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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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||