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Abstract/Syllabus:
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Statistics for Laboratory Scientists II
Spring 2006
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.
Schedule
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| 1 |
Goodness of Fit |
Lecture |
| 2 |
Goodness of Fit, Multinomial Distribution |
Lecture |
| 3 |
2x2 tables, Hypergeometric Distribution, Paired Data |
Lecture |
| 4 |
r x k Tables, Sample Size |
Lecture |
| 6 |
Variances; Introduction to ANOVA |
Lecture |
| 7 |
ANOVA: Permutation Tests, Random Effects |
Lecture |
| 8 |
ANOVA: Model Assumptions and Diagnostics |
Lecture |
| 9 |
ANOVA: Multiple Comparisons |
Lecture |
| 10 |
ANOVA: Non-parametric Methods |
Lecture |
| 11 |
ANOVA: Nested Models |
Lecture |
| 12 |
ANOVA: Two-Way Analysis of Variance |
Lecture |
| 13 |
Simple Linear Regression |
Lecture |
| 14 |
Regression and Correlation |
Lecture |
| 15 |
Simple Linear Regression: Tests and Confidence Intervals |
Lecture |
| 16 |
Simple Linear Regression: Prediction and Calibration |
Lecture |
| 17 |
Multiple Linear Regression: Introduction |
Lecture |
| 18 |
Multiple Linear Regression: Diagnostics |
Lecture |
| 20 |
Non-Linear Regression |
Lecture |
| 21 |
Logistic Regression |
Lecture |
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