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### Course Preview

#### 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

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

#### Chapter 3 - Examining Distributions

- 3.0 The Concept of Distribution
- 3.1 Visualizing Distributions with Histograms
- 3.2 Shape, Center, Spread, and Weird Things
- 3.3 The Data Generating Process
- 3.4 The Back and Forth Between Data and the DGP
- 3.5 The Back and Forth Between Data and the DGP (Continued)
- 3.6 The Five-Number Summary
- 3.7 Boxplots and the Five-Number Summary
- 3.8 Exploring Variation in Categorical Variables
- 3.9 Chapter 3 Review Questions
- 3.10 Chapter 3 Review Questions 2

#### Chapter 4 - Explaining Variation

- 4.0 Welcome to Explaining Variation
- 4.1 Explaining One Variable with Another
- 4.2 Outcome and Explanatory Variables
- 4.3 More Ways to Visualize Relationships: Point and Jitter Plots
- 4.4 Even More Ways: Putting these Plots Together
- 4.5 Representing Relationships Among Variables
- 4.6 Sources of Variation
- 4.7 Randomness
- 4.8 From Categorical to Quantitative Explanatory Variables
- 4.9 Quantitative Explanatory Variables
- 4.10 Research Design
- 4.11 Fooled by Chance: the Problem of Type I Error
- 4.12 Quantifying the Data Generating Process
- 4.13 Chapter 4 Review Questions
- 4.14 Chapter 4 Review Questions 2

#### PART II: MODELING VARIATION

#### Chapter 5 - A Simple Model

- 5.0 What is a Model, and Why Would We Want One?
- 5.1 Modeling a Distribution as a Single Number
- 5.2 The Mean as a Model
- 5.3 Fitting the Empty Model
- 5.4 Generating Predictions from the Empty Model
- 5.5 Venturing into the World of Mathematical Notation
- 5.6 DATA = MODEL + ERROR: Notation
- 5.7 Statistics and Parameters
- 5.8 The Power of Aggregation
- 5.9 Summarizing Where We Are
- 5.10 Chapter 5 Review Questions
- 5.11 Chapter 5 Review Questions 2

#### Chapter 6 - Quantifying Error

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

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

- 7.0 Explaining Variation
- 7.1 Specifying the Model
- 7.2 Fitting a Model with an Explanatory Variable
- 7.3 Generating Predictions from the Model
- 7.4 Examining Residuals from the Model
- 7.5 Quantifying Model Fit with Sums of Squares
- 7.6 Comparing Two Models: Proportional Reduction in Error
- 7.7 Measures of Effect Size
- 7.8 Modeling the DGP
- 7.9 Extending to a Three-Group Model
- 7.10 Improving Models by Adding Parameters
- 7.11 The F Ratio
- 7.12 Chapter 7 Review Questions
- 7.13 Chapter 7 Review Questions 2

#### Chapter 8 - Models with a Quantitative Explanatory Variable

- 8.0 Groups versus Quantitative Explanatory Variables
- 8.1 The Regression Line as a Model
- 8.2 Fitting a Regression Model
- 8.3 Using the Regression Model to Make Predictions
- 8.4 Examining Residuals from the Model
- 8.5 Assessing Model Fit with Sum of Squares
- 8.6 Assessing Model Fit with PRE and F
- 8.7 Correlation
- 8.8 The Correlation Coefficient: Pearson's R
- 8.9 Limitations to Keep in Mind
- 8.10 Chapter 8 Review Questions
- 8.11 Chapter 8 Review Questions 2

#### PART III: EVALUATING MODELS

#### Chapter 9 - The Logic of Inference

#### Chapter 10 - Model Comparison with F

- 10.1 Moving Beyond b1
- 10.2 Sampling Distributions of PRE and F
- 10.3 The Sampling Distribution of F
- 10.4 The F-Distribution: A Mathematical Model of the Sampling Distribution of F
- 10.5 Using F to Test a Regression Model
- 10.6 Type I and Type II Error
- 10.7 Using F to Compare Multiple Groups
- 10.8 Pairwise Comparisons

#### Chapter 11 - Parameter Estimation and Confidence Intervals

- 11.1 From Hypothesis Testing to Confidence Intervals
- 11.2 Using Bootstrapping to Calculate the 95% Confidence Interval
- 11.3 Shuffle, Resample, and Standard Error
- 11.4 Interpreting the Confidence Interval
- 11.5 Confidence Intervals for Other Parameters
- 11.6 What Affects the Width of the Confidence Interval