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Applied regression including computing and graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Applied regression including computing and graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, 1999
Descrizione fisica 1 online resource (632 p.)
Disciplina 519.5
519.536
Altri autori (Persone) WeisbergSanford <1947->
Collana Wiley series in probability and statistics. Texts and references section
Soggetto topico Regression analysis
Multivariate analysis
ISBN 1-282-30751-7
9786612307515
0-470-31694-2
0-470-31778-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied 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
1.6.6 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
3.1 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
4.3.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
5.11 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
6.6.2 Analysis of Variance
Record Nr. UNINA-9910144694803321
Cook R. Dennis  
New York, : Wiley, 1999
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied regression including computing and graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Applied regression including computing and graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, 1999
Descrizione fisica 1 online resource (632 p.)
Disciplina 519.5
519.536
Altri autori (Persone) WeisbergSanford <1947->
Collana Wiley series in probability and statistics. Texts and references section
Soggetto topico Regression analysis
Multivariate analysis
ISBN 1-282-30751-7
9786612307515
0-470-31694-2
0-470-31778-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied 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
1.6.6 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
3.1 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
4.3.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
5.11 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
6.6.2 Analysis of Variance
Record Nr. UNINA-9910830719303321
Cook R. Dennis  
New York, : Wiley, 1999
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied regression including computing and graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Applied regression including computing and graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, 1999
Descrizione fisica 1 online resource (632 p.)
Disciplina 519.5
519.536
Altri autori (Persone) WeisbergSanford <1947->
Collana Wiley series in probability and statistics. Texts and references section
Soggetto topico Regression analysis
Multivariate analysis
ISBN 1-282-30751-7
9786612307515
0-470-31694-2
0-470-31778-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied 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
1.6.6 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
3.1 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
4.3.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
5.11 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
6.6.2 Analysis of Variance
Record Nr. UNINA-9910841171503321
Cook R. Dennis  
New York, : Wiley, 1999
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to envelopes : dimension reduction for efficient estimation in multivariate statistics / / R. Dennis Cook
An introduction to envelopes : dimension reduction for efficient estimation in multivariate statistics / / R. Dennis Cook
Autore Cook R. Dennis
Edizione [First edition.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , 2018
Descrizione fisica 1 online resource (316 pages)
Disciplina 519.5/35
Collana Wiley series in probability and statistics
Soggetto topico Multivariate analysis
Dimension reduction (Statistics)
Soggetto genere / forma Electronic books.
ISBN 1-119-42297-3
1-119-42295-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Response envelopes -- Illustrative analyses using response envelopes -- Partial response envelopes -- Predictor envelopes -- Enveloping multivariate means -- Envelope agorithms -- Envelope exensions -- Inner and scaled envelopes -- Connections and adaptations.
Record Nr. UNINA-9910466744403321
Cook R. Dennis  
Hoboken, NJ : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to envelopes : dimension reduction for efficient estimation in multivariate statistics / / R. Dennis Cook
An introduction to envelopes : dimension reduction for efficient estimation in multivariate statistics / / R. Dennis Cook
Autore Cook R. Dennis
Edizione [First edition.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , 2018
Descrizione fisica 1 online resource (316 pages)
Disciplina 519.5/35
Collana Wiley series in probability and statistics
Soggetto topico Multivariate analysis
Dimension reduction (Statistics)
ISBN 1-119-42296-5
1-119-42297-3
1-119-42295-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Response envelopes -- Illustrative analyses using response envelopes -- Partial response envelopes -- Predictor envelopes -- Enveloping multivariate means -- Envelope agorithms -- Envelope exensions -- Inner and scaled envelopes -- Connections and adaptations.
Record Nr. UNINA-9910530689703321
Cook R. Dennis  
Hoboken, NJ : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to envelopes : dimension reduction for efficient estimation in multivariate statistics / / R. Dennis Cook
An introduction to envelopes : dimension reduction for efficient estimation in multivariate statistics / / R. Dennis Cook
Autore Cook R. Dennis
Edizione [First edition.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , 2018
Descrizione fisica 1 online resource (316 pages)
Disciplina 519.5/35
Collana Wiley series in probability and statistics
Soggetto topico Multivariate analysis
Dimension reduction (Statistics)
ISBN 1-119-42296-5
1-119-42297-3
1-119-42295-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Response envelopes -- Illustrative analyses using response envelopes -- Partial response envelopes -- Predictor envelopes -- Enveloping multivariate means -- Envelope agorithms -- Envelope exensions -- Inner and scaled envelopes -- Connections and adaptations.
