## Description

**Test Bank for Statistics With R-Solving Problems Using Real-World Data Harris**

**Test Bank for Statistics With R-Solving Problems Using Real-World Data By Jenine K. Harris, ISBN: 9781506388151**

**Table of Contents**

Chapter 1. Preparing data for analysis and visualization in R: The R-team and the pot policy problem

Choosing and Learning R

Learning R with Publicly-Available Data

Achievements to Unlock

The Tricky Weed Problem

Achievement 1: Observations and Variables

Achievement 2: Using Reproducible Research Practices

Achievement 3: Understanding and Changing Data Types

Achievement 4: Entering or Loading Data into R

Achievement 5: Identifying and Treating Missing Values

Achievement 6: Building a Basic Bar Graph

Chapter Summary

Chapter 2: Computing and Reporting Descriptive Statistics: The R-team and the Troubling Transgender Healthcare Problem

Achievements to Unlock

The Transgender Healthcare Problem

Data, Codebook, and R Packages for Learning About Descriptive Statistics

Achievement 1: Understanding Variable Types and Data Types

Achievement 2: Choosing and Conducting Descriptive Analyses for Categorical (Factor) Values

Achievement 3: Choosing and Conducting Descriptive Analyses for Continuous (Numeric) Variables

Achievement 4: Developing Clear Tables for Reporting Descriptive Statistics

Chapter Summary

Chapter 3: Data Visualization: The R-Team and the Tricky Trigger Problem

Achievements to Unlock

The Tricky Trigger Problem

Data, Codebook, and R Packages for Graphs

Achievement 1: Graphs for a Single Categorical Variable

Achievement 2: Graphs for a Single Continuous Variable

Achievement 3: Choosing and Creating Graphs for Two Variables at Once

Achievement 4: Ensuring Graphs are Well-Formatted with Appropriate and Clear Titles, Labels, Colors, and Other Features

Chapter Summary

Chapter 4: Probability Distributions and Inference: The R-Team and the Opioid Overdose Problem

Achievements to Unlock

The Awful Opioid Overdose Problem

Data, Codebook, and R Packages for Learning About Distributions

Achievement 1: Defining and Using the Probability Distributions to Infer From A Sample

Achievement 2: Understanding the Characteristics and Uses of a Binomial Distribution of a Binary Variable

Achievement 3: Understanding the Characteristics and Uses of the Normal Distribution of a Continuous Variable

Achievement 4: Computing and Interpreting z-scores to Compare Observations to Groups

Achievement 5: Estimating Population Means from Sample Means Using the Normal Distribution

Achievement 6: Computing and Interpreting Confidence Intervals around Means and Proportions

Chapter Summary

Chapter 5: Computing and Interpreting Chi-Squared: The R-Team and the Vexing Voter Fraud Problem

Achievements to Unlock

The Voter Fraud Problem

Data, Codebook, and R Packages for Learning About Chi-Squared

Achievement 1: Understanding the Relationship Between Two Categorical Variables using Bar Graphs, Frequencies, and Percentages

Achievement 2: Computing and Comparing Observed and Expected Values for the Groups

Achievement 3: Calculating the Chi-Squared Statistics for the Test of Indepedence

Achievement 4: Intepreting the Chi-Squared Statistics and Making a Conclusion about Whether or Not There is A Relationship

Achievement 5: Using Null Hypothesis Significance Testing to Organize Statistical Testing

Achievement 6: Using Standardized Residuals to Understand Which Groups Contributed to Significant Relationship

Achievement 7: Computing and Interpreting Effect Sizes to Understand the Strength of a Significant Chi-Squared Relationship

Achievement 8: Understanding the Options for Failed Chi-Squared Assumptions

Chapter Summary

Chapter 6: Conducting and Interpreting t-tests: The R-Team and the Blood Pressure Predicament

Achievements to Unlock

The Blood Pressure Predicament

Data, Code book, and R Packages for Learning about t-tests

Achievement 1: Understanding the Relationship between One Categorical Variable and One Continuous Variable Using Graphs, Frequencies, and Percentages

Achievement 2: Comparing a Sample Mean to a Population Mean with One Sample t-test

Achievement 3: Comparing Two Unrelated Sample Means with an Independent Samples t-test

Achievement 4: Comparing Two Related Sample Means with a Dependent Samples Test

Achievement 5: Computing and Interpreting an Effect Size for Significant t-tests

Achievement 6: Examining and Checking the Underlying Assumptions for Using the t-test

