BIOS 6030 - Principles of Biostatistics, 3 credits
This is a beginning course in applied biostatistics. It covers graphical and numberical methods of describing data sets, an introduction to probability and probability distributions, estimation, hypothesis testing, power and sample size estimation. The objective of this course is to introduce the students to biostatisitcal methods and to understand the underlyging principles as well as practical guidelines of "how to do it" and "how to interpret it" and the role they can play in decision making for public health application. te course will focus on descriptive and inferential statistical techniques with emphasis on selection of appropriate application and interpretation of results.
Course Learning Objectives
The student will be able to:
- Distinguish between categorical variables without order, categorical riables with order and continuous variables.
- Select the appropriate graphic presentation for a set of data and generate the graph.
- Construct frequency distributions.
- Compute measures of central tendency (mean, median, and mode) and variability (variance, standard deviation).
- Select and use the appropriate laws of probability (additive, multiplicative, Bayes' Law).
- Use the binomial and normal distributions to assess the probability and uncertainty of health outcomes.
- Construct and interpret confidence intervals around means.
- Differentiate between the research, null and alternative hypotheses.
- Construct one and two-sided hypotheses.
- Identify type I and type II errors, significance level, p value and power.
- Determine the sample size needed for one- and two-sample tests on means.
- Perform and interpret one-sample, two-sample, and paired t tests on means.
- Perform and interpret the normal theory two-sample test of proportions.
- Perform and interpret the F test to compare two variances.
- Perform and interpret chi square tests of independence.
- Compute the least squares estimate of slopes and intercepts for simple linear regression.
- Test the hypothesis that a simple linear regression is significant.
- Compute and interpret Pearson product moment correlation coefficients.
- Construct graphs, charts and tables to communicate the results of statistical analyses for decision making purpose.