Course summary: Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.
A free text book from Open Intro.
Lectures: 10:30-11:50, Tuesday and Thursday in W2008. Lab: Tuesday 1:30-2:20 (W2009) and Wednesday 3:00-3:50 (W3031). TA Office Hour: Friday 12:15-1:15 (W3031).
Exams 140.652: Tuesday, November 17 (midterm). Tuesday, December 15 (final).
NEW: Please fill out the course evaluation.
Notes / Supplements / Code
| N | S | C | Topic | ||
| Lecture | 28 | |
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Multiple comparisons (optional) |
| Lecture | 27 | |
Poisson and failure rates | ||
| Lecture | 26 | |
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Permutation and non-parametric tests |
| Lecture | 25 | |
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Matched pairs |
| Lecture | 24 | |
Odds ratios and relative risks (cont) | ||
| Lecture | 23 | |
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Simpson's paradox and confounding | |
| Lecture | 22 | |
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Contingency tables and goodness of fit |
| Lecture | 21 | |
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Fisher's exact test |
| Lecture | 20 | |
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Delta method | |
| Lecture | 19 | |
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Odds ratios and relative risks |
| Lecture | 18 | |
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Binomial proportions (cont) |
| Lecture | 17 | |
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Hypothesis testing (cont) |
| Lecture | 16 | |
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Power |
| Lecture | 15 | |
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Hypothesis testing | |
| Lecture | 14 | |
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Data transformations |