Achievement 7: Identifying and Using Alternate tests for when t-test Assumptions are Not Met

Chapter Summary

Chapter 7: Analysis of Variance (ANOVA): The R-Team and the Technical Difficulties Problem

The Technical Difficulties Problem

Data, Codebook, and R Packages for Learning about ANOVA

Achievement 1: Exploring the Data Using Graphics and Descriptive Statistics

Achievement 2: Understanding and Conducting One-Way Analysis of Variance (ANOVA)

Achievement 3: Choosing and Using Post-Hoc Tests and Contrasts

Achievement 4: Computing and Interpreting Effect Sizes for ANOVA

Achievement 5: Testing ANOVA Assumptions

Achievement 6: Choosing and Using Alternative Tests when ANOVA Assumptions are Not Met

Achievement 7: Understanding and Conducting Two-Way ANOVA

Chapter Summary

Chapter 8: Correlation Coefficients: The R-team and the Clean Water Conundrum

Achievements to Unlock

The Clean Water Conundrum

Data and R Packages for Learning about Correlation

Achievement 1: Exploring the Data Using Graphics and Descriptive Statistics

Achievement 2: Computing and Interpreting Pearson’s r Correlation Coefficient

Achievement 3: Conducting an Inferential Statistical Test for Pearson’s r Correlation Coefficient

Achievement 4: Examining Effect Size for Pearson’s r with the Coefficient of Determination

Achievement 5: Checking Assumptions for Pearson’s r Correlation Analyses

Achievement 6: Transforming the Variables as an Alternative as an Alternative when Pearso’s r Correlation Assumptions are Not Met

Achievement 7: Using Spearman’s rho as an Alternative When Pearson’s r Correlation Assumptions are Not Met

Achievment 8: Introducing Partial Correlations

Chapter Summary

Chapter 9: Linear Regression: The R-Team and the Needle Exchange Examination

Achievements to Unlock

The Needle Exchange Examination

Data, Codebook, and R Packages for Linear Regression Practice

Achievement 1: Using Exploratory Data Analysis to Learn about the Data Before Developing a Linear Regression Model

Achievement 2: Exploring the Statistical Model for a Line

Achievement 3: Computing the Slope and Intercept in a Simple Linear Regression

Achievement 4: Slope Interpretation and Significance (b, p-value, CI)

Achievement 5: Model Significance and Model Fit

Achievement 6: Checking Assumptions and Conducting Diagnoses

Achievement 7: Adding Variables to the Model and Using Transformation

Chapter 10: Binary Logistic Regression: The R-Team Examines the Perplexing Libraries Problem

Achievements to Unlock

The Perplexing Libraries Problem

Data, Codebook, and R Packages for Logistics Regression Practice

Achivement 1: Using Exploratory Data Analysis before Developing a Logistic Regression Model

Achievement 2: Understanding the Binary Logistic Regression Statistical Model

Achievement 3: Estimating a Simple Logistic Regression Model and Interpreting Predictor Significance and Interpretation

Achievement 4: Computing and Interpreting Two Measures of Model Fit

Achievement 5: Estimating a Larger Logistic Regression Model with Categorical and Continuous Predictors

Achievement 6: Interpreting the results of a Larger Logistic Regression Model

Achievement 7: Checking Logistic Regression Assumptions and Using Diagnostics to Identify Outliers and Influential Values

Achievement 8: Using the Model to Predict Probabilities for Observations that are Outside the Data Set

Achievement 9: Adding and Interpreting Interaction Terms in Logistic Regression

Achievement 10: Using the Likelihood Ratio (LR) Test to Compare

Chapter Summary

Chapter 11: Multinational and Ordinal Logistic Regression: The R-Team Examines the Diversity Dilemma in STEM

Achievements to Unlock

The Diversity Dilemma in STEM

Data, Codebook, and R Packages for Multinomial and Ordinal Regression Practice

Achievement 1: Exploratory Data Analysis for the Multinomial Model

Achievement 2: Estimating and Interpreting a Multinomial Logistic Regression Model

Achievement 3: Checking Assumptions for Multinomial Logistic Regression

Achievement 4: Exploratory Data Analysis for Ordinal Regression

Achievement 5: Estimate an Ordinal Regression Model

Achievement 6: Check Assumptions for Ordinal Regression

Chapter Summary

References