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# Solution Manual for Statistics With R-Solving Problems Using Real-World Data By Harris

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

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Solution Manual for Statistics With R-Solving Problems Using Real-World Data By Harris

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

Statistics with R is easily the most accessible and almost fun introduction to statistics and R that I have read. Even the most hesitant student is likely to embrace the material with this text.”

—David A.M. Peterson, Department of Political Science, Iowa State University

Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the “R Team” characters. Inspired by the programming group “R Ladies,” the R Team works together to master the skills of statistical analysis and data visualization to untangle real-world, messy data using R. The storylines draw students into investigating contemporary issues such as marijuana legalization, voter registration, and the opioid epidemic, and lead them step-by-step through full-color illustrations of R statistics and interactive exercises.

Table of Content

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