دانلود کتاب Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Chapman & Hall/CRC The R Series)

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Cover Half Title Title Page Copyright Page Dedication Table of Contents Foreword Preface About the authors 1: Getting Started with Data in R 1.1 What are R and RStudio? 1.1.1 Installing R and RStudio 1.1.2 Using R via RStudio 1.2 How do I code in R? 1.2.1 Basic programming concepts and terminology 1.2.2 Errors, warnings, and messages 1.2.3 Tips on learning to code 1.3 What are R packages? 1.3.1 Package installation 1.3.2 Package loading 1.3.3 Package use 1.4 Explore your first datasets 1.4.1 nycflights13 package 1.4.2 flights data frame 1.4.3 Exploring data frames 1.4.4 Identification and measurement variables 1.4.5 Help files 1.5 Conclusion 1.5.1 Additional resources 1.5.2 What’s to come? I: Data Science with tidyverse 2: Data Visualization 2.1 The grammar of graphics 2.1.1 Components of the grammar 2.1.2 Gapminder data 2.1.3 Other components 2.1.4 ggplot2 package 2.2 Five named graphs - the 5NG 2.3 5NG#1: Scatterplots 2.3.1 Scatterplots via geom_point 2.3.2 Overplotting 2.3.3 Summary 2.4 5NG#2: Linegraphs 2.4.1 Linegraphs via geom_line 2.4.2 Summary 2.5 5NG#3: Histograms 2.5.1 Histograms via geom_histogram 2.5.2 Adjusting the bins 2.5.3 Summary 2.6 Facets 2.7 5NG#4: Boxplots 2.7.1 Boxplots via geom_boxplot 2.7.2 Summary 2.8 5NG#5: Barplots 2.8.1 Barplots via geom_bar or geom_col 2.8.2 Must avoid pie charts! 2.8.3 Two categorical variables 2.8.4 Summary 2.9 Conclusion 2.9.1 Summary table 2.9.2 Function argument specification 2.9.3 Additional resources 2.9.4 What’s to come 3: Data Wrangling 3.1 The pipe operator: %>% 3.2 filter rows 3.3 summarize variables 3.4 group_by rows 3.4.1 Grouping by more than one variable 3.5 mutate existing variables 3.6 arrange and sort rows 3.7 join data frames 3.7.1 Matching “key” variable names 3.7.2 Different “key” variable names 3.7.3 Multiple “key” variables 3.7.4 Normal forms 3.8 Other verbs 3.8.1 select variables 3.8.2 rename variables 3.8.3 top_n values of a variable 3.9 Conclusion 3.9.1 Summary table 3.9.2 Additional resources 3.9.3 What’s to come? 4: Data Importing and “Tidy” Data 4.1 Importing data 4.1.1 Using the console 4.1.2 Using RStudio’s interface 4.2 “Tidy” data 4.2.1 Definition of “tidy” data 4.2.2 Converting to “tidy” data 4.2.3 nycflights13 package 4.3 Case study: Democracy in Guatemala 4.4 tidyverse package 4.5 Conclusion 4.5.1 Additional resources 4.5.2 What’s to come? II: Data Modeling with moderndive 5: Basic Regression 5.1 One numerical explanatory variable 5.1.1 Exploratory data analysis 5.1.2 Simple linear regression 5.1.3 Observed/fitted values and residuals 5.2 One categorical explanatory variable 5.2.1 Exploratory data analysis 5.2.2 Linear regression 5.2.3 Observed/fitted values and residuals 5.3 Related topics 5.3.1 Correlation is not necessarily causation 5.3.2 Best-fitting line 5.3.3 get_regression_x() functions 5.4 Conclusion 5.4.1 Additional resources 5.4.2 What’s to come? 6: Multiple Regression 6.1 One numerical and one categorical explanatory variable 6.1.1 Exploratory data analysis 6.1.2 Interaction model 6.1.3 Parallel slopes model 6.1.4 Observed/fitted values and residuals 6.2 Two numerical explanatory variables 6.2.1 Exploratory data analysis 6.2.2 Regression plane 6.2.3 Observed/fitted values and residuals 6.3 Related topics 6.3.1 Model selection 6.3.2 Correlation coefficient 6.3.3 Simpson’s Paradox 6.4 Conclusion 6.4.1 Additional resources 6.4.2 What’s to come? III: Statistical Inference with infer 7: Sampling 7.1 Sampling bowl activity 7.1.1 What proportion of this bowl’s balls are red? 7.1.2 Using the shovel once 7.1.3 Using the shovel 