This will be the last MATH 373 ever, as we will transition to MATH 372 instead.

This class will be held in the Sakamaki Innovation Zone D103.

Textbook: *Stats: Data and Models* (4th edition) by De Veaux, Velleman, and Bock.

## Homework

There will be 75 homework exercises, 3 each from Chapters 1–25.

(Lecture problems in parentheses.)

Part I

Chapter 1: 17, 31, 32 out of 34. (19,23,24)

Chapter 2: 14, 21, 34 out of 46. (12,36,41)

Chapter 3: 4, 47, 51 out of 60. (16,20,44)

Chapter 4: 18, 41, 51 out of 52. (35,40,47)

Chapter 5: 6, 12, 29 out of 52. (2,7,48)

Part II

Chapter 6: 4, 8, 13 out of 52. (12,21,33)

Chapter 7: 4, 45, 47 out of 76. (33,38,58)

Chapter 8: 5, 34, 35 out of 48. (16,27,33)

Chapter 9: 1, 4, 33 out of 34. (8,10,20)

Part III

Chapter 10: 17, 32, 33 out of 44. (1,16,36)

Chapter 11: 17, 18, 31 out of 45. (16,25,44)

Chapter 12: 7, 35, 39 out of 61. (4,31,58)

Part IV

Chapter 13: 1, 44 out of 52. (30,31,48)

Chapter 14: 10, 53 out of 60. (9,12,41)

Chapter 15: 15, 41, 47 out of 54. (20,35,39)

Chapter 16: 19, 34, 50 out of 62. (51,54,58)

Part V

Chapter 17: 21, 50, 57 out of 62. (2,39,61)

Chapter 18: 8, 22, 36 out of 44. (7,21,38)

Chapter 19: 2, 38, 42 out of 42. (5,8,26)

Chapter 20: 14, 19, 45 out of 48. (9,30,35)

Chapter 21: 4, 10, 37 out of 50. (24,30,38)

Part VI

Chapter 22: 33, 75, 82 out of 86. (47,50,71)

Chapter 23: 2, 12, 16 out of 40. (32,33,39)

Chapter 24: 27, 30, 43 out of 52. (18,35,48)

Chapter 25: 15, 18, 50 out of 62. (9,25,40)

