# MATH 373 (Spring 2017)

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)

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
3.5 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
4.5 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!

14.2 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
15.3 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

19.1 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

20.1 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