Course summary: Introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.
First term objectives: Graphical displays of data, basic experimental design, probabilities and distributions, confidence intervals and tests of hypotheses. Second term objectives: Tests for goodness of fit, contingency tables, analysis of variance, multiple comparisons, linear regression, experimental design, special topics.
Text: Sokal and Rohlf Other recommended: [ Verzani | Dalgaard | Gonick ]
Useful links: [ General Course Info | R Resources | Practice Problems ]
N: Notes / Handouts R: Reading C: Code H: Homework L: Lab
| Date | N | R | C | H | L | Topic | |
| January | 23 | |
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What is statistics? | ||
| 25 | |
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What is probability? | |||
| 28 | |
Probability examples | |||||
| 30 | |
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Introduction to R - Getting started | ||
| February | 1 | |
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Random variables and distributions (1) | ||
| 4 | |
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Random variables and distributions (2) | |||
| 6 | |
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Random variables and distributions (3) | ||
| 8 | |
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Introduction to R - Data types and manipulation | ||||
| 11 | |
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Multiple random variables | |||
| 13 | |
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Sampling distributions (1) → Quiz at 2.30pm in W2009 Bring a calculator | |||
| 15 | |
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Sampling distributions (2) | ||
| 18 | |
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Confidence intervals (1) | |||
| 20 | |
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Confidence intervals (2) | ||
| 22 | |
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Confidence intervals (3) | |||
| 25 | |
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Introduction to R - Data import and export | ||||
| 27 | |
Quiz at 10.30am in W2015 Bring a calculator | |||||
| 29 | |
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Testing hypothesis (1) | ||||
| March | 3 | |
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Testing hypothesis (1) | ||
| 5 | |
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Testing hypothesis (2) | ||
| 7 | |
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Sample size and power calculations | |||
| 10 | Final exam at 10.30am in W2015 Bring a calculator | ||||||
| 12 | |
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Sample size and power calculations | |||
| Spring break | |||||||
| 24 | |
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Permutation and non-parametric tests | |||
| 26 | |
Permutation and non-parametric tests | |||||
| 28 | |
|
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Maximum likelihood estimation | |||
| 31 | |
|
Confidence intervals for proportions | ||||
| April | 2 | |
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Goodness of fit (1) | ||
| 4 | |
Goodness of fit (1) | |||||
| 7 | |
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Goodness of fit (2) | ||
| 9 | |
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2 x 2 tables | ||
| 11 | |
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r x k tables | |||
| 14 | |
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r x k tables | |||
| 16 | |
|
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ANOVA - Introduction → Quiz at 2.30pm in W4007 | |||
| 18 | |
|
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ANOVA - Introduction + permutation tests, random effects | |||
| 21 | |
Transformations and outliers | |||||
| 23 | |
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Model assumptions and diagnostics | ||
| 23 | |
Experimental design → Lecture at 2.30pm in W4007 | |||||
| 25 | |
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ANOVA - Non-parametric methods | |||
| 28 | |
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Multiple comparisons | |||
| 30 | |
|
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ANOVA - nested models | |||
| 30 | |
ANOVA - nested models (cont) → Lecture at 2.30pm in W4007 | |||||
| May | 2 | |
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Two-way analysis of variance | ||
| 5 | Two-way analysis of variance (cont.) | ||||||
| 7 | |
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Simple linear regression → Quiz at 2.30pm in W4007. | |||
| 9 | |
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Simple linear regression: tests and confidence intervals | ||
| 12 | |
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Regression and correlation | |||
| 14 | |
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Prediction and calibration | |||
| 14 | |
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Multiple linear regression → Lecture at 2.30pm in W4007 | |||
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Non-linear regression | |||||
| 16 | Final | ||||||