMPLE LINEAR REGRESSION
3.1 INTRODUCTION
3.2 COVARIANCE AND CORRELATION COEFFICIENT
3.3 EXAMPLE: COMPUTER REPAIR DATA
3.4 THE SIMPLE LINEAR REGRESSION MODEL
3.5 PARAMETER ESTIMATION
3.6 TESTS OF HYPOTHESES
3.7 CONFIDENCE INTERVALS
3.8 PREDICTIONS
3.9 MEASURING THE QUALITY OF FIT
3.10 REGRESSION LINE THROUGH THE ORIGIN
3.11 TRIVIAL REGRESSION MODELS
3.12 BIBLIOGRAPHIC NOTES
NOTES
CHAPTER 4: MULTIPLE LINEAR REGRESSION
4.1 INTRODUCTION
4.2 DESCRIPTION OF THE DATA AND MODEL
4.3 EXAMPLE: SUPERVISOR PERFORMANCE DATA
4.4 PARAMETER ESTIMATION
4.5 INTERPRETATIONS OF REGRESSION COEFFICIENTS
4.6 CENTERING AND SCALING
4.7 PROPERTIES OF THE LEAST SQUARES ESTIMATORS
4.8 MULTIPLE CORRELATION COEFFICIENT
4.9 INFERENCE FOR INDIVIDUAL REGRESSION COEFFICIENTS
4.10 TESTS OF HYPOTHESES IN A LINEAR MODEL
4.11 PREDICTIONS
4.12 SUMMARY
NOTES
CHAPTER 5: REGRESSION DIAGNOSTICS: DETECTION OF MODEL VIOLATIONS
5.1 INTRODUCTION
5.2 THE STANDARD REGRESSION ASSUMPTIONS
5.3 VARIOUS TYPES OF RESIDUALS
5.4 GRAPHICAL METHODS
5.5 GRAPHS BEFORE FITTING A MODEL
5.6 GRAPHS AFTER FITTING A MODEL
5.7 CHECKING LINEARITY AND NORMALITY ASSUMPTIONS
5.8 LEVERAGE, INFLUENCE, AND OUTLIERS
5.9 MEASURES OF INFLUENCE
5.10 THE POTENTIAL–RESIDUAL PLOT
5.11 REGRESSION DIAGNOSTICS IN R
5.12 WHAT TO DO WITH THE OUTLIERS?
5.13 ROLE OF VARIABLES IN A REGRESSION EQUATION
5.14 EFFECTS OF AN ADDITIONAL PREDICTOR
5.15 ROBUST REGRESSION
NOTES
CHAPTER 6: QUALITATIVE VARIABLES AS PREDICTORS
6.1 INTRODUCTION
6.2 SALARY SURVEY DATA
6.3 INTERACTION VARIABLES
6.4 SYSTEMS OF REGRESSION EQUATIONS: COMPARING TWO GROUPS
6.5 OTHER APPLICATIONS OF INDICATOR VARIABLES
6.6 SEASONALITY
6.7 STABILITY OF REGRESSION PARAMETERS OVER TIME
NOTES
CHAPTER 7: TRANSFORMATION OF VARIABLES
7.1 INTRODUCTION
7.2 TRANSFORMATIONS TO ACHIEVE LINEARITY
7.3 BACTERIA DEATHS DUE TO X-RAY RADIATION
7.4 TRANSFORMATIONS TO STABILIZE VARIANCE
7.5 DETECTION OF HETEROSCEDASTIC ERRORS
7.6 REMOVAL OF HETEROSCEDASTICITY
7.7 WEIGHTED LEAST SQUARES
7.8 LOGARITHMIC TRANSFORMATION OF DATA
7.9 POWER TRANSFORMATION
7.10 SUMMARY
NOTES
CHAPTER 8: WEIGHTED LEAST SQUARES
8.1 INTRODUCTION
8.2 HETEROSCEDASTIC MODELS
8.3 TWO-STAGE ESTIMATION
8.4 EDUCATION EXPENDITURE DATA
8.5 FITTING A DOSE–RESPONSE RELATIONSHIP CURVE
NOTES
CHAPTER 9: THE PROBLEM OF CORRELATED ERRORS
9.1 INTRODUCTION: AUTOCORRELATION
9.2 CONSUMER EXPENDITURE AND MONEY STOCK
9.3 DURBIN–WATSON STATISTIC
9.4 REMOVAL OF AUTOCORRELATION BY TRANSFORMATION