Record Nr. UNINA-9910819491103321
Cook R. Dennis  
Hoboken, NJ : , : Wiley, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to regression graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
An introduction to regression graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1994
Descrizione fisica 1 online resource (282 p.)
Disciplina 519.536028566
519.536078
Altri autori (Persone) WeisbergSanford <1947->
Collana Wiley series in probability and mathematical statistics
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods - Data processing
ISBN 1-282-30784-3
9786612307843
0-470-31686-1
0-470-31770-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto An Introduction to Regression Graphics; Contents; Preface; 1 Getting Started; 1.1 Doing the examples; 1.2 A Very Brief Introduction to Xlisp-Stat; 1.2.1 Entering Data; 1.2.2 Working with Lists; 1.2.3 Calculating the Slope and Intercept; 1.2.4 Drawing a Histogram; 1.2.5 Drawing a Scatterplot; 1.2.6 Saving and Printing Text; 1.2.7 Saving and Printing a Graph; 1.2.8 Quitting Xlisp-Stat; 1.3 An Introduction to the R-code; 1.4 Using Your Own Data; 1.5 Getting Help; 1.6 Complements; Exercises; 2 Simple Regression Plots; 2.1 Thinking about Scatterplots; 2.2 Simple Linear Regression
2.3 Assessing Linearity2.3.1 Superimposing the Fitted Line; 2.3.2 Residual Plots; 2.3.3 Average Smoothing; 2.3.4 Regression Smoothing; 2.4 Complements; Exercises; 3 Two-Dimensional Plots; 3.1 Aspect Ratio and Focusing; 3.2 Power Transformations; 3.3 Thinking about Power Transformations; 3.4 Showing Labels and Coordinates; 3.5 Linking Plots; 3.6 Marking and Coloring Points; 3.7 Brushing; 3.8 Name Lists; 3.9 complements; Exercises; 4 Scatterplot Matrices; 4.1 Using a Scatterplot Matrix; 4.2 Identifying Points; 4.3 Transforming Predictors to Linearity; 4.4 Partial Response Plots; 4.5 Complements
Exercises5 Three-Dimensional Plots; 5.1 Viewing a Three-Dimensional Plot; 5.1.1 Rotation Control; 5.1.2 Recalling Views; 5.1.3 Rocking; 5.1.4 Show Axes; 5.1.5 Depth Cuing; 5.2 Scaling and Centering; 5.3 Two-Dimensional Plots fromThree-Dimensional Plots; 5.3.1 Saving h; 5.3.2 Rotation in Two Dimensions; 5.3.3 Extracting a Two-Dimensional Plot; 5.3.4 Summary; 5.4 Removing a Linear Trend in Three-Dimensional Plots; 5.5 Using Uncorrelated Predictors; 5.6 Complements; Exercises; 6 Visualizing Linear Regression with Two Predictors; 6.1 Linear Regression; 6.1.1 The Ideal Summary Plot
6.1.2 Viewing an Ideal Summary Plot When o2 = 06.2 Fitting by Eye; 6.2.1 Fitting by Eye When o2 = 0; 6.2.2 Fitting by Eye When o2 > 0; 6.2.3 Fitting by ols; 6.2.4 Checking a Candidate Summary Plot; 6.3 Correlated Predictors; 6.4 Distribution of the Predictors; 6.4.1 Nonlinear Predictors; 6.4.2 Linear Relationships Between Predictors; 6.4.3 Partial Variance Functions; 6.4.4 Scatterplot Matrices; 6.4.5 Multiple Regression; 6.5 Linear Predictors; 6.6 Complements; Exercises; 7 Visualizing Regression without Linearity; 7.1 General Three-Dimensional Response Plots; 7.1.1 Zero-Dimensional Structure
7.1.2 One-Dimensional Structure7.1.3 Two-Dimensional Structure; 7.2 Example: Australian Athletes Data; 7.3 Example: Ethanol Data; 7.4 Many Predictors; 7.4.1 The One-Dimensional Estimation Result; 7.4.2 An Example with a Nonlinear Response; 7.5 Example: Berkeley Guidance Study for Girls; 7.6 Example: Australian Athletes Again; 7.7 Complements; 7.7.1 Linearity; 7.7.2 ols Summary Plots; 7.7.3 References; Exercises; 8 Finding Dimension; 8.1 Finding Dimension Graphically; 8.1.1 The Inverse Regression Curve; 8.1.2 Inverse Partial Response Plots; 8.2 Sliced Inverse Regression
8.3 Example: Ethanol Data Revisited
Record Nr. UNINA-9910144693403321
Cook R. Dennis  
New York, : Wiley, c1994
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to regression graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
An introduction to regression graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1994
Descrizione fisica 1 online resource (282 p.)
