Regression for health and social science: applied linear models with R
Gespeichert in:
1. Verfasser: | |
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Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore
Cambridge University Press
2022
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Online-Zugang: | Inhaltsverzeichnis Klappentext |
Beschreibung: | xvi, 278 Seiten Illustrationen, Diagramme |
ISBN: | 9781108478182 1108478182 |
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100 | 1 | |a Zelterman, Daniel |e Verfasser |0 (DE-588)171800346 |4 aut | |
245 | 1 | 0 | |a Regression for health and social science |b applied linear models with R |c Daniel Zelterman, Yale University, Connecticut |
264 | 1 | |a Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore |b Cambridge University Press |c 2022 | |
300 | |a xvi, 278 Seiten |b Illustrationen, Diagramme | ||
336 | |b txt |2 rdacontent | ||
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adam_text | Contents Preface Acknowledgments 1 Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 2 What Is Statistics? Statistics in the News: the Weather Map Mathematical Background Calculus Calculus in the News: New-Home Construction A Cautionary Tale Exercises 1.7.1 Motorcycle Accidents 1.7.2 Olympic Records 1.7.3 Gasoline Consumption 1.7.4 Foreign Owners of US Treasury Debt 1.7.5 US Presidents and Stock Market Returns Principles of Statistics 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 The Binomial Distribution Confidence Intervals and the Hubble Constant The Normal Distribution Hypothesis Tests The Student t-Test 2.5.1 An Example in Practice 2.5.2 Read the Data and Perform Simple Checks 2.5.3 Run and Interpret the t-Test The Chi-Squared Test and 2x2 Tables What Are Degrees of Freedom? R, in a Nutshell Survey of the Remainder of the Book page xi xv 1 1 4 6 7 9 11 13 14 14 15 17 17 21 21 27 29 32 36 37 40 42 45 51 52 56
vi Contents 2.10 Exercises 2.10.1 Maintaining Balance 2.10.2 Reading Scores 2.10.3 A Helium-Filled Football 2.10.4 Reexamine the Fusion Times 57 60 61 61 62 3 Introduction to Linear Regression 3.1 Low-Birth-Weight Infants 3.2 The Least-Squares Regression Line 3.3 Regression in R 3.4 Statistics in the News: Future Healthcare Costs 3.5 Exercises 3.5.1 Statistics iņ the News: Savings forMedicare 3.5.2 Arsenic in Drinking Water 3.5.3 Dermatologists’ Fees 3.5.4 Breast Cancer Survival and Climate 3.5.5 Cancer Mortality in Florida 3.5.6 Vital Rates 64 64 65 69 71 72 75 76 78 79 80 82 4 Assessing the Regression 4.1 Correlation 4.2 Statistics in the News: Correlations of theGlobal Economy 4.3 Analysis of Variance 4.4 Model Assumptions and Residual Plots 4.5 Exercises 4.5.1 Food Imports 4.5.2 US Homicide Rates 4.5.3 Statistics in the News: Women Managers 4.5.4 Statistics is More Than Just Numbers 84 84 86 87 91 95 96 97 98 99 5 Multiple Regression and Diagnostics 5.1 Example: Maximum January Temperatures 5.2 Graphical Displays of Multivariate Data 5.3 Leverage and the Hat Matrix Diagonal 5.4 Jackknife Diagnostics 5.5 Partial Correlation 5.6 Model-Building Strategies 5.7 Exercises 5.7.1 University Endowments 5.7.2 Maximum January Temperatures 5.7.3 Heart Surgery Mortality 5.7.4 Characteristics of Cars, 1974 5.7.5 Statistics in Advertising: Wine Prices 5.7.6 Statistics in Finance: Mutual Fund Returns _ 101 101 104 106 110 113 116 120 120 122 123 124 125 128