9.5 ITERATIVE ESTIMATION WITH AUTOCORRELATED ERRORS
9.6 AUTOCORRELATION AND MISSING VARIABLES
9.7 ANALYSIS OF HOUSING STARTS
9.8 LIMITATIONS OF THE DURBIN–WATSON STATISTIC
9.9 INDICATOR VARIABLES TO REMOVE SEASONALITY
9.10 REGRESSING TWO TIME SERIES
NOTES
CHAPTER 10: ANALYSIS OF COLLINEAR DATA
10.1 INTRODUCTION
10.2 EFFECTS OF COLLINEARITY ON INFERENCE
10.3 EFFECTS OF COLLINEARITY ON FORECASTING
10.4 DETECTION OF COLLINEARITY
NOTES
CHAPTER 11: WORKING WITH COLLINEAR DATA
11.1 INTRODUCTION
11.2 PRINCIPAL COMPONENTS
11.3 COMPUTATIONS USING PRINCIPAL COMPONENTS
11.4 IMPOSING CONSTRAINTS
11.5 SEARCHING FOR LINEAR FUNCTIONS OF THE β'S
11.6 BIASED ESTIMATION OF REGRESSION COEFFICIENTS
11.7 PRINCIPAL COMPONENTS REGRESSION
11.8 REDUCTION OF COLLINEARITY IN THE ESTIMATION DATA
11.9 CONSTRAINTS ON THE REGRESSION COEFFICIENTS
11.10 PRINCIPAL COMPONENTS REGRESSION: A CAUTION
11.11 RIDGE REGRESSION
11.12 ESTIMATION BY THE RIDGE METHOD
11.13 RIDGE REGRESSION: SOME REMARKS
11.14 SUMMARY
11.15 BIBLIOGRAPHIC NOTES
NOTES
CHAPTER 12: VARIABLE SELECTION PROCEDURES
12.1 INTRODUCTION
12.2 FORMULATION OF THE PROBLEM
12.3 CONSEQUENCES OF VARIABLES DELETION
12.4 USES OF REGRESSION EQUATIONS
12.5 CRITERIA FOR EVALUATING EQUATIONS
12.6 COLLINEARITY AND VARIABLE SELECTION
12.7 EVALUATING ALL POSSIBLE EQUATIONS
12.8 VARIABLE SELECTION PROCEDURES
12.9 GENERAL REMARKS ON VARIABLE SELECTION METHODS
12.10 A STUDY OF SUPERVISOR PERFORMANCE
12.11 VARIABLE SELECTION WITH COLLINEAR DATA
12.12 THE HOMICIDE DATA
12.13 VARIABLE SELECTION USING RIDGE REGRESSION
12.14 SELECTION OF VARIABLES IN AN AIR POLLUTION STUDY
12.15 A POSSIBLE STRATEGY FOR FITTING REGRESSION MODELS
12.16 BIBLIOGRAPHIC NOTES
NOTES
CHAPTER 13: LOGISTIC REGRESSION
13.1 INTRODUCTION
13.2 MODELING QUALITATIVE DATA
13.3 THE LOGIT MODEL
13.4 EXAMPLE: ESTIMATING PROBABILITY OF BANKRUPTCIES
13.5 LOGISTIC REGRESSION DIAGNOSTICS
13.6 DETERMINATION OF VARIABLES TO RETAIN
13.7 JUDGING THE FIT OF A LOGISTIC REGRESSION
13.8 THE MULTINOMIAL LOGIT MODEL
13.9 CLASSIFICATION PROBLEM: ANOTHER APPROACH
NOTES
CHAPTER 14: FURTHER TOPICS
14.1 INTRODUCTION
14.2 GENERALIZED LINEAR MODEL
14.3 POISSON REGRESSION MODEL
14.4 INTRODUCTION OF NEW DRUGS
14.5 ROBUST REGRESSION
14.6 FITTING A QUADRATIC MODEL
14.7 DISTRIBUTION OF PCB IN U.S. BAYS
NOTES

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Tags: Ali S Hadi, Samprit Chatterjee, Regression, Analysis