Disciplina 519.536028566
519.536078
Altri autori (Persone) WeisbergSanford <1947->
Collana Wiley series in probability and mathematical statistics
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods - Data processing
ISBN 1-282-30784-3
9786612307843
0-470-31686-1
0-470-31770-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto An Introduction to Regression Graphics; Contents; Preface; 1 Getting Started; 1.1 Doing the examples; 1.2 A Very Brief Introduction to Xlisp-Stat; 1.2.1 Entering Data; 1.2.2 Working with Lists; 1.2.3 Calculating the Slope and Intercept; 1.2.4 Drawing a Histogram; 1.2.5 Drawing a Scatterplot; 1.2.6 Saving and Printing Text; 1.2.7 Saving and Printing a Graph; 1.2.8 Quitting Xlisp-Stat; 1.3 An Introduction to the R-code; 1.4 Using Your Own Data; 1.5 Getting Help; 1.6 Complements; Exercises; 2 Simple Regression Plots; 2.1 Thinking about Scatterplots; 2.2 Simple Linear Regression
2.3 Assessing Linearity2.3.1 Superimposing the Fitted Line; 2.3.2 Residual Plots; 2.3.3 Average Smoothing; 2.3.4 Regression Smoothing; 2.4 Complements; Exercises; 3 Two-Dimensional Plots; 3.1 Aspect Ratio and Focusing; 3.2 Power Transformations; 3.3 Thinking about Power Transformations; 3.4 Showing Labels and Coordinates; 3.5 Linking Plots; 3.6 Marking and Coloring Points; 3.7 Brushing; 3.8 Name Lists; 3.9 complements; Exercises; 4 Scatterplot Matrices; 4.1 Using a Scatterplot Matrix; 4.2 Identifying Points; 4.3 Transforming Predictors to Linearity; 4.4 Partial Response Plots; 4.5 Complements
Exercises5 Three-Dimensional Plots; 5.1 Viewing a Three-Dimensional Plot; 5.1.1 Rotation Control; 5.1.2 Recalling Views; 5.1.3 Rocking; 5.1.4 Show Axes; 5.1.5 Depth Cuing; 5.2 Scaling and Centering; 5.3 Two-Dimensional Plots fromThree-Dimensional Plots; 5.3.1 Saving h; 5.3.2 Rotation in Two Dimensions; 5.3.3 Extracting a Two-Dimensional Plot; 5.3.4 Summary; 5.4 Removing a Linear Trend in Three-Dimensional Plots; 5.5 Using Uncorrelated Predictors; 5.6 Complements; Exercises; 6 Visualizing Linear Regression with Two Predictors; 6.1 Linear Regression; 6.1.1 The Ideal Summary Plot
6.1.2 Viewing an Ideal Summary Plot When o2 = 06.2 Fitting by Eye; 6.2.1 Fitting by Eye When o2 = 0; 6.2.2 Fitting by Eye When o2 > 0; 6.2.3 Fitting by ols; 6.2.4 Checking a Candidate Summary Plot; 6.3 Correlated Predictors; 6.4 Distribution of the Predictors; 6.4.1 Nonlinear Predictors; 6.4.2 Linear Relationships Between Predictors; 6.4.3 Partial Variance Functions; 6.4.4 Scatterplot Matrices; 6.4.5 Multiple Regression; 6.5 Linear Predictors; 6.6 Complements; Exercises; 7 Visualizing Regression without Linearity; 7.1 General Three-Dimensional Response Plots; 7.1.1 Zero-Dimensional Structure
7.1.2 One-Dimensional Structure7.1.3 Two-Dimensional Structure; 7.2 Example: Australian Athletes Data; 7.3 Example: Ethanol Data; 7.4 Many Predictors; 7.4.1 The One-Dimensional Estimation Result; 7.4.2 An Example with a Nonlinear Response; 7.5 Example: Berkeley Guidance Study for Girls; 7.6 Example: Australian Athletes Again; 7.7 Complements; 7.7.1 Linearity; 7.7.2 ols Summary Plots; 7.7.3 References; Exercises; 8 Finding Dimension; 8.1 Finding Dimension Graphically; 8.1.1 The Inverse Regression Curve; 8.1.2 Inverse Partial Response Plots; 8.2 Sliced Inverse Regression
