Nov 23, 2024  
2015-2016 Undergraduate Academic Catalog 
    
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2015-2016 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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MA 3610 - Biostatistics

4 lecture hours 0 lab hours 4 credits
Course Description
This course provides an introduction to biostatistics and design of experiments for biomedical engineering students. As a result of this course, the students are expected to understand and prepare statistical analyses to data from physiological systems in the laboratory and clinical environment. Students learn basic probability theory that includes discrete and continuous probability distributions. They learn how to apply that theory to hypothesis testing and understand the difference between a z-test and t-test, and one- and two-sample inference hypothesis testing. Additional concepts also covered are associated with measurement validity and reliability, hypothesis formulation and testing, and the experimental and statistical control of error. Particular emphasis is given to the appropriate selection and use of parametric statistical tests including t-tests, and simple and multiple regressions. The statistical software package Minitab will be used throughout the course and students will become accustomed to using it. This course is open only to students in the biomedical engineering program. Note: students cannot receive credit for both MA 3610 and MA 262 . (prereq: MA 136 ) (coreq: MA 137 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Recognize and evaluate conditional probability situations, such as those when Bayes’ Rule applies
  • Understand and interpret conditional probability results such as the specificity of the test, the sensitivity of the test and predictive values positive and negative
  • Perform z-tests and t-tests based on single samples and multiple samples, both paired and unpaired, by hand and by using Minitab, and be able to interpret the results
  • Understand the difference between a z-test and a t-test, and one- and two-sample inference hypothesis testing and be able to choose the appropriate test for a given set of data
  • Know when to use the consequences of using directional alternative hypotheses
  • Assess true positive and true negative conclusions, Type I and Type II errors; influence of alpha, sample size, and effect size on statistical power. Understand how these concepts apply to diagnostic screening tests
  • Determine rejection criteria for a given statistical test
  • Understand assumptions and assumptions testing
  • Interpret and use statistical tables and determine degrees of freedom (when appropriate)
  • Determine linear and nonlinear regression lines
  • Determine correlation and the correlation coefficient
  • Know when to use, and how to apply, nonparametric statistics
Prerequisites by Topic
Course Topics
  • Introduction to Probability; Conditional Probability; Bayes’ Rule
  • Bayes’ Rule and Screening Tests; Prevalence and Incidence
  • Binomial Distribution
  • Poisson Distribution
  • One-Sample Hypothesis Testing (Means and Proportions)
  • Two-Sample Hypothesis testing (Means and Proportions)
  • Nonparametric Statistics
  • Linear Regression and Correlation
  • Nonlinear Regression
  • Hypothesis Testing of Categorical Data/Fisher’s Exact Test
Coordinator
Ronald Jorgensen


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