{"product_id":"regression-analysis-by-example-using-r-hardback-9781119830870","title":"Regression Analysis By Example Using R (Hardback) 9781119830870","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eRegression Analysis By Example Using R\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAli S. Hadi (Author), Samprit Chatterjee (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119830870, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 September 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e23.6 x 15.8 x 3.3 cm, 0.658 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eRegression Analysis By Example Using R\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn the newly revised sixth edition of \u003ci\u003eRegression Analysis By Example Using R\u003c\/i\u003e, 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.\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eReaders will also find:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eReorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis\u003c\/li\u003e \u003cli\u003eUpdated data sets and examples throughout the book\u003c\/li\u003e \u003cli\u003eComplimentary access to a companion website that provides data sets in xlsx, csv, and txt format\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, \u003ci\u003eRegression Analysis By Example Using R \u003c\/i\u003ewill also benefit readers who need a reference for quick updates on regression methods and applications.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What Is Regression Analysis? 1\u003c\/p\u003e \u003cp\u003e1.2 Publicly Available Data Sets 2\u003c\/p\u003e \u003cp\u003e1.3 Selected Applications of Regression Analysis 3\u003c\/p\u003e \u003cp\u003e1.3.1 Agricultural Sciences 3\u003c\/p\u003e \u003cp\u003e1.3.2 Industrial and Labor Relations 4\u003c\/p\u003e \u003cp\u003e1.3.3 Government 5\u003c\/p\u003e \u003cp\u003e1.3.4 History 5\u003c\/p\u003e \u003cp\u003e1.3.5 Environmental Sciences 6\u003c\/p\u003e \u003cp\u003e1.3.6 Industrial Production 6\u003c\/p\u003e \u003cp\u003e1.3.7 The Space Shuttle Challenger 7\u003c\/p\u003e \u003cp\u003e1.3.8 Cost of Health Care 7\u003c\/p\u003e \u003cp\u003e1.4 Steps in Regression Analysis 7\u003c\/p\u003e \u003cp\u003e1.4.1 Statement of the Problem 9\u003c\/p\u003e \u003cp\u003e1.4.2 Selection of Potentially Relevant Variables 9\u003c\/p\u003e \u003cp\u003e1.4.3 Data Collection 9\u003c\/p\u003e \u003cp\u003e1.4.4 Model Specification 10\u003c\/p\u003e \u003cp\u003e1.4.5 Method of Fitting 12\u003c\/p\u003e \u003cp\u003e1.4.6 Model Fitting 13\u003c\/p\u003e \u003cp\u003e1.4.7 Model Criticism and Selection 14\u003c\/p\u003e \u003cp\u003e1.4.8 Objectives of Regression Analysis 15\u003c\/p\u003e \u003cp\u003e1.5 Scope and Organization of the Book 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Brief Introduction to R 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 What Is R and RStudio? 19\u003c\/p\u003e \u003cp\u003e2.2 Installing R and RStudio 20\u003c\/p\u003e \u003cp\u003e2.3 Getting Started With R 21\u003c\/p\u003e \u003cp\u003e2.3.1 Command Level Prompt 21\u003c\/p\u003e \u003cp\u003e2.3.2 Calculations Using R 22\u003c\/p\u003e \u003cp\u003e2.3.3 Editing Your R Code 24\u003c\/p\u003e \u003cp\u003e2.3.4 Best Practice: Object Names in R 25\u003c\/p\u003e \u003cp\u003e2.4 Data Values and Objects in R 25\u003c\/p\u003e \u003cp\u003e2.4.1 Types of Data Values in R 25\u003c\/p\u003e \u003cp\u003e2.4.2 Types (Structures) of Objects in R 28\u003c\/p\u003e \u003cp\u003e2.4.3 Object Attributes 34\u003c\/p\u003e \u003cp\u003e2.4.4 Testing (Checking) Object Type 34\u003c\/p\u003e \u003cp\u003e2.4.5 Changing Object Type 34\u003c\/p\u003e \u003cp\u003e2.5 R Packages (Libraries) 35\u003c\/p\u003e \u003cp\u003e2.5.1 Installing R Packages 35\u003c\/p\u003e \u003cp\u003e2.5.2 Name Spaces 36\u003c\/p\u003e \u003cp\u003e2.5.3 Updating R 37\u003c\/p\u003e \u003cp\u003e2.5.4 Datasets in R Packages 37\u003c\/p\u003e \u003cp\u003e2.6 Importing (Reading) Data into R Workspace 37\u003c\/p\u003e \u003cp\u003e2.6.1 Best Practice: Working Directory 38\u003c\/p\u003e \u003cp\u003e2.6.2 Reading ASCII (Text) Files 38\u003c\/p\u003e \u003cp\u003e2.6.3 Reading CSV Files 40\u003c\/p\u003e \u003cp\u003e2.6.4 Reading Excel Files 40\u003c\/p\u003e \u003cp\u003e2.6.5 Reading Files from the Internet 41\u003c\/p\u003e \u003cp\u003e2.7 Writing (Exporting) Data to Files 42\u003c\/p\u003e \u003cp\u003e2.7.1 Diverting Normal R Output to a File 42\u003c\/p\u003e \u003cp\u003e2.7.2 Saving Graphs in Files 42\u003c\/p\u003e \u003cp\u003e2.7.3 Exporting Data to Files 43\u003c\/p\u003e \u003cp\u003e2.8 Some Arithmetic and Other Operators 43\u003c\/p\u003e \u003cp\u003e2.8.1 Vectors 43\u003c\/p\u003e \u003cp\u003e2.8.2 Matrix Computations 45\u003c\/p\u003e \u003cp\u003e2.9 Programming in R 50\u003c\/p\u003e \u003cp\u003e2.9.1 Best Practice: Script Files 50\u003c\/p\u003e \u003cp\u003e2.9.2 Some Useful Commands or Functions 50\u003c\/p\u003e \u003cp\u003e2.9.3 Conditional Execution 51\u003c\/p\u003e \u003cp\u003e2.9.4 Loops 53\u003c\/p\u003e \u003cp\u003e2.9.5 Functions and Functionals 54\u003c\/p\u003e \u003cp\u003e2.9.6 User Defined Functions 55\u003c\/p\u003e \u003cp\u003e2.10 Bibliographic Notes 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Simple Linear Regression 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 65\u003c\/p\u003e \u003cp\u003e3.2 Covariance and Correlation Coefficient 65\u003c\/p\u003e \u003cp\u003e3.3 Example: Computer Repair Data 69\u003c\/p\u003e \u003cp\u003e3.4 The Simple Linear Regression Model 72\u003c\/p\u003e \u003cp\u003e3.5 Parameter Estimation 73\u003c\/p\u003e \u003cp\u003e3.6 Tests of Hypotheses 77\u003c\/p\u003e \u003cp\u003e3.7 Confidence Intervals 82\u003c\/p\u003e \u003cp\u003e3.8 Predictions 83\u003c\/p\u003e \u003cp\u003e3.9 Measuring the Quality of Fit 84\u003c\/p\u003e \u003cp\u003e3.10 Regression Line Through the Origin 88\u003c\/p\u003e \u003cp\u003e3.11 Trivial Regression Models 89\u003c\/p\u003e \u003cp\u003e3.12 Bibliographic Notes 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multiple Linear Regression 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 97\u003c\/p\u003e \u003cp\u003e4.2 Description of the Data and Model 97\u003c\/p\u003e \u003cp\u003e4.3 Example: Supervisor Performance Data 98\u003c\/p\u003e \u003cp\u003e4.4 Parameter Estimation 100\u003c\/p\u003e \u003cp\u003e4.5 Interpretations of Regression Coefficients 101\u003c\/p\u003e \u003cp\u003e4.6 Centering and Scaling 104\u003c\/p\u003e \u003cp\u003e4.6.1 Centering and Scaling in Intercept Models 104\u003c\/p\u003e \u003cp\u003e4.6.2 Scaling in No-Intercept Models 105\u003c\/p\u003e \u003cp\u003e4.7 Properties of the Least Squares Estimators 106\u003c\/p\u003e \u003cp\u003e4.8 Multiple Correlation Coefficient 107\u003c\/p\u003e \u003cp\u003e4.9 Inference for Individual Regression Coefficients 108\u003c\/p\u003e \u003cp\u003e4.10 Tests of Hypotheses in a Linear Model 111\u003c\/p\u003e \u003cp\u003e4.10.1 Testing All