Syllabus

ST 101-001 Statistics by Example

Text: Stats: Modeling the World, 3rd ed., 2010, David Bock, Paul Velleman, and Richard De Veaux

Readings (Course Text)
I. STATS STARTS HERE
1. STATISTICS, DATA, AND STATISTICAL THINKING
1.1 So, What is (Are) Statistics Chapter 1
1.2 Data Chapter 2
II. EXPLORING AND UNDERSTANDING DATA
2. SUMMARIZING DATA
2.1 Displaying Qualitative DataChapter 3
2.2 Displaying Quantitative DataChapter 4
Numerical Summaries
2.3 Describing Distributions Numerically: Chapters 4, 5
Measures of Center and Spread, Measures
of Relative Standing, Boxplots
2.4 The Standard Deviation as a Ruler and the Normal ModelChapter 6
3 EXPLORING RELATIONSHIPS IN DATA
3.1 Scatterplots: Looking at RelationshipsChapter 7
3.2 CorrelationChapter 7
3.3 Method of Least SquaresChapters 8, 9
III PRODUCING DATA
4. SAMPLE SURVEYS
4.1 Three Key Ideas in SamplingChapter 12
4.2 Simple Random Samples
4.3 Stratified, Cluster, Systematic, and Multistage Samples
IV PROBABILITY: TOWARDS STATISTICAL INFERENCE
4. PROBABILITY AND SAMPLING DISTRIBUTIONS
4.1 Probability BasicsChapter 15, 16
4.2 Probability Models: Geometric, Binomial,Chapter 17
Poisson, and Normal Distributions
4.3 Sampling Distribution ModelsChapter 18/TD>
V. STATISTICAL INFERENCE: CONCLUSIONS FROM DATA
5 INFERENCE FOR PROPORTIONS AND MEANS
5.1-5.2 Confidence Intervals for ProportionsChapter 19
5.3 Determining Sample Size (Proportions)Chapter 19
5.4 Confidence Intervals for Two ProportionsChapter 22
5.5 Confidence Intervals for MeansChapter 23
5.6 Determining Sample Size (Means)12.6
5.7 Confidence Intervals for Two Means: Independent SamplesChapter 24
5.8 Comparing Means: Paired SamplesChapter 25
6. INFERENCE WHEN VARIABLES ARE RELATED: COMPARING COUNTS
6.1 Goodness of FitChapter 26
6.2 Tests of Homogeniety
6.3 Tests of Independence
7. INFERENCE WHEN VARIABLES ARE RELATED: REGRESSION ANALYSIS
7.1 Inference for Simple Linear RegressionChapter 27
7.2 Multiple RegressionChapter 29