GHS 207 - Introduction to Biostatistics

Competencies

By the end of the course, the student should be able to:

  • Describe variables, distribution, and measures of central tendency and variance;
  • Understand basic probability concepts, Bayes' theorem, and the concepts of sensitivity and specificity, positive and negative predictive values;
  • Describe the concepts of confidence intervals, hypothesis tests, significance level, statistical error, and power;
  • Describe the difference between parametric and non-parametric data analysis and make data appropriate decisions about which to use;
  • Apply statistical techniques to compare groups of data using both parametric and non-parametric techniques;
  • Conduct and understand correlation analyses;
  • Analyze data using both simple and multiple linear regression;
  • Interpret statistical results; and
  • Communicate statistical results in writing.
  • Successfully complete the above analyses and tests using the R Statistical Software.

Course content

  • Description of Variables - nominal, ordinal, categorical, continuous
  • Descriptive statistics: distributions, means, standard deviation, standard error, and variance; predictors, outcomes, exposure variables and their measurement
  • Probability, Bayes' theorem, and diagnostic tests
  • Probability distributions: binomial, normal, and standard normal
  • Central limit theorem, confidence intervals for means and proportions, Student's distribution
  • Hypothesis testing, statistical error, and power
  • t-test and Analysis of Variance
  • Non-parametric tests
  • Proportions and chi-square test
  • Correlation and simple linear regression
  • Multiple linear regression
  • Practical application of statistics to real world problems
  • Presentation of statistical data via tables and written summaries
  • Analyze data using statistical software

Course Co-directors

Alden Blair, PhD (c), MS
Glenn-Milo Santos, PhD, MPH

Teaching format

Lectures, laboratory, independent study, assigned projects, and practice with statistical software (STATA)

  • 2-3 hours of lecture per week (quiz and lecture)
  • 3 hours of lab to apply statistical concepts

Course credits

3 units