Contents VII Indicators, Interactions, and Transformations 6.1 Indicator Variables 6.2 Drug Interactions 6.3 Interactions of Explanatory Variables 6.4 Transformations 6.5 Additional Topics: Longitudinal Data 6.6 Exercises 6.6.1 More on Wine Prices 6.6.2 Nicotine Levels in Cigarettes 6.6.3 The Speed of a Reaction 6.6.4 Tumor Growth in Mice 6.6.5 Used Car Prices 6.6.6 Percent Body Fat 6.6.7 Fertility Rates in Switzerland 6.6.8 ELISA 130 130 138 140 144 149 151 152 153 153 154 155 156 157 158 Nonparametric Statistics 7.1 A Test for Medians 7.2 Elementary School Math Achievement Scores 7.3 Rank Sum Test 7.4 Ranking and the Healthiest State 7.5 Nonparametric Regression: LOESS 7.6 Exercises 7.6.1 Cloth Run-Up 7.6.2 Prices of Beanie Babies 7.6.3 The Cracker Diet 160 160 165 167 169 170 173 175 176 177 Logistic Regression 8.1 Example: an Insecticide Experiment 8.2 The Logit Transformation 8.3 Logistic Regression in R 8.4 The New York Mets 8.5 Key Points 8.6 Exercises 8.6.1 A Phase I Clinical Trial in Cancer 8.6.2 Toxoplasmosis in El Salvador 8.6.3 Estimation of the EDoi 8.6.4 Super Bowl XXXVIII 178 178 179 182 186 187 188 189 190 192 193 Diagnostics for Logistic Regression 9.1 A Larger Example 9.2 Residuals for Logistic Regression 9.3 Influence in Logistic Regression 195 195 197 201
vili Contents 9.4 Exercises 9.4.1 Statistics in the News: Sex and Violins 9.4.2 Glove Use among Nurses 9.4.3 Statistics in Sports: Pittsburgh Steelers’ Rushing Game 9.4.4 Climate Records in Washington, DC 205 206 207 209 210 10 Poisson Regression 10.1 Lottery Winners 10.2 Poisson Distribution Basics 10.3 Statistics in the News: Terror Attacks 10.4 Regression Models for Poisson Data 10.5 The Offset 10.6 Exercises 10.6.1 Coronary Bypass Mortality, Revisited 10.6.2 Cases of Mental Illness 10.6.3 Airlines Bump Passengers 10.6.4 Lottery Winners 10.6.5 Species on the Galapagos Islands 10.6.6 Sports Statistics: Pro Bowl Appearances 10.6.7 Cancer Rates in Japan 10.6.8 Tourette’s Syndrome 212 212 212 215 216 221 223 223 223 224 226 226 227 229 231 11 Survival Analysis 11.1 Censoring 11.2 The Survival Curve and its Estimate 11.3 The Log-Rank Test 11.4 Exercises 11.4.1 Cancer of the Bile Duct 11.4.2 Survival of Centenarians 233 233 235 240 243 243 244 12 Proportional Hazards Regression 12.1 The Hazard Function 12.2 The Model of Proportional Hazards Regression 12.3 Proportional Hazards Regression in R 12.4 Exercises 12.4.1 Survival of Halibut 12.4.2 Stanford Heart Transplant Survival 12.4.3 Primary Biliary Cirrhosis 12.4.4 Multiple Myeloma ^ 245 245 247 249 251 251 253 253 254
Contents 13 Review of Methods 13.1 The Appropriate Method 13.2 Other Review Questions Appendix IX 256 256 258 A.l Normal Distribution A.2 Chi-Squared Distribution 263 263 264 Selected Solutions and Hints References Index 266 272 274 Statistical Distributions
This textbook for students in nontechnical scientific fields covers the basics of linear model methods with a miniiņum of mathenļatics, assuming only a precalculus background. Numerous examples drawn from the news and current events, with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards , regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation.
