Statistics with R : solving problems using real-world data /: solving problems using real-world data. (2020)

• Record Type: Book
• Further Information: Note: Jenine K. Harris.
• Authors: Harris, Jenine K
• Contents: 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 to understand the strength of a significant chi-squared relationship; 5.11 Achievement 8: Understanding the options for failed chi-squared assumptions; 5.12 Chapter summary; Chapter 6: Conducting and Interpreting t-Tests: The R-Team and the Blood Pressure Predicament; 6.1 Achievements to unlock; 6.2 The blood pressure predicament<br /> 6.3 Data, codebook, and R packages for learning about t-tests; 6.4 Achievement 1: Understanding the relationship between one categorical variable and one continuous variable using histograms, means, and standard deviations; 6.5 Achievement 2: Comparing a sample mean to a population mean with a one-sample t-test; 6.6 Achievement 3: Comparing two unrelated sample means with an independent-samples t-test; 6.7 Achievement 4: Comparing two related sample means with a dependent-samples t-test; 6.8 Achievement 5: Computing and interpreting an effect size for significant t-tests; 6.9 Achievement 6: Examining and checking the underlying assumptions for using the t-test; 6.10 Achievement 7: Identifying and using alternate tests when t-test assumptions are not met; 6.11 Chapter summary; Chapter 7: Analysis of Variance: The R-Team and the Technical Difficulties Problem; 7.1 Achievements to unlock; 7.2 The technical difficulties problem; 7.3 Data, codebook, and R packages for learning about ANOVA; 7.4 Achievement 1: Exploring the data using graphics and descriptive statistics; 7.5 Achievement 2: Understanding and conducting one-way ANOVA; 7.6 Achievement 3: Choosing and using post hoc tests and contrasts; 7.7 Achievement 4: Computing and interpreting effect sizes for ANOVA; 7.8 Achievement 5: Testing ANOVA assumptions; 7.9 Achievement 6: Choosing and using alternative tests when ANOVA assumptions are not met; 7.10 Achievement 7: Understanding and conducting two-way ANOVA; 7.11 Chapter summary; Chapter 8: Correlation Coefficients: The R-Team and the Clean Water Conundrum; 8.1 Achievements to unlock; 8.2 The clean water conundrum; 8.3 Data and R packages for learning about correlation; 8.4 Achievement 1: Exploring the data using graphics and descriptive statistics; 8.5 Achievement 2: Computing and interpreting Pearson’s r correlation coefficient; 8.6 Achievement 3: Conducting an inferential statistical test for Pearson’s r correlation coefficient; 8.7 Achievement 4: Examining effect size for Pearson’s r with the coefficient of determination; 8.8 Achievement 5: Checking assumptions for Pearson’s r correlation analyses; 8.9 Achievement 6: Transforming the variables as an alternative when Pearson’s r correlation assumptions are not met; 8.10 Achievement 7: Using Spearman’s rho as an alternative when Pearson’s r correlation assumptions are not met; 8.11 Achievement 8: Introducing partial correlations; 8.12 Chapter summary; Chapter 9: Linear Regression: The R-Team and the Needle Exchange Examination; 9.1 Achievements to unlock; 9.2 The needle exchange examination; 9.3 Data, codebook, and R packages for linear regression practice; 9.4 Achievement 1: Using exploratory data analysis to learn about the data before developing a linear regression model; 9.5 Achievement 2: Exploring the statistical model for a line; 9.6 Achievement 3: Computing the slope and intercept in a simple linear regression; 9.7 Achievement 4: Slope interpretation and significance (b1, p-value, CI); 9.8 Achievement 5: Model significance and model fit; 9.9 Achievement 6: Checking assumptions and conducting diagnostics; 9.10 Achievement 7: Adding variables to the model and using transformation; 9.11 Chapter summary; Chapter 10: Binary Logistic Regression: The R-Team and the Perplexing Libraries Problem; 10.1 Achievements to unlock; 10.2 The perplexing libraries problem; 10.3 Data, codebook, and R packages for logistic regression practice; 10.4 Achievement 1: Using exploratory data analysis before developing a logistic regression model; 10.5 Achievement 2: Understanding the binary logistic regression statistical model; 10.6 Achievement 3: Estimating a simple logistic regression model and interpreting predictor significance and interpretation; 10.7 Achievement 4: Computing and interpreting two measures of model fit; 10.8 Achievement 5: Estimating a larger logistic regression model with categorical and continuous predictors; 10.9 Achievement 6: Interpreting the results of a larger logistic regression model; 10.10 Achievement 7: Checking logistic regression assumptions and using diagnostics to identify outliers and influential values; 10.11 Achievement 8: Using the model to predict probabilities for observations that are outside the data set; 10.12 Achievement 9: Adding and interpreting interaction terms in logistic regression; 10.13 Achievement 10: Using the likelihood ratio test to compare two nested logistic regression models; 10.14 Chapter summary; Chapter 11: Multinomial and Ordinal Logistic Regression: T
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 519.502855133; Social sciences -- Statistical methods -- Data processing; Social sciences -- Statistical methods; R (Computer program language)
• Languages: English
• ISBNs: 9781506388137
• Related ISBNs: 9781506388151
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.