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