|
adam_txt |
Contents Preface Acknowledgments 1 Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 2 What Is Statistics? Statistics in the News: the Weather Map Mathematical Background Calculus Calculus in the News: New-Home Construction A Cautionary Tale Exercises 1.7.1 Motorcycle Accidents 1.7.2 Olympic Records 1.7.3 Gasoline Consumption 1.7.4 Foreign Owners of US Treasury Debt 1.7.5 US Presidents and Stock Market Returns Principles of Statistics 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 The Binomial Distribution Confidence Intervals and the Hubble Constant The Normal Distribution Hypothesis Tests The Student t-Test 2.5.1 An Example in Practice 2.5.2 Read the Data and Perform Simple Checks 2.5.3 Run and Interpret the t-Test The Chi-Squared Test and 2x2 Tables What Are Degrees of Freedom? R, in a Nutshell Survey of the Remainder of the Book page xi xv 1 1 4 6 7 9 11 13 14 14 15 17 17 21 21 27 29 32 36 37 40 42 45 51 52 56
vi Contents 2.10 Exercises 2.10.1 Maintaining Balance 2.10.2 Reading Scores 2.10.3 A Helium-Filled Football 2.10.4 Reexamine the Fusion Times 57 60 61 61 62 3 Introduction to Linear Regression 3.1 Low-Birth-Weight Infants 3.2 The Least-Squares Regression Line 3.3 Regression in R 3.4 Statistics in the News: Future Healthcare Costs 3.5 Exercises 3.5.1 Statistics iņ the News: Savings forMedicare 3.5.2 Arsenic in Drinking Water 3.5.3 Dermatologists’ Fees 3.5.4 Breast Cancer Survival and Climate 3.5.5 Cancer Mortality in Florida 3.5.6 Vital Rates 64 64 65 69 71 72 75 76 78 79 80 82 4 Assessing the Regression 4.1 Correlation 4.2 Statistics in the News: Correlations of theGlobal Economy 4.3 Analysis of Variance 4.4 Model Assumptions and Residual Plots 4.5 Exercises 4.5.1 Food Imports 4.5.2 US Homicide Rates 4.5.3 Statistics in the News: Women Managers 4.5.4 Statistics is More Than Just Numbers 84 84 86 87 91 95 96 97 98 99 5 Multiple Regression and Diagnostics 5.1 Example: Maximum January Temperatures 5.2 Graphical Displays of Multivariate Data 5.3 Leverage and the Hat Matrix Diagonal 5.4 Jackknife Diagnostics 5.5 Partial Correlation 5.6 Model-Building Strategies 5.7 Exercises 5.7.1 University Endowments 5.7.2 Maximum January Temperatures 5.7.3 Heart Surgery Mortality 5.7.4 Characteristics of Cars, 1974 5.7.5 Statistics in Advertising: Wine Prices 5.7.6 Statistics in Finance: Mutual Fund Returns _ 101 101 104 106 110 113 116 120 120 122 123 124 125 128
Contents VII Indicators, Interactions, and Transformations 6.1 Indicator Variables 6.2 Drug Interactions 6.3 Interactions of Explanatory Variables 6.4 Transformations 6.5 Additional Topics: Longitudinal Data 6.6 Exercises 6.6.1 More on Wine Prices 6.6.2 Nicotine Levels in Cigarettes 6.6.3 The Speed of a Reaction 6.6.4 Tumor Growth in Mice 6.6.5 Used Car Prices 6.6.6 Percent Body Fat 6.6.7 Fertility Rates in Switzerland 6.6.8 ELISA 130 130 138 140 144 149 151 152 153 153 154 155 156 157 158 Nonparametric Statistics 7.1 A Test for Medians 7.2 Elementary School Math Achievement Scores 7.3 Rank Sum Test 7.4 Ranking and the Healthiest State 7.5 Nonparametric Regression: LOESS 7.6 Exercises 7.6.1 Cloth Run-Up 7.6.2 Prices of Beanie Babies 7.6.3 The Cracker Diet 160 160 165 167 169 170 173 175 176 177 Logistic Regression 8.1 Example: an Insecticide Experiment 8.2 The Logit Transformation 8.3 Logistic Regression in R 8.4 The New York Mets 8.5 Key Points 8.6 Exercises 8.6.1 A Phase I Clinical Trial in Cancer 8.6.2 Toxoplasmosis in El Salvador 8.6.3 Estimation of the EDoi 8.6.4 Super Bowl XXXVIII 178 178 179 182 186 187 188 189 190 192 193 Diagnostics for Logistic Regression 9.1 A Larger Example 9.2 Residuals for Logistic Regression 9.3 Influence in Logistic Regression 195 195 197 201