Regression Coefficients Equal to Zero\u003c\/p\u003e \u003cp\u003e4.10.2 Testing a Subset of Regression Coefficients Equal to 113\u003c\/p\u003e \u003cp\u003e4.10.3 Testing the Equality of Regression Coefficients\u003c\/p\u003e \u003cp\u003e4.10.4 Estimating and Testing of Regression Parameters 118\u003c\/p\u003e \u003cp\u003e4.11 Predictions 121\u003c\/p\u003e \u003cp\u003e4.12 Summary 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Regression Diagnostics: Detection of Model Violations 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 131\u003c\/p\u003e \u003cp\u003e5.2 The Standard Regression Assumptions 132\u003c\/p\u003e \u003cp\u003e5.3 Various Types of Residuals 134\u003c\/p\u003e \u003cp\u003e5.4 Graphical Methods 136\u003c\/p\u003e \u003cp\u003e5.5 Graphs Before Fitting a Model 139\u003c\/p\u003e \u003cp\u003e5.5.1 One-Dimensional Graphs 139 5.5.2 Two-Dimensional Graphs 140\u003c\/p\u003e \u003cp\u003e5.5.3 Rotating Plots 142\u003c\/p\u003e \u003cp\u003e5.5.4 Dynamic Graphs 142\u003c\/p\u003e \u003cp\u003e5.6 Graphs After Fitting a Model 143\u003c\/p\u003e \u003cp\u003e5.7 Checking Linearity and Normality Assumptions 143\u003c\/p\u003e \u003cp\u003e5.8 Leverage, Influence, and Outliers 144\u003c\/p\u003e \u003cp\u003e5.8.1 Outliers in the Response Variable 146\u003c\/p\u003e \u003cp\u003e5.8.2 Outliers in the Predictors 146\u003c\/p\u003e \u003cp\u003e5.8.3 Masking and Swamping Problems 147\u003c\/p\u003e \u003cp\u003e5.9 Measures of Influence 148\u003c\/p\u003e \u003cp\u003e5.9.1 Cook’s Distance 150\u003c\/p\u003e \u003cp\u003e5.9.2 Welsch and Kuh Measure 151\u003c\/p\u003e \u003cp\u003e5.9.3 Hadi’s Influence Measure 151\u003c\/p\u003e \u003cp\u003e5.10 The Potential-Residual Plot 152\u003c\/p\u003e \u003cp\u003e5.11 Regression Diagnostics in R 154 5.12 What to Do with the Outliers? 155\u003c\/p\u003e \u003cp\u003e5.13 Role of Variables in a Regression Equation 156\u003c\/p\u003e \u003cp\u003e5.11.1 Added-Variable Plot 156\u003c\/p\u003e \u003cp\u003e5.11.2 Residual Plus Component Plot 157\u003c\/p\u003e \u003cp\u003e5.14 Effects of an Additional Predictor 159\u003c\/p\u003e \u003cp\u003e5.15 Robust Regression 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Qualitative Variables as Predictors 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 167\u003c\/p\u003e \u003cp\u003e6.2 Salary Survey Data 168\u003c\/p\u003e \u003cp\u003e6.3 Interaction Variables 171\u003c\/p\u003e \u003cp\u003e6.4 Systems of Regression Equations 175\u003c\/p\u003e \u003cp\u003e6.4.1 Models with Different Slopes and Different Intercepts 176\u003c\/p\u003e \u003cp\u003e6.4.2 Models with Same Slope and Different Intercepts 183\u003c\/p\u003e \u003cp\u003e6.4.3 Models with Same Intercept and Different Slopes 184\u003c\/p\u003e \u003cp\u003e6.5 Other Applications of Indicator Variables 185\u003c\/p\u003e \u003cp\u003e6.6 Seasonality 186\u003c\/p\u003e \u003cp\u003e6.7 Stability of Regression Parameters Over Time 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Transformation of Variables 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 195\u003c\/p\u003e \u003cp\u003e7.2 Transformations to Achieve Linearity 197\u003c\/p\u003e \u003cp\u003e7.3 Bacteria Deaths Due to X-Ray Radiation 199\u003c\/p\u003e \u003cp\u003e7.3.1 Inadequacy of a Linear Model 200\u003c\/p\u003e \u003cp\u003e7.3.2 Logarithmic Transformation for Achieving Linearity 201\u003c\/p\u003e \u003cp\u003e7.4 Transformations to Stabilize Variance 203\u003c\/p\u003e \u003cp\u003e7.5 Detection