Statistics with R: a beginner's guide
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne
Sage
[2023]
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| Edition: | 2nd edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xix, 369 Seiten Illustrationen, Diagramme |
| ISBN: | 9781529753523 9781529753530 |
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Record in the Search Index
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| adam_text | Contents About the Author Preface to the Second Edition Discover this Textbook s Online Resources! Acknowledgments xi xiii xviii xix 1 Introduction and R Instructions 1 Basic Terminology Data: Qualitative or Quantitative Data: Cross-Sectional or Longitudinal Descriptive Statistics Probability Statistics: Estimation and Inference Summary Appendix: An Introduction to R Some Things to Remember Whenever Using R R Functions Our Approach to Writing This Book Exercises 2 5 7 8 9 9 11 13 34 34 35 35 2 Descriptive Statistics: Tabular and Graphical Methods 37 Methods of Summarizing and Displaying Qualitative Data Methods of Summarizing and Displaying Quantitative Data Cross-Tabulations and Scatter Plots Summary Appendix 38 44 48 53 55 3 Descriptive Statistics: Numerical Methods 63 Measures of Central Tendency Measures of Location Exploratory Data Analysis: The Box Plot Display Measures of Variability The z-Score: A Measure of Relative Location Measures of Association: The Bivariate Case The Geometric Mean Summary Appendix 64 67 69 70 74 77 82 85 89
Viii I CONTENTS 4 Introduction to Probability Some Important Definitions Counting Rules Assigning Probabilities Events and Probabilities Probabilities of Unions and Intersections of Events Conditional Probability Bayes Theorem and Events „ Summary 5 Discrete Probability Distributions Some Important Definitions The Discrete Uniform Probability Distribution The Expected Value and Standard Deviation of a Discrete Random Variable The Binomial Probability Distribution The Poisson Probability Distribution The Hypergeometric Probability Distribution The Hypergeometric Probability Distribution: The General Case Bayes Theorem and Discrete Random Variables Summary Appendix б Continuous Probability Distributions Continuous Uniform Probability Distribution Normal Probability Distribution Exponential Probability Distribution Optional Material: A Derivation of the Cumulative Exponential Probability Function Bayes Theorem and Continuous Random Variables Summary Appendix 97 98 99 109 116 121 1շ4 125 ^ ^2 ^7 I41 442 l4^ 153 157 158 162 171 176 177 181 184 7 Point Estimation and Sampling Distributions 191 Populations and Samples The Simple Random Sample The Sample Statistic: x, s, and p The Sampling Distribution of x The Sampling Distribution of p Some Other Commonly Used Sampling Methods Bayes Theorem: Approximate Bayesian Computation Summary Appendix 193 194 197 197 204 208 211 217 221
CONTENTS 8 Confidence Interval Estimation Interval Estimate of μ When σ Is Known Interval Estimate of μ When σ Is Unknown Sample Size Determination in the Case of μ Interval Estimate of p Sample Size Determination in the Case of p Bayes Theorem: Confidence Intervals or Credible Intervals Summary Appendix 9 Hypothesis Tests: Introduction, Basic Concepts, and an Example Summary 10 Hypothesis Tests about Means and Proportions: Applications The Lower-Tail Hypothesis Test about μ: σ Is Known The Two-Tail Hypothesis Test about μ: σ Is Known The Upper-Tail Hypothesis Test about μ: σ Is Unknown The Two-Tail Hypothesis Test about μ: σ Is Unknown Hypothesis Tests about p Calculating the Probability of a Type II Error: β Adjusting the Sample Size to Control the Size of β Bayes Theorem and an Inferential Approach to p Summary Appendix 11 Comparisons of Means and Proportions The Difference between μ, and μ2: Independent Samples The Difference between μ, and μ2: Paired Samples The Difference between px and p2: Independent Samples Bayes Theorem and the Difference between p՝ and p2 Summary Appendix 12 Simple Linear Regression Simple Linear Regression: The Model The Estimated Regression Equation Goodness of Fit: The Coefficient of Determination, r2 The Hypothesis Test about βι Alternative Approaches to Testing Significance So Far, We Have Tested Only b,. Will We Also Test b0? Assumptions: What Are They? Assumptions: How Are They Validated? 227 229 234 239 241 243 245 246 249 251 262 267 268 271 273 276 278 283 286 288 296 298 301 302 308 314 319 323 328 331 332 333 339 343 349 351 351
352
x Į CONTENTS Optional Material: Derivation of the Expressions for the Least-Squares Estimates of ß0 and β, Bayes Theorem: Using Stan to Estimate the Relationship between Two Variables Summary Appendix 13 Multiple Regression Simple Linear Regression: A Reprise Multiple Regression: The Model Multiple Regression: The Multiple Regression Equation The Estimated Multiple Regression Equation Multiple Regression: Two Independent Variables Assumptions: What Are They? Can We Validate Them? Tests of Significance: The Overall Regression Model Tests of Significance: The Independent Variables There Must Be an Easier Way Than This, Right? Using the Estimated Regression Equation for Prediction Independent Variable Selection: The Best-Subsets Method Logistic Regression: The Zero-One Dependent Variable Bayes Theorem: Stan and Multiple Regression Analysis Summary Appendix Index 357 358 365 371 373 374 378 379 379 380 388 389 392 395 396 397 404 409 413 418 421
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| adam_txt |