8.3 Example: Ethanol Data Revisited
Record Nr. UNINA-9910830316203321
Cook R. Dennis  
New York, : Wiley, c1994
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to regression graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
An introduction to regression graphics [[electronic resource] /] / R. Dennis Cook, Sanford Weisberg
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1994
Descrizione fisica 1 online resource (282 p.)
Disciplina 519.536028566
519.536078
Altri autori (Persone) WeisbergSanford <1947->
Collana Wiley series in probability and mathematical statistics
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods - Data processing
ISBN 1-282-30784-3
9786612307843
0-470-31686-1
0-470-31770-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto An Introduction to Regression Graphics; Contents; Preface; 1 Getting Started; 1.1 Doing the examples; 1.2 A Very Brief Introduction to Xlisp-Stat; 1.2.1 Entering Data; 1.2.2 Working with Lists; 1.2.3 Calculating the Slope and Intercept; 1.2.4 Drawing a Histogram; 1.2.5 Drawing a Scatterplot; 1.2.6 Saving and Printing Text; 1.2.7 Saving and Printing a Graph; 1.2.8 Quitting Xlisp-Stat; 1.3 An Introduction to the R-code; 1.4 Using Your Own Data; 1.5 Getting Help; 1.6 Complements; Exercises; 2 Simple Regression Plots; 2.1 Thinking about Scatterplots; 2.2 Simple Linear Regression
2.3 Assessing Linearity2.3.1 Superimposing the Fitted Line; 2.3.2 Residual Plots; 2.3.3 Average Smoothing; 2.3.4 Regression Smoothing; 2.4 Complements; Exercises; 3 Two-Dimensional Plots; 3.1 Aspect Ratio and Focusing; 3.2 Power Transformations; 3.3 Thinking about Power Transformations; 3.4 Showing Labels and Coordinates; 3.5 Linking Plots; 3.6 Marking and Coloring Points; 3.7 Brushing; 3.8 Name Lists; 3.9 complements; Exercises; 4 Scatterplot Matrices; 4.1 Using a Scatterplot Matrix; 4.2 Identifying Points; 4.3 Transforming Predictors to Linearity; 4.4 Partial Response Plots; 4.5 Complements
Exercises5 Three-Dimensional Plots; 5.1 Viewing a Three-Dimensional Plot; 5.1.1 Rotation Control; 5.1.2 Recalling Views; 5.1.3 Rocking; 5.1.4 Show Axes; 5.1.5 Depth Cuing; 5.2 Scaling and Centering; 5.3 Two-Dimensional Plots fromThree-Dimensional Plots; 5.3.1 Saving h; 5.3.2 Rotation in Two Dimensions; 5.3.3 Extracting a Two-Dimensional Plot; 5.3.4 Summary; 5.4 Removing a Linear Trend in Three-Dimensional Plots; 5.5 Using Uncorrelated Predictors; 5.6 Complements; Exercises; 6 Visualizing Linear Regression with Two Predictors; 6.1 Linear Regression; 6.1.1 The Ideal Summary Plot
6.1.2 Viewing an Ideal Summary Plot When o2 = 06.2 Fitting by Eye; 6.2.1 Fitting by Eye When o2 = 0; 6.2.2 Fitting by Eye When o2 > 0; 6.2.3 Fitting by ols; 6.2.4 Checking a Candidate Summary Plot; 6.3 Correlated Predictors; 6.4 Distribution of the Predictors; 6.4.1 Nonlinear Predictors; 6.4.2 Linear Relationships Between Predictors; 6.4.3 Partial Variance Functions; 6.4.4 Scatterplot Matrices; 6.4.5 Multiple Regression; 6.5 Linear Predictors; 6.6 Complements; Exercises; 7 Visualizing Regression without Linearity; 7.1 General Three-Dimensional Response Plots; 7.1.1 Zero-Dimensional Structure
