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 twoway 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 freelyavailable 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 twoway 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 freelyavailable 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, SpringerVerlag.
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 NonParametric 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 