Contents About the Author Preface to the Second Edition Discover this Textbook's Online Resources! Acknowledgments xi xiii xviii xix 1 Introduction and R Instructions 1 Basic Terminology Data: Qualitative or Quantitative Data: Cross-Sectional or Longitudinal Descriptive Statistics Probability Statistics: Estimation and Inference Summary Appendix: An Introduction to R Some Things to Remember Whenever Using R R Functions Our Approach to Writing This Book Exercises 2 5 7 8 9 9 11 13 34 34 35 35 2 Descriptive Statistics: Tabular and Graphical Methods 37 Methods of Summarizing and Displaying Qualitative Data Methods of Summarizing and Displaying Quantitative Data Cross-Tabulations and Scatter Plots Summary Appendix 38 44 48 53 55 3 Descriptive Statistics: Numerical Methods 63 Measures of Central Tendency Measures of Location Exploratory Data Analysis: The Box Plot Display Measures of Variability The z-Score: A Measure of Relative Location Measures of Association: The Bivariate Case The Geometric Mean Summary Appendix 64 67 69 70 74 77 82 85 89
Viii I CONTENTS 4 Introduction to Probability Some Important Definitions Counting Rules Assigning Probabilities Events and Probabilities Probabilities of Unions and Intersections of Events Conditional Probability Bayes' Theorem and Events „ Summary 5 Discrete Probability Distributions Some Important Definitions The Discrete Uniform Probability Distribution The Expected Value and Standard Deviation of a Discrete Random Variable The Binomial Probability Distribution The Poisson Probability Distribution The Hypergeometric Probability Distribution The Hypergeometric Probability Distribution: The General Case Bayes' Theorem and Discrete Random Variables Summary Appendix б Continuous Probability Distributions Continuous Uniform Probability Distribution Normal Probability Distribution Exponential Probability Distribution Optional Material: A Derivation of the Cumulative Exponential Probability Function Bayes' Theorem and Continuous Random Variables Summary Appendix 97 98 99 109 116 121 1շ4 125 ^ ^2 ^7 I41 442 l4^ 153 157 158 162 171 176 177 181 184 7 Point Estimation and Sampling Distributions 191 Populations and Samples The Simple Random Sample The Sample Statistic: x, s, and p The Sampling Distribution of x The Sampling Distribution of p Some Other Commonly Used Sampling Methods Bayes Theorem: Approximate Bayesian Computation Summary Appendix 193 194 197 197 204 208 211 217 221
CONTENTS 8 Confidence Interval Estimation Interval Estimate of μ When σ Is Known Interval Estimate of μ When σ Is Unknown Sample Size Determination in the Case of μ Interval Estimate of p Sample Size Determination in the Case of p Bayes' Theorem: Confidence Intervals or Credible Intervals Summary Appendix 9 Hypothesis Tests: Introduction, Basic Concepts, and an Example Summary 10 Hypothesis Tests about Means and Proportions: Applications The Lower-Tail Hypothesis Test about μ: σ Is Known The Two-Tail Hypothesis Test about μ: σ Is Known The Upper-Tail Hypothesis Test about μ: σ Is Unknown The Two-Tail Hypothesis Test about μ: σ Is Unknown Hypothesis Tests about p Calculating the Probability of a Type II Error: β Adjusting the Sample Size to Control the Size of β Bayes' Theorem and an Inferential Approach to p Summary Appendix 11 Comparisons of Means and Proportions The Difference between μ, and μ2: Independent Samples The Difference between μ, and μ2: Paired Samples The Difference between px and p2: Independent Samples Bayes' Theorem and the Difference between p՝ and p2 Summary Appendix 12 Simple Linear Regression Simple Linear Regression: The Model The Estimated Regression Equation Goodness of Fit: The Coefficient of Determination, r2 The Hypothesis Test about βι Alternative Approaches to Testing Significance So Far, We Have Tested Only b,. Will We Also Test b0? Assumptions: What Are They? Assumptions: How Are They Validated? 227 229 234 239 241 243 245 246 249 251 262 267 268 271 273 276 278 283 286 288 296 298 301 302 308 314 319 323 328 331 332 333 339 343 349 351 351
352
x Į CONTENTS Optional Material: Derivation of the Expressions for the Least-Squares Estimates of ß0 and β, Bayes' Theorem: Using Stan to Estimate the Relationship between Two Variables Summary Appendix 13 Multiple Regression Simple Linear Regression: A Reprise Multiple Regression: The Model Multiple Regression: The Multiple Regression Equation The Estimated Multiple Regression Equation Multiple Regression: Two Independent Variables Assumptions: What Are They? Can We Validate Them? Tests of Significance: The Overall Regression Model Tests of Significance: The Independent Variables There Must Be an Easier Way Than This, Right? Using the Estimated Regression Equation for Prediction Independent Variable Selection: The Best-Subsets Method Logistic Regression: The Zero-One Dependent Variable Bayes' Theorem: Stan and Multiple Regression Analysis Summary Appendix Index 357 358 365 371 373 374 378 379 379 380 388 389 392 395 396 397 404 409 413 418 421 |
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| spelling | Stinerock, Robert Verfasser (DE-588)1152271407 aut Statistics with R a beginner's guide Robert Stinerock 2nd edition Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne Sage [2023] © 2023 xix, 369 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Statistik (DE-588)4056995-0 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Statistik (DE-588)4056995-0 s R Programm (DE-588)4705956-4 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033685652&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Statistics with R a beginner's guide |
| title_auth | Statistics with R a beginner's guide |
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| title_full | Statistics with R a beginner's guide Robert Stinerock |
| title_fullStr | Statistics with R a beginner's guide Robert Stinerock |
| title_full_unstemmed | Statistics with R a beginner's guide Robert Stinerock |
| title_short | Statistics with R |
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