This is only a preview of the course. Although it includes all course content, it will not grade responses or provide feedback to students. To use the course with your students, click here to request an instructor account.

### Course Preview

Book

#### Getting Started (Don't Skip This Part)

#### Statistics and Data Science: A Modeling Approach

#### PART I: EXPLORING VARIATION

#### Chapter 1 - Welcome to Statistics: A Modeling Approach

#### Chapter 2 - Understanding Data

- 2.1 Starting with a Bunch of Numbers
- 2.2 From Numbers to Data
- 2.3 A Data Frame Example: MindsetMatters
- 2.4 Measurement
- 2.5 Measurement (Continued)
- 2.6 Sampling from a Population
- 2.7 The Structure of Data
- 2.8 Missing Data
- 2.9 Creating and Recoding Variables
- 2.10 Chapter 2 Review Questions
- 2.11 Chapter 2 Review Questions 2

#### Chapter 3 - Examining Distributions

- 3.1 The Concept of Distribution
- 3.2 Histograms
- 3.3 Visualizing Data With Histograms
- 3.4 Shape, Center, Spread, and Weird Things
- 3.5 The Five-Number Summary
- 3.6 Quartiles and the Five-Number Summary
- 3.7 Boxplots and the Five-Number Summary
- 3.8 Outliers on a Boxplot
- 3.9 Exploring Variation in Categorical Variables
- 3.10 The Data Generating Process
- 3.11 The Back and Forth Between Data and the DGP
- 3.12 From DGP to Population to Samples
- 3.13 Weird DGPs and Their Samples
- 3.14 Chapter 3 Review Questions
- 3.15 Chapter 3 Review Questions 2

#### Chapter 4 - Explaining Variation

- 4.1 Welcome to Explaining Variation
- 4.2 Explaining One Variable with Another
- 4.3 Outcome and Explanatory Variables
- 4.4 More Ways to Visualize Relationships: Point and Jitter Plots
- 4.5 Even More Ways: Putting these Plots Together
- 4.6 Representing Relationships Among Variables
- 4.7 Sources of Variation
- 4.8 Randomness
- 4.9 From Categorical to Quantitative Explanatory Variables
- 4.10 Quantitative Explanatory Variables
- 4.11 Research Design
- 4.12 Considering Randomness as a Possible DGP
- 4.13 Shuffling Can Help Us Understand Real Data Better
- 4.14 Quantifying the Data Generating Process
- 4.15 Chapter 4 Review Questions
- 4.16 Chapter 4 Review Questions 2

#### PART II: MODELING VARIATION

#### Chapter 5 - A Simple Model

- 5.1 What is a Model, and Why Would We Want One?
- 5.2 Modeling a Distribution as a Single Number
- 5.3 Median vs. Mean as a Model
- 5.4 Exploring the Mean
- 5.5 Fitting the Empty Model
- 5.6 Generating Predictions from the Empty Model
- 5.7 Thinking About Error
- 5.8 The World of Mathematical Notation
- 5.9 DATA = MODEL + ERROR: Notation
- 5.10 Summarizing Where We Are
- 5.11 Chapter 5 Review Questions
- 5.12 Chapter 5 Review Questions 2

#### Chapter 6 - Quantifying Error

- 6.1 Quantifying Total Error Around a Model
- 6.2 The Beauty of Sum of Squares
- 6.3 Variance
- 6.4 Standard Deviation
- 6.5 Z-Scores
- 6.6 Interpreting and Using Z-Scores
- 6.7 Modeling the Shape of the Error Distribution
- 6.8 Modeling Error with the Normal Distribution
- 6.9 Using the Normal Model to Make Predictions
- 6.10 Getting Familiar with the Normal Distribution
- 6.11 The Empirical Rule
- 6.12 Next Up: Explaining Error
- 6.13 Chapter 6 Review Questions
- 6.14 Chapter 6 Review Questions 2

#### Chapter 7 - Adding an Explanatory Variable to the Model

- 7.1 Explaining Variation
- 7.2 Using R to Fit the Group Model
- 7.3 GLM Notation for the Group Model
- 7.4 How the Model Makes Predictions
- 7.5 Error Leftover From the Group Model
- 7.6 Graphing Residuals From the Model
- 7.7 Error Reduced by the Group Model
- 7.8 Using SS Error to Compare Group to Empty Model
- 7.9 Partitioning Sums of Squares into Model and Error
- 7.10 Using Proportional Reduction in Error (PRE) to Compare Two Models
- 7.11 Chapter 7 Review Questions

#### Chapter 8 - Digging Deeper into Group Models

#### Chapter 9 - Models with a Quantitative Explanatory Variable

- 9.1 Using a Quantitative Explanatory Variable in a Model
- 9.2 Specifying the Height Model with GLM Notation
- 9.3 Interpreting the Parameter Estimates for a Regression Model
- 9.4 Comparing Regression Models to Group Models
- 9.5 Error from the Height Model
- 9.6 Sums of Squares in the ANOVA Table
- 9.7 Assessing Model Fit with PRE and F
- 9.8 Correlation
- 9.9 More on Pearson's R
- 9.10 Using Shuffle to Interpret the Slope of a Regression Line
- 9.11 Limitations to Keep in Mind
- 9.12 Chapter 9 Review Questions
- 9.13 Chapter 9 Review Questions 2

#### PART III: EVALUATING MODELS

#### Chapter 10 - The Logic of Inference

- 10.1 The Problem of Inference
- 10.2 Constructing a Sampling Distribution
- 10.3 Exploring the Sampling Distribution of b1
- 10.4 What Counts as Unlikely
- 10.5 The p-Value
- 10.6 Calculating the p-Value for a Sample
- 10.7 A Mathematical Model of the Sampling Distribution of b1
- 10.8 Things That Affect p-Value
- 10.9 Hypothesis Testing for Regression Models
- 10.10 Chapter 10 Review Questions
- 10.11 Chapter 10 Review Questions 2

#### Chapter 11 - Model Comparison with F

- 11.1 Moving Beyond b1
- 11.2 Sampling Distribution of PRE
- 11.3 Sampling Distribution of F
- 11.4 Using the Sampling Distribution of F
- 11.5 Calculating P-Value from the Sampling Distribution of F
- 11.6 The F-Distribution: A Mathematical Model of the Sampling Distribution of F
- 11.7 F-Distribution and t-Distribution
- 11.8 Using F to Test a Regression Model
- 11.9 Type I and Type II Error
- 11.10 Using F to Compare Multiple Groups
- 11.11 Pairwise Comparisons
- 11.12 The Problem of Simultaneous Comparisons
- 11.13 The Chi-Square Test of Independence
- 11.14 Chapter 11 Review Questions
- 11.15 Chapter 11 Review Questions 2

#### Chapter 12 - Parameter Estimation and Confidence Intervals

- 12.1 From Hypothesis Testing to Confidence Intervals
- 12.2 Thinking With Sampling Distributions
- 12.3 The Basic Idea Behind Confidence Intervals
- 12.4 Using Bootstrapping to Calculate the 95% Confidence Interval
- 12.5 Using the Bootstrapped Sampling Distribution to Find the Confidence Interval
- 12.6 Shuffle, Resample, and Standard Error
- 12.7 Using the t-Distribution to Construct a Confidence Interval
- 12.8 Interpreting the Confidence Interval
- 12.9 Confidence Intervals and Model Comparison
- 12.10 Confidence Interval for Beta0
- 12.11 Confidence Interval for the Slope of a Regression Line
- 12.12 Confidence Intervals for Pairwise Comparisons
- 12.13 What Affects the Width of the Confidence Interval
- 12.14 Chapter 12 Review Questions
- 12.15 Chapter 12 Review Questions 2