of Heteroscedastic Errors 208\u003c\/p\u003e \u003cp\u003e7.6 Removal of Heteroscedasticity 210\u003c\/p\u003e \u003cp\u003e7.7 Weighted Least Squares 211\u003c\/p\u003e \u003cp\u003e7.8 Logarithmic Transformation of Data 212\u003c\/p\u003e \u003cp\u003e7.9 Power Transformation 213\u003c\/p\u003e \u003cp\u003e7.10 Summary 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Weighted Least Squares 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 223\u003c\/p\u003e \u003cp\u003e8.2 Heteroscedastic Models 224\u003c\/p\u003e \u003cp\u003e8.2.1 Supervisors Data 224\u003c\/p\u003e \u003cp\u003e8.2.2 College Expense Data 226\u003c\/p\u003e \u003cp\u003e8.3 Two-Stage Estimation 227\u003c\/p\u003e \u003cp\u003e8.4 Education Expenditure Data 229\u003c\/p\u003e \u003cp\u003e8.5 Fitting a Dose-Response Relationship Curve 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The Problem of Correlated Errors 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction: Autocorrelation 241\u003c\/p\u003e \u003cp\u003e9.2 Consumer Expenditure and Money Stock 242\u003c\/p\u003e \u003cp\u003e9.3 Durbin-Watson Statistic 245\u003c\/p\u003e \u003cp\u003e9.4 Removal of Autocorrelation by Transformation 246\u003c\/p\u003e \u003cp\u003e9.5 Iterative Estimation with Autocorrelated Errors 249\u003c\/p\u003e \u003cp\u003e9.6 Autocorrelation and Missing Variables 250\u003c\/p\u003e \u003cp\u003e9.7 Analysis of Housing Starts 251\u003c\/p\u003e \u003cp\u003e9.8 Limitations of the Durbin-Watson Statistic 253\u003c\/p\u003e \u003cp\u003e9.9 Indicator Variables to Remove Seasonality 255\u003c\/p\u003e \u003cp\u003e9.10 Regressing Two Time Series 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Analysis of Collinear Data 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 261\u003c\/p\u003e \u003cp\u003e10.2 Effects of Collinearity on Inference 262\u003c\/p\u003e \u003cp\u003e10.3 Effects of Collinearity on Forecasting 267\u003c\/p\u003e \u003cp\u003eCONTENTS\u003c\/p\u003e \u003cp\u003e10.4 Detection of Collinearity 271\u003c\/p\u003e \u003cp\u003e10.4.1 Simple Signs of Collinearity 271\u003c\/p\u003e \u003cp\u003e10.4.2 Variance Inflation Factors 274\u003c\/p\u003e \u003cp\u003e10.4.3 The Condition Indices 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Working With Collinear Data 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 283\u003c\/p\u003e \u003cp\u003e11.2 Principal Components 283\u003c\/p\u003e \u003cp\u003e11.3 Computations Using Principal Components 287\u003c\/p\u003e \u003cp\u003e11.4 Imposing Constraints 289\u003c\/p\u003e \u003cp\u003e11.5 Searching for Linear Functions of the β's 292\u003c\/p\u003e \u003cp\u003e11.6 Biased Estimation of Regression Coefficients 295\u003c\/p\u003e \u003cp\u003e11.7 Principal Components Regression 296\u003c\/p\u003e \u003cp\u003e11.8 Reduction of Collinearity in the Estimation Data 298\u003c\/p\u003e \u003cp\u003e11.9 Constraints on the Regression Coefficients 300\u003c\/p\u003e \u003cp\u003e11.10 Principal Components Regression: A Caution 301\u003c\/p\u003e \u003cp\u003e11.11 Ridge Regression 303\u003c\/p\u003e \u003cp\u003e11.12 Estimation by the Ridge Method 305\u003c\/p\u003e \u003cp\u003e11.13 Ridge Regression: Some Remarks 308\u003c\/p\u003e \u003cp\u003e11.14 Summary 311\u003c\/p\u003e \u003cp\u003e11.15 Bibliographic Notes 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Variable Selection Procedures 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 321\u003c\/p\u003e \u003cp\u003e12.2 Formulation of the