From the Table of Contents

Part I: Exploring and Understanding Data

## 1. Stats Starts Here

1.1 What Is Statistics?

1.2 Data

1.3 Variables

## 2. Displaying and Describing Categorical Data

2.1 Summarizing and Displaying a Single Categorical variable

2.2 Exploring the Relationship Between Two Categorical variables

## 3. Displaying and Summarizing Quantitative Data

3.1 Displaying quantitative variables

3.2 Shape

3.3 Center

3.4 Spread

3.5 Boxplots and 5-Number Summaries

3.6 The Center of Symmetric Distributions: The Mean

3.7 The Spread of Symmetric Distributions: The Standard Deviation

3.8 Summary—What to Tell About a quantitative variable

## 4. Understanding and Comparing Distributions

4.1 Comparing Groups with Histograms

4.2 Comparing Groups with Boxplots

4.3 Outliers

4.4 Timeplots: Order, Please!

4.5 Re-Expressing Data: A First Look

## 5. The Standard Deviation as a Ruler and the Normal Model

5.1 Standardizing with z-Scores

5.2 Shifting and Scaling

5.3 Normal Models

5.4 Finding Normal Percentiles

5.5 Normal Probability Plots

Part II: Exploring Relationships Between Variables

## 6. Scatterplots, Association, and Correlation

6.1 Scatterplots

6.2 Correlation

6.3 Warning: Correlation ≠ Causation

6.4 Straightening Scatterplots

## 7. Linear Regression

7.1 Least Squares: The Line of “Best Fit”

7.2 The Linear Model

7.3 Finding the Least Squares Line

7.4 Regression to the Mean

7.5 Examining the Residuals

7.6 R2—The variation Accounted For by the Model

7.7 Regression Assumptions and Conditions

## 8. Regression Wisdom

8.1 Examining Residuals

8.2 Extrapolation: Reaching Beyond the Data

8.3 Outliers, Leverage, and Influence

8.4 Lurking variables and Causation

8.5 Working with Summary values

## 9. Re-expressing Data: Get It Straight!

9.1 Straightening Scatterplots – The Four Goals

9.2 Finding a Good Re-Expression

Part III: Gathering Data

## 10. Understanding Randomness

10.1 What Is Randomness?

10.2 Simulating by Hand

## 11. Sample Surveys

11.1 The Three Big Ideas of Sampling

11.2 Populations and Parameters

11.3 Simple Random Samples

11.4 Other Sampling Designs

11.5 From the Population to the Sample: You Can’t Always Get What You Want

11.6 The valid Survey

11.7 Common Sampling Mistakes, or How to Sample Badly

## 12. Experiments and Observational Studies

12.1 Observational Studies

12.2 Randomized, Comparative Experiments

12.3 The Four Principles of Experimental Design

12.4 Control Treatments

12.5 Blocking

12.6 Confounding

Part IV: Randomness and Probability

## 13. From Randomness to Probability

13.1 Random Phenomena

13.2 Modeling Probability

13.3 Formal Probability

## 14. Probability Rules!

14.1 The General Addition Rule

14.2 Conditional Probability and the General Multiplication Rule

14.3 Independence

14.4 Picturing Probability: Tables, Venn Diagrams, and Trees

14.5 Reversing the Conditioning and Bayes’ Rule

## 15. Random Variables

15.1 Center: The Expected value

15.2 Spread: The Standard Deviation

15.3 Shifting and Combining Random variables

15.4 Continuous Random variables

## 16. Probability Models

16.1 Bernoulli Trials

16.2 The Geometric Model

16.3 The Binomial Model

16.4 Approximating the Binomial with a Normal Model

16.5 The Continuity Correction

16.6 The Poisson Model

16.7 Other Continuous Random Variables: The Uniform and the Exponential

Part V: From the Data at Hand to the World at Large

## 17. Sampling Distribution Models

17.1 Sampling Distribution of a Proportion

17.2 When Does the Normal Model Work? Assumptions and Conditions

17.3 The Sampling Distribution of Other Statistics

17.4 The Central Limit Theorem: The Fundamental Theorem of Statistics

17.5 Sampling Distributions: A Summary

## 18. Confidence Intervals for Proportions

18.1 A Confidence Interval

18.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?

18.3 Margin of Error: Certainty vs. Precision

18.4 Assumptions and Conditions

## 19. Testing Hypotheses About Proportions

19.1 Hypotheses

19.2 P-values

19.3 The Reasoning of Hypothesis Testing

19.4 Alternative Alternatives

19.5 P-values and Decisions: What to Tell About a Hypothesis Test

## 20. Inferences About Means

20.1 Getting Started: The Central Limit Theorem (Again)

20.2 Gosset’s t

20.3 Interpreting Confidence Intervals

20.4 A Hypothesis Test for the Mean

20.5 Choosing the Sample Size

## 21. More About Tests and Intervals

21.1 Choosing Hypotheses

21.2 How to Think About P-values

21.3 Alpha Levels

21.4 Critical values for Hypothesis Tests

21.5 Errors

Part VI: Accessing Associations Between Variables

## 22. Comparing Groups

22.1 The Standard Deviation of a Difference

22.2 Assumptions and Conditions for Comparing Proportions

22.3 A Confidence Interval for the Difference Between Two Proportions

22.4 The Two Sample z-Test: Testing for the Difference Between Proportions

22.5 A Confidence Interval for the Difference Between Two Means

22.6 The Two-Sample t-Test: Testing for the Difference Between Two Means

22.7 The Pooled t-Test: Everyone into the Pool?

## 23. Paired Samples and Blocks

23.1 Paired Data

23.2 Assumptions and Conditions

23.3 Confidence Intervals for Matched Pairs

23.4 Blocking

## 24. Comparing Counts

24.1 Goodness-of-Fit Tests

24.2 Chi-Square Test of Homogeneity

24.3 Examining the Residuals

24.4 Chi-Square Test of Independence

## 25. Inferences for Regression

25.1 The Population and the Sample

25.2 Assumptions and Conditions

25.3 Intuition About Regression Inference

25.4 Regression Inference

25.5 Standard Errors for Predicted values

25.6 Confidence Intervals for Predicted values

25.7 Logistic Regression