Instructor
Karl Broman
Offered By
Biostatistics
Description
This course 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.
Syllabus
Course Description
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.
Course Objectives
- Graphical displays of data
- Basic experimental design
- Basic probability
- Confidence intervals and tests of hypotheses
Readings
Required:
ML Samuels, JA Witmer (2002) Statistics for the life sciences, 3rd ed, Prentice Hall.
Recommended:
L Gonick, W Smith (1994) Cartoon guide to statistics. HarperCollins.
P Dalgaard (2002) Introductory statistics with R, Springer-Verlag.
Schedule
|
1 |
Overview; What Is Statistics? |
|
2 |
Displaying Data Badly; Data Summaries |
|
3 |
Experimental Design |
|
4 |
Observational Studies |
|
5 |
Probability, Conditional Probability |
|
6 |
Examples, Bayes's Theorem |
|
7 |
More Examples |
|
8 |
Random Variables, Distributions, Binomial, Poisson |
|
9 |
Normal Distribution, Multiple Random Variables |
|
10 |
Sampling Distributions; Central Limit Theorem |
|
11 |
More of the Same |
|
12 |
Maximum Likelihood Estimation |
|
13 |
Confidence Interval (CI) for the Mean |
|
14 |
CIs for Differences Between Means, CI for Population SD
|
|
15 |
Tests of Hypotheses |
|
16 |
Tests for Differences Between Means |
|
17 |
Calculation of Sample Size and Power |
|
18 |
Permutation Tests and Other Non-Parametric Tests |
|
19 |
Confidence Interval for a Proportion |
|
20 |
Uses and Abuses of Tests
|
|
21 |
Transformations and Outliers |
|
22 |
Analysis of Gene Expression Microarrays |
|
23 |
Identifying Essential Genes in M tuberculosis |
|
24 |
Exam |
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