Course Schedule for Section 1
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Assignments | Topic of Class |
| Aug. 26 | Introduction | |
| 28 | How to ask informative questions | |
| Sep. 2 | FPP 19 | Design of surveys |
| 4 | FPP 1 - 2 | Design of experiments and observational studies |
| 9 | FPP 3 - 6 | Exploratory data analysis with one variable |
| 11 | FPP 7 - 8 | Exploratory data analysis with two variables |
| 16 | FPP 9 - 10 | Correlation and regression |
| 18 | FPP 11 - 12 Answers to Frequently Asked Questions | More on regression |
| 23 | Even more on regression, Introduction to multiple regression | |
| 25 | FPP 13 - 14 | Basics of probability |
| 30 | Review FPP 13 - 14, FPP 15. Final project proposal due. Instructions for final project |
Applications of probability |
| Oct. 2 | Contingency tables | |
| 7 | Midterm exam. Instructions for midterm 1 | |
| 9 | FPP 16 - 17 | Expected values, Standard errors, Central Limit Theorem |
| 14 | NO CLASS-- FALL BREAK | |
| 16 | FPP 18, 20 - 21. Skim FPP 19 for review. | Confidence intervals |
| 21 | FPP 22 - 24 | More on confidence intervals. Specifying study size. |
| 23 | FPP 25 - 26 | Significance tests |
| 28 | FPP 27 | More on significance tests |
| 30 | FPP 28 | Chi-squared tests |
| Nov. 4 | FPP 29 | Limitations of significance testing approach |
| 6 | Data analysis in practice
-- Missing data -- Complex study designs -- Communication of statistical results |
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Midterm exam. Instructions for midterm 2 | |
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No reading |
Review exam and discuss projects |
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No reading |
Maximum likelihood estimation |
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Bayesian Supplement, Ch. 1. Final project presentations in labs. | Bayesian paradigm: Prior and posterior distributions |
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Bayesian Supplement Ch 2, 3 and 5. | Bayesian paradigm: Normal and binomial model |
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NO CLASS-- THANKSGIVING | |
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No reading |
Case Studies: -- Milwaukee school choice study -- The 2000 census |
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No reading |
What is statistics research? Wrap-up |
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Final Exam, 9:00 AM - noon. Instructions for Final Exam |