7.1.2 One-Dimensional Structure7.1.3 Two-Dimensional Structure; 7.2 Example: Australian Athletes Data; 7.3 Example: Ethanol Data; 7.4 Many Predictors; 7.4.1 The One-Dimensional Estimation Result; 7.4.2 An Example with a Nonlinear Response; 7.5 Example: Berkeley Guidance Study for Girls; 7.6 Example: Australian Athletes Again; 7.7 Complements; 7.7.1 Linearity; 7.7.2 ols Summary Plots; 7.7.3 References; Exercises; 8 Finding Dimension; 8.1 Finding Dimension Graphically; 8.1.1 The Inverse Regression Curve; 8.1.2 Inverse Partial Response Plots; 8.2 Sliced Inverse Regression
8.3 Example: Ethanol Data Revisited
Record Nr. UNINA-9910840532503321
Cook R. Dennis  
New York, : Wiley, c1994
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1998
Descrizione fisica 1 online resource (378 p.)
Disciplina 519.536
519.536028
Collana Wiley series in probability and statistics Probability and statistics section
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods
ISBN 1-282-30757-6
9786612307577
0-470-31693-4
0-470-31777-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Regression Graphics Ideas for Studying Regressions through Graphics; Contents; Preface; 1. Introduction; 1.1. C.C & I,1; 1.1.1. Construction; 1.1.3. Inference; 1.1.2. Characterization; 1.2. Illustrations; 1.2.1. Residuals versus fitted values; 1.2.2. Residuals versus the predictors; 1.2.3. Residuals versus the response; 1.3. On things to come; 1.4. Notational conventions; Problems; 2. Introduction to 2D Scatterplots; 2.1. Response plots in simple regression; 2.2. New Zealand horse mussels; 2.3. Transforming y via inverse response plots; 2.3.1 Response transformations
2.3.2 Response transformations: Mussel data2.4. Danish twins; 2.5. Scatterplot matrices; 2.5.1 Consrruction; 2.5.2 Example; 2.6. Regression graphics in the 1920s; 2.6.1. Ezekiel's successive approximations; 2.6.2. Bean's graphic method; 2.7. Discussion; Problems; 3. Constructing 3D Scatterplots; 3.1. Getting an impression of 3D; 3.2. Depth cuing; 3.3. Scaling; 3.4. Orthogonalization; Problems; 4. Interpreting 3D Scatterplots; 4.1. Haystacks; 4.2. Structural dimensionality; 4.2.1. One predictor; 4.2.2. Two predictors; 4.2.3 Many predictors; 4.3. One-dimensional structure
4.4. Two-dimensional structure4.4.1. Removing linear trends; 4.4.2. Identifying semiparametric regression functions; 4.5. Assessing structural dimensionality; 4.5.1. A visual metaphor for structural dimension; 4.5.2. A first method for deciding d = 1 or 2; 4.5.3. Natural rubber; 4.6. Assessment methods; 4.6.1. Using independence; 4.6.2. Using uncorrelated 2D views; 4.6.3. Uncorrelated 2D views: Haystack data; 4.6.4. Intraslice residuals; 4.6.5. Intraslice orthogonalization; 4.6.6. Mussels again; 4.6.7. Discussion; Problems; 5. Binary Response Variables; 5.1. One predictor; 5.2. Two predictors
7.5.2 Conditions for S ylx1=S(n1)
Record Nr. UNINA-9910144684803321
Cook R. Dennis  
New York, : Wiley, c1998
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui