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### 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 Visualizing Distributions with Histograms
- 3.3 Shape, Center, Spread, and Weird Things
- 3.4 The Data Generating Process
- 3.5 The Back and Forth Between Data and the DGP
- 3.6 The Back and Forth Between Data and the DGP (Continued)
- 3.7 The Five-Number Summary
- 3.8 Boxplots and the Five-Number Summary
- 3.9 Exploring Variation in Categorical Variables
- 3.10 Chapter 3 Review Questions
- 3.11 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 The p-Value
- 10.5 A Mathematical Model of the Sampling Distribution of b1
- 10.6 Things That Affect p-Value
- 10.7 Hypothesis Testing for Regression Models
- 10.8 Chapter 10 Review Questions
- 10.9 Chapter 10 Review Questions 2

#### Chapter 11 - Model Comparison with F

- 11.1 Moving Beyond b1
- 11.2 Sampling Distributions of PRE and F
- 11.3 The Sampling Distribution of F
- 11.4 The F-Distribution: A Mathematical Model of the Sampling Distribution of F
- 11.5 Using F to Test a Regression Model
- 11.6 Type I and Type II Error
- 11.7 Using F to Compare Multiple Groups
- 11.8 Pairwise Comparisons
- 11.9 Chapter 11 Review Questions
- 11.10 Chapter 11 Review Questions 2

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

- 12.1 From Hypothesis Testing to Confidence Intervals
- 12.2 Using Bootstrapping to Calculate the 95% Confidence Interval
- 12.3 Shuffle, Resample, and Standard Error
- 12.4 Interpreting the Confidence Interval
- 12.5 Confidence Intervals for Other Parameters
- 12.6 What Affects the Width of the Confidence Interval
- 12.7 Chapter 12 Review Questions
- 12.8 Chapter 12 Review Questions 2