LEADER 05455nam 2200661Ia 450 001 9910144694803321 005 20170810195445.0 010 $a1-282-30751-7 010 $a9786612307515 010 $a0-470-31694-2 010 $a0-470-31778-7 035 $a(CKB)1000000000687551 035 $a(EBL)469129 035 $a(OCoLC)264624039 035 $a(SSID)ssj0000334792 035 $a(PQKBManifestationID)11929213 035 $a(PQKBTitleCode)TC0000334792 035 $a(PQKBWorkID)10271252 035 $a(PQKB)11426332 035 $a(MiAaPQ)EBC469129 035 $a(PPN)159329159 035 $a(EXLCZ)991000000000687551 100 $a19990119d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied regression including computing and graphics$b[electronic resource] /$fR. Dennis Cook, Sanford Weisberg 210 $aNew York $cWiley$d1999 215 $a1 online resource (632 p.) 225 0 $aWiley series in probability and statistics. Texts and references section 300 $a"A Wiley-Interscience publication." 311 $a0-471-31711-X 320 $aIncludes bibliographical references (p. 571-578) and indexes. 327 $aApplied Regression Including Computing and Graphics; Contents; Preface; PART I INTRODUCTION; 1 Looking Forward and Back; 1.1 Example: Haystack Data; 1.2 Example: Bluegill Data; 1.3 Loading Data into Arc; 1.4 Numerical Summaries; 1.4.1 Display Summaries; 1.4.2 Command Line; 1.4.3 Displaying Data; 1.4.4 Saving Output to a File and Printing; 1 .5 Graphical Summaries; 1.5.1Histograms; 1.5.2 Boxplots; 1.6.Bringing in the Population; 1.6. I The Density Function; 1.6.2 Normal Distribution; I .6.3 Computing Normal Quantiles; 1.6.4 Computing Normal Probabilities; 1.6.5 Boxplots of Normal Data 327 $a1.6.6 The Sampling Distribution of the Mean1.7 Inference; 1.7.1 Sample Mean; 1.7.2 Confidence Interval for the Mean; 1.7.3 Probability of a Record Bluegill; 1.8 Complements; Problems; 2 Introduction to Regression; 2.1 Using Boxplots to Study Length \ Age; 2.2 Using a Scatterplot to Study Length \ Age; 2.3 Mouse Modes; 2.3.1 Show Coordinates Mouse Mode; 2.3.2 Slicing Mode; 2.3.3 Brushing Mode; 2.4 Characterizing Length\ Age; 2.5 Mean and Variance Functions; 2.5.1 Mean Function; 2.5.2 Variance Function; 2.6 Highlights; 2.7 Complements; Problems; 3 Introduction to Smoothing 327 $a3.1 Slicing a Scatterplot3.2 Estimating E(y I x) by Slicing; 3.3 Estimating E(y Ix) by Smoothing; 3.4 Checking a Theory; 3.5 Boxplots; 3.6 Snow Geese; 3.6.1 Snow Goose Regression; 3.6.2 Mean Function; 3.6.3 Variance Function; 3.7 Complements; Problems; 4 Bivariate Distributions; 4.1 General Bivariate Distributions; 4.1.1 Bivariate Densities; 4.1.2 Connecting with Regression; 4.1.3 Independence; 4.1.4 Covariance; 4.1.5 Correlation Coefficient; 4.2 Bivariate Normal Distribution; 4.2.1 Correlation Coefficient in Normal Populations; 4.2.2 Correlation Coefficient in Non-normal Populations 327 $a4.3.Regression in Bivariate Normal Populations4.3.1 Mean Function; 4.3.2 Mean Function in Standardized Variables; 4.3.3 Mean Function as a Straight Line; 4.3.4 Variance Function; 4.4 Smoothing Bivariate Normal Data; 4.5 Complements; 4.5.1 Confidence Interval for a Correlation; 4.5.2 References; Problems; 5 Two-Dimensional Plots; 5.1 Aspect Ratio and Focusing; 5.2 Power Transformations; 5.3 Thinking about Power Transformations; 5.4 Log Transformations; 5.5 Showing Labels and Coordinates; 5.6 Linking Plots; 5.7 Point Symbols and Colors; 5.8 Brushing; 5.9 Name Lists; 5.10 Probability Plots 327 $a5.11 ComplementsProblems; PART II. TOOLS; 6 Simple Linear Regression; 6.1 Simple Linear Regression; 6.2 Least Squares Estimation; 6.2.1 Notation; 6.2.2 The Least Squares Criterion; 6.2.3 Ordinary Least Squares Estimators; 6.2.4 More on Sample Correlation; 6.2.5 Some Properties of Least Squares Estimates; 6.2.6 Estimating the Common Variance, (T*; 6.2.7 Summary; 6.3 Using Arc; 6.3.1 Interpreting the Intercept; 6.4 Inference; 6.4.1 Inferences about Parameters; 6.4.2 Estimating Population Means; 6.4.3 Prediction; 6.5 Forbes' Experiments, Revisited; 6.6 Model Comparison; 6.6.1 Models 327 $a6.6.2 Analysis of Variance 330 $aA step-by-step guide to computing and graphics in regression analysisIn this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis.In 23 co 410 0$aWiley Series in Probability and Statistics 606 $aRegression analysis 606 $aMultivariate analysis 615 0$aRegression analysis. 615 0$aMultivariate analysis. 676 $a519.5 676 $a519.536 700 $aCook$b R. Dennis$089150 701 $aWeisberg$b Sanford$f1947-$0104044 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144694803321 996 $aApplied regression including computing and graphics$9509083 997 $aUNINA