33 times 7.1.4 What did we just do? 7.2 Virtual sampling 7.2.1 Using the virtual shovel once 7.2.2 Using the virtual shovel 33 times 7.2.3 Using the virtual shovel 1000 times 7.2.4 Using different shovels 7.3 Sampling framework 7.3.1 Terminology and notation 7.3.2 Statistical definitions 7.3.3 The moral of the story 7.4 Case study: Polls 7.5 Conclusion 7.5.1 Sampling scenarios 7.5.2 Central Limit Theorem 7.5.3 Additional resources 7.5.4 What’s to come? 8: Bootstrapping and Confidence Intervals 8.1 Pennies activity 8.1.1 What is the average year on US pennies in 2019? 8.1.2 Resampling once 8.1.3 Resampling 35 times 8.1.4 What did we just do? 8.2 Computer simulation of resampling 8.2.1 Virtually resampling once 8.2.2 Virtually resampling 35 times 8.2.3 Virtually resampling 1000 times 8.3 Understanding confidence intervals 8.3.1 Percentile method 8.3.2 Standard error method 8.4 Constructing confidence intervals 8.4.1 Original workflow 8.4.2 infer package workflow 8.4.3 Percentile method with infer 8.4.4 Standard error method with infer 8.5 Interpreting confidence intervals 8.5.1 Did the net capture the fish? 8.5.2 Precise and shorthand interpretation 8.5.3 Width of confidence intervals 8.6 Case study: Is yawning contagious? 8.6.1 Mythbusters study data 8.6.2 Sampling scenario 8.6.3 Constructing the confidence interval 8.6.4 Interpreting the confidence interval 8.7 Conclusion 8.7.1 Comparing bootstrap and sampling distributions 8.7.2 Theory-based confidence intervals 8.7.3 Additional resources 8.7.4 What’s to come? 9: Hypothesis Testing 9.1 Promotions activity 9.1.1 Does gender affect promotions at a bank? 9.1.2 Shuffling once 9.1.3 Shuffling 16 times 9.1.4 What did we just do? 9.2 Understanding hypothesis tests 9.3 Conducting hypothesis tests 9.3.1 infer package workflow 9.3.2 Comparison with confidence intervals 9.3.3 “There is only one test” 9.4 Interpreting hypothesis tests 9.4.1 Two possible outcomes 9.4.2 Types of errors 9.4.3 How do we choose alpha? 9.5 Case study: Are action or romance movies rated higher? 9.5.1 IMDb ratings data 9.5.2 Sampling scenario 9.5.3 Conducting the hypothesis test 9.6 Conclusion 9.6.1 Theory-based hypothesis tests 9.6.2 When inference is not needed 9.6.3 Problems with p-values 9.6.4 Additional resources 9.6.5 What’s to come 10: Inference for Regression 10.1 Regression refresher 10.1.1 Teaching evaluations analysis 10.1.2 Sampling scenario 10.2 Interpreting regression tables 10.2.1 Standard error 10.2.2 Test statistic 10.2.3 p-value 10.2.4 Confidence interval 10.2.5 How does R compute the table? 10.3 Conditions for inference for regression 10.3.1 Residuals refresher 10.3.2 Linearity of relationship 10.3.3 Independence of residuals 10.3.4 Normality of residuals 10.3.5 Equality of variance 10.3.6 What’s the conclusion? 10.4 Simulation-based inference for regression 10.4.1 Confidence interval for slope 10.4.2 Hypothesis test for slope 10.5 Conclusion 10.5.1 Theory-based inference for regression 10.5.2 Summary of statistical inference 10.5.3 Additional resources 10.5.4 What’s to come IV: Conclusion 11: Tell Your Story with Data 11.1 Review 11.2 Case study: Seattle house prices 11.2.1 Exploratory data analysis: Part I 11.2.2 Exploratory data analysis: Part II 11.2.3 Regression modeling 11.2.4 Making predictions 11.3 Case study: Effective data storytelling 11.3.1 Bechdel test for Hollywood gender representation 11.3.2 US Births in 1999 11.3.3 Scripts of R code Appendix A: Statistical Background A.1 Basic statistical terms A.1.1 Mean A.1.2 Median A.1.3 Standard deviation A.1.4 Five-number summary A.1.5 Distribution A.1.6 Outliers A.2 Normal distribution A.3 log10 transformations Appendix B: Versions of R Packages Used Bibliography Index