Statistical Inference via Data Science A ModernDive into R and the Tidyverse 1st edition by Albert Kim,Chester Ismay 0367409828‎ 9780367409821

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ISBN 10:0367409828
ISBN 13:‎ 978-0367409821
Author: Albert Kim,Chester Ismay  

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout.

Features:
● Assumes minimal prerequisites, notably, no prior calculus nor coding experience
● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com
● Centers on simulation-based approaches to statistical inference rather than mathematical formulas
● Uses the infer package for “tidy” and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods
● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com

This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.

 

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

 

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 RSRStudio

 

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

 

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