Statistical Inference via Data Science A ModernDive Into R and the Tidyverse 1st edition by Chester Ismay, Albert Kim 9781000763560 1000763560

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ISBN 10: 1000763560
ISBN 13: 9781000763560
Author: Chester Ismay; Albert Y. Kim

Statistical Inference via Data Science A ModernDive Into R and the Tidyverse 1st Table of contents:

1. Getting Started with Data in R
   • What are R and RStudio?
     • Installing R and RStudio
     • Using R via RStudio
   • How do I code in R?
     • Basic programming concepts and terminology
     • Errors, warnings, and messages
     • Tips on learning to code
   • What are R packages?
     • Package installation
     • Package loading
     • Package use
   • Explore your first datasets
     • nycflights13 package
     • flights data frame
     • Exploring data frames
     • Identification and measurement variables
     • Help files
   • Conclusion
     • Additional resources
     • What’s to come?

I. Data Science with tidyverse 2. Data Visualization

• The grammar of graphics
  • Components of the grammar
  • Gapminder data
  • Other components
  • ggplot2 package
• Five named graphs – the 5NG
  • 5NG#1: Scatterplots
    • Scatterplots via geom_point
    • Overplotting
    • Summary
  • 5NG#2: Linegraphs
    • Linegraphs via geom_line
    • Summary
  • 5NG#3: Histograms
    • Histograms via geom_histogram
    • Adjusting the bins
    • Summary
  • Facets
  • 5NG#4: Boxplots
    • Boxplots via geom_boxplot
    • Summary
  • 5NG#5: Barplots
    • Barplots via geom_bar or geom_col
    • Must avoid pie charts!
    • Two categorical variables
    • Summary
• Conclusion
  • Summary table
  • Function argument specification
  • Additional resources
  • What’s to come

3. Data Wrangling

   • The pipe operator: %>%
   • Filter rows
   • Summarize variables
   • Group_by rows
     • Grouping by more than one variable
   • Mutate existing variables
   • Arrange and sort rows
   • Join data frames
     • Matching “key” variable names
     • Different “key” variable names
     • Multiple “key” variables
     • Normal forms
   • Other verbs
     • Select variables
     • Rename variables
     • Top_n values of a variable
   • Conclusion
     • Summary table
     • Additional resources
     • What’s to come?

4. Data Importing and “Tidy” Data

   • Importing data
     • Using the console
     • Using RStudio’s interface
   • “Tidy” data
     • Definition of “tidy” data
     • Converting to “tidy” data
     • nycflights13 package
   • Case study: Democracy in Guatemala
   • tidyverse package
   • Conclusion
     • Additional resources
     • What’s to come?

II. Data Modeling with moderndive 5. Basic Regression

• One numerical explanatory variable
  • Exploratory data analysis
  • Simple linear regression
  • Observed/fitted values and residuals
• One categorical explanatory variable
  • Exploratory data analysis
  • Linear regression
  • Observed/fitted values and residuals
• Related topics
  • Correlation is not necessarily causation
  • Best-fitting line
  • get_regression_x() functions
• Conclusion
  • Additional resources
  • What’s to come?

6. Multiple Regression
   • One numerical and one categorical explanatory variable
     • Exploratory data analysis
     • Interaction model
     • Parallel slopes model
     • Observed/fitted values and residuals
   • Two numerical explanatory variables
     • Exploratory data analysis
     • Regression plane
     • Observed/fitted values and residuals
   • Related topics
     • Model selection
     • Correlation coefficient
     • Simpson’s Paradox
   • Conclusion
     • Additional resources
     • What’s to come?

III. Statistical Inference with infer 7. Sampling

• Sampling bowl activity
  • What proportion of this bowl’s balls are red?
  • Using the shovel once
  • Using the shovel 33 times
  • What did we just do?
• Virtual sampling
  • Using the virtual shovel once
  • Using the virtual shovel 33 times
  • Using the virtual shovel 1000 times
  • Using different shovels
• Sampling framework
  • Terminology and notation
  • Statistical definitions
  • The moral of the story
• Case study: Polls
• Conclusion
  • Sampling scenarios
  • Central Limit Theorem
  • Additional resources
  • What’s to come?

8. Bootstrapping and Confidence Intervals

   • Pennies activity
     • What is the average year on US pennies in 2019?
     • Resampling once
     • Resampling 35 times
     • What did we just do?
   • Computer simulation of resampling
     • Virtually resampling once
     • Virtually resampling 35 times
     • Virtually resampling 1000 times
   • Understanding confidence intervals
     • Percentile method
     • Standard error method
   • Constructing confidence intervals
     • Original workflow
     • infer package workflow
     • Percentile method with infer
     • Standard error method with infer
   • Interpreting confidence intervals
     • Did the net capture the fish?
     • Precise and shorthand interpretation
     • Width of confidence intervals
   • Case study: Is yawning contagious?
     • Mythbusters study data
     • Sampling scenario
     • Constructing the confidence interval
     • Interpreting the confidence interval
   • Conclusion
     • Comparing bootstrap and sampling distributions
     • Theory-based confidence intervals
     • Additional resources
     • What’s to come?

9. Hypothesis Testing

   • Promotions activity
     • Does gender affect promotions at a bank?
     • Shuffling once
     • Shuffling 16 times
     • What did we just do?
   • Understanding hypothesis tests
   • Conducting hypothesis tests
     • infer package workflow
     • Comparison with confidence intervals
     • “There is only one test”
   • Interpreting hypothesis tests
     • Two possible outcomes
     • Types of errors
     • How do we choose alpha?
   • Case study: Are action or romance movies rated higher?
     • IMDb ratings data
     • Sampling scenario
     • Conducting the hypothesis test
   • Conclusion
     • Theory-based hypothesis tests
     • When inference is not needed
     • Problems with p-values
     • Additional resources
     • What’s to come?

10. Inference for Regression

• Regression refresher
  • Teaching evaluations analysis
  • Sampling scenario
• Interpreting regression tables
  • Standard error
  • Test statistic
  • p-value
  • Confidence interval
  • How does R compute the table?
• Conditions for inference for regression
  • Residuals refresher
  • Linearity of relationship
  • Independence of residuals
  • Normality of residuals
  • Equality of variance
  • What’s the conclusion?
• Simulation-based inference for regression
  • Confidence interval for slope
  • Hypothesis test for slope
• Conclusion
  • Theory-based inference for regression
  • Summary of statistical inference
  • Additional resources
  • What’s to come

IV. Conclusion 11. Tell Your Story with Data

• Review
• Case study: Seattle house prices
  • Exploratory data analysis: Part I
  • Exploratory data analysis: Part II
  • Regression modeling
  • Making predictions
• Case study: Effective data storytelling
  • Bechdel test for Hollywood gender representation
  • US Births in 1999
  • Scripts of R code

Appendix A: Statistical Background

• Basic statistical terms
  • Mean
  • Median
  • Standard deviation
  • Five-number summary
  • Distribution
  • Outliers
• Normal distribution
• log10 transformations

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