Problem 322\u003c\/p\u003e \u003cp\u003e12.3 Consequences of Variables Deletion 322\u003c\/p\u003e \u003cp\u003e12.4 Uses of Regression Equations 324\u003c\/p\u003e \u003cp\u003e12.4.1 Description and Model Building 324\u003c\/p\u003e \u003cp\u003e12.4.2 Estimation and Prediction 324\u003c\/p\u003e \u003cp\u003e12.4.3 Control 324\u003c\/p\u003e \u003cp\u003e12.5 Criteria for Evaluating Equations 325\u003c\/p\u003e \u003cp\u003e12.5.1 Residual Mean Square 325\u003c\/p\u003e \u003cp\u003e12.5.2 Mallows Cp 326\u003c\/p\u003e \u003cp\u003e12.5.3 Information Criteria 327\u003c\/p\u003e \u003cp\u003e12.6 Collinearity and Variable Selection 328\u003c\/p\u003e \u003cp\u003e12.7 Evaluating All Possible Equations 328\u003c\/p\u003e \u003cp\u003e12.8 Variable Selection Procedures 329\u003c\/p\u003e \u003cp\u003e12.8.1 Forward Selection Procedure 329\u003c\/p\u003e \u003cp\u003e12.8.2 Backward Elimination Procedure 330\u003c\/p\u003e \u003cp\u003e12.8.3 Stepwise Method 330\u003c\/p\u003e \u003cp\u003e12.9 General Remarks on Variable Selection Methods 331\u003c\/p\u003e \u003cp\u003e12.10 A Study of Supervisor Performance 332\u003c\/p\u003e \u003cp\u003e12.11 Variable Selection with Collinear Data 336\u003c\/p\u003e \u003cp\u003e12.12 The Homicide Data 336\u003c\/p\u003e \u003cp\u003e12.13 Variable Selection Using Ridge Regression 339\u003c\/p\u003e \u003cp\u003e12.14 Selection of Variables in an Air Pollution Study 339\u003c\/p\u003e \u003cp\u003e12.15 A Possible Strategy for Fitting Regression Models 345\u003c\/p\u003e \u003cp\u003e12.16 Bibliographic Notes 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Logistic Regression 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 353\u003c\/p\u003e \u003cp\u003e13.2 Modeling Qualitative Data 354\u003c\/p\u003e \u003cp\u003e13.3 The Logit Model 354\u003c\/p\u003e \u003cp\u003e13.4 Example: Estimating Probability of Bankruptcies 356\u003c\/p\u003e \u003cp\u003e13.5 Logistic Regression Diagnostics 358\u003c\/p\u003e \u003cp\u003e13.6 Determination of Variables to Retain 359\u003c\/p\u003e \u003cp\u003e13.7 Judging the Fit of a Logistic Regression 362\u003c\/p\u003e \u003cp\u003e13.8 The Multinomial Logit Model 364\u003c\/p\u003e \u003cp\u003e13.8.1 Multinomial Logistic Regression 364\u003c\/p\u003e \u003cp\u003e13.8.2 Example: Determining Chemical Diabetes 365\u003c\/p\u003e \u003cp\u003e13.8.3 Ordinal Logistic Regression 368\u003c\/p\u003e \u003cp\u003e13.8.4 Example: Determining Chemical Diabetes Revisited 368\u003c\/p\u003e \u003cp\u003e13.9 Classification Problem: Another Approach 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Further Topics 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 375\u003c\/p\u003e \u003cp\u003e14.2 Generalized Linear Model 375\u003c\/p\u003e \u003cp\u003e14.3 Poisson Regression Model 376\u003c\/p\u003e \u003cp\u003e14.4 Introduction of New Drugs 377\u003c\/p\u003e \u003cp\u003e14.5 Robust Regression 378\u003c\/p\u003e \u003cp\u003e14.6 Fitting a Quadratic Model 379\u003c\/p\u003e \u003cp\u003e14.7 Distribution of PCB in U.S. Bays 381\u003c\/p\u003e \u003cp\u003eExercises 384\u003c\/p\u003e \u003cp\u003eReferences 385\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52507275067672,"sku":"9781119830870","price":83.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119830870.jpg?v=1786443184","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/regression-analysis-by-example-using-r-hardback-9781119830870","provider":"Freshly Printed Books","version":"1.0","type":"link"}