Statistics with R - Harris, Jenine K

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Résumé :
PREFACE
ABOUT THE AUTHOR
Chapter 1: Preparing Data for Analysis and Visualization in R: The R-Team and the Pot Policy Problem
1.1 Choosing and learning R
1.2 Learning R with publicly available data
1.3 Achievements to unlock
1.4 The tricky weed problem
1.5 Achievement 1: Observations and variables
1.6 Achievement 2: Using reproducible research practices
1.7 Achievement 3: Understanding and changing data types
1.8 Achievement 4: Entering or loading data into R
1.9 Achievement 5: Identifying and treating missing values
1.10 Achievement 6: Building a basic bar chart
1.11 Chapter summary
Chapter 2: Computing and Reporting Descriptive Statistics: The R-Team and the Troubling Transgender Health Care Problem
2.1 Achievements to unlock
2.2 The transgender health care problem
2.3 Data, codebook, and R packages for learning about descriptive statistics
2.4 Achievement 1: Understanding variable types and data types
2.5 Achievement 2: Choosing and conducting descriptive analyses for categorical (factor) variables
2.6 Achievement 3: Choosing and conducting descriptive analyses for continuous (numeric) variables
2.7 Achievement 4: Developing clear tables for reporting descriptive statistics
2.8 Chapter summary
Chapter 3: Data Visualization: The R-Team and the Tricky Trigger Problem
3.1 Achievements to unlock
3.2 The tricky trigger problem
3.3 Data, codebook, and R packages for graphs
3.4 Achievement 1: Choosing and creating graphs for a single categorical variable
3.5 Achievement 2: Choosing and creating graphs for a single continuous variable
3.6 Achievement 3: Choosing and creating graphs for two variables at once
3.7 Achievement 4: Ensuring graphs are well-formatted with appropriate and clear titles, labels, colors, and other features
3.8 Chapter summary
Chapter 4: Probability Distributions and Inference: The R-Team and the Opioid Overdose Problem
4.1 Achievements to unlock
4.2 The awful opioid overdose problem
4.3 Data, codebook, and R packages for learning about distributions
4.4 Achievement 1: Defining and using the probability distributions to infer from a sample
4.5 Achievement 2: Understanding the characteristics and uses of a binomial distribution of a binary variable
4.6 Achievement 3: Understanding the characteristics and uses of the normal distribution of a continuous variable
4.7 Achievement 4: Computing and interpreting z-scores to compare observations to groups
4.8 Achievement 5: Estimating population means from sample means using the normal distribution
4.9 Achievement 6: Computing and interpreting confidence intervals around means and proportions
4.10 Chapter summary
Chapter 5: Computing and Interpreting Chi-Squared: The R-Team and the Vexing Voter Fraud Problem
5.1 Achievements to unlock
5.2 The voter fraud problem
5.3 Data, documentation, and R packages for learning about chi-squared
5.4 Achievement 1: Understanding the relationship between two categorical variables using bar charts, frequencies, and percentages
5.5 Achievement 2: Computing and comparing observed and expected values for the groups
5.6 Achievement 3: Calculating the chisquared statistic for the test of independence
5.7 Achievement 4: Interpreting the chi-squared statistic and making a conclusion about whether or not there is a relationship
5.8 Achievement 5: Using Null Hypothesis Significance Testing to organize statistical testing
5.9 Achievement 6: Using standardized residuals to understand which groups contributed to significant relationships
5.10 Achievement 7: Computing and interpreting effect sizes ...

Biographie:

Jenine K. Harris earned her doctorate in public health studies and biostatistics from Saint Louis University School of Public Health in 2008. Currently, she teaches biostatistics courses as an Associate Professor in the Brown School public health program at Washington University in St. Louis. In 2013, she authored An Introduction to Exponential Random Graph Modeling, which was published in the Sage Quantitative Applications in the Social Sciences series and is accompanied by the ergmharris R package available on the Comprehensive R Archive Network (CRAN). She is an author on more than 80 peer-reviewed publications, and developed and published the odds.n.ends R package available on the CRAN. She is the leader of R-Ladies St. Louis, which she co-founded with Chelsea West in 2017 (@rladiesstl). R-Ladies St. Louis is a local chapter of R-Ladies Global (@rladiesglobal), an organization devoted promoting gender diversity in the R community. Her recent research interests focus on improving the quality of research in public health by using reproducible research practices throughout the research process.?