vili Contents 9.4 Exercises 9.4.1 Statistics in the News: Sex and Violins 9.4.2 Glove Use among Nurses 9.4.3 Statistics in Sports: Pittsburgh Steelers’ Rushing Game 9.4.4 Climate Records in Washington, DC 205 206 207 209 210 10 Poisson Regression 10.1 Lottery Winners 10.2 Poisson Distribution Basics 10.3 Statistics in the News: Terror Attacks 10.4 Regression Models for Poisson Data 10.5 The Offset 10.6 Exercises 10.6.1 Coronary Bypass Mortality, Revisited 10.6.2 Cases of Mental Illness 10.6.3 Airlines Bump Passengers 10.6.4 Lottery Winners 10.6.5 Species on the Galapagos Islands 10.6.6 Sports Statistics: Pro Bowl Appearances 10.6.7 Cancer Rates in Japan 10.6.8 Tourette’s Syndrome 212 212 212 215 216 221 223 223 223 224 226 226 227 229 231 11 Survival Analysis 11.1 Censoring 11.2 The Survival Curve and its Estimate 11.3 The Log-Rank Test 11.4 Exercises 11.4.1 Cancer of the Bile Duct 11.4.2 Survival of Centenarians 233 233 235 240 243 243 244 12 Proportional Hazards Regression 12.1 The Hazard Function 12.2 The Model of Proportional Hazards Regression 12.3 Proportional Hazards Regression in R 12.4 Exercises 12.4.1 Survival of Halibut 12.4.2 Stanford Heart Transplant Survival 12.4.3 Primary Biliary Cirrhosis 12.4.4 Multiple Myeloma ^ 245 245 247 249 251 251 253 253 254
Contents 13 Review of Methods 13.1 The Appropriate Method 13.2 Other Review Questions Appendix IX 256 256 258 A.l Normal Distribution A.2 Chi-Squared Distribution 263 263 264 Selected Solutions and Hints References Index 266 272 274 Statistical Distributions
This textbook for students in nontechnical scientific fields covers the basics of linear model methods with a miniiņum of mathenļatics, assuming only a precalculus background. Numerous examples drawn from the news and current events, with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards , regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. |
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spelling | Zelterman, Daniel Verfasser (DE-588)171800346 aut Regression for health and social science applied linear models with R Daniel Zelterman, Yale University, Connecticut Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore Cambridge University Press 2022 xvi, 278 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Erscheint auch als Online-Ausgabe 978-1-108-78450-4 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033197661&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033197661&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
spellingShingle | Zelterman, Daniel Regression for health and social science applied linear models with R |
title | Regression for health and social science applied linear models with R |
title_auth | Regression for health and social science applied linear models with R |
title_exact_search | Regression for health and social science applied linear models with R |
title_exact_search_txtP | Regression for health and social science applied linear models with R |
title_full | Regression for health and social science applied linear models with R Daniel Zelterman, Yale University, Connecticut |
title_fullStr | Regression for health and social science applied linear models with R Daniel Zelterman, Yale University, Connecticut |
title_full_unstemmed | Regression for health and social science applied linear models with R Daniel Zelterman, Yale University, Connecticut |
title_short | Regression for health and social science |
title_sort | regression for health and social science applied linear models with r |
title_sub | applied linear models with R |
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