PHPC1015 Biostatistics

COURSE DESCRIPTION: This course will provide an overview of the use of basic statistical concepts and methods in the public health field. Emphasis will be placed on conceptual understanding of statistical approaches to medical data rather than on theory and equations. Students will learn the importance of the correct use of statistical techniques in addressing questions of public health importance such as (1) How can we tell if global warming really happening what is causing it? (2) How does diet affect individual risk of chronic diseases? (3) Is gambling in casinos always a losing proposition? (4) Does exposure to other people’s cigarette smoke really cause health problem? The course will enable students to develop the statistical literacy necessary to interpret the vast amounts of information they must process for personal decision making and less vulnerable to manipulation. Students will also be introduced to a user-friendly software for performing basic statistical analyses.

 WK Topic Teaching format Duration References Learning Outcomes 1 Introduction to biostatistics  Tutorial/Computing LT  C 45 m  1h 45m At the end of this lecture students will have an understanding of the importance of biostatistics in public health, of methods for data collection, and the basic steps and potential pitfalls in statistical analysis. 2 Introduction to descriptive statistics: Levels of measurement, measuring central tendency and variability, frequency tables.  Tutorial/Computing LT          C 45 m          1h 45m Harris 7-19 At the end of this lecture students will have an understanding of types of data and the uses and misuses of graphical and summary measures for describing data. 3 Probability;Tutorial/Computing LT    C 45 m    1h 45m At the end of this lecture students will have a knowledge of probability laws and their uses and random variables 4 Probability distributions:Normal, binomial, Poisson Tutorial/Computing LT  C 45 m  1h 45m At the end of this lecture students will have an understanding of the important probability distributions in biostatistics and their uses. 5 Sampling distributionsTutorial/Computing LT    C 45 m    1h 45m At the end of this lecture students will understand the Central Limit Theorem and it’s importance in statistical applications. 6 Estimation and confidence intervals.  Tutorial/Computing LT    C 45 m    1h 45m Harris 20-23 At the end of this lecture students will understand how to estimate means and variances of random variables and how to calculate confidence intervals. 7 Hypothesis testing: t-tests  Tutorial/Computing LT  C 45 m  1h 45m Harris 24-30 At the end of this lecture students will understand the meaning and interpretation of p-values, the concepts of hypothesis testing and type I and type II error and the uses and interpretation of t-tests. 8 Hypothesis testing:Categorical data.   Tutorial/Computing LT    C 45 m    1h 45m Harris 34-36 At the end of this lecture students will understand the uses and interpretation of the chi-square and trend test for hypothesis testing with categorical data. 9 Nonparametric tests.  Tutorial/Computing LT  C 45 m  1h 45m Harris 31-33 At the end of this lecture students will understand the basics of hypothesis testing using nonparametric methods and how to choose between parametric and non-parametric tests for a particular dataset. 10 Correlation and simple linear regression.  Tutorial/Computing LT    C 45 m    1h 45m Harris 48-54 At the end of this lecture students will understand the use of correlation analysis for testing and measuring linear association between continuous variables and simple linear regression for modeling this type of association. 11 Multiple linear regression I.  Tutorial/Computing LT  C 45 m  1h 45m Harris 54-56 At the end of this lecture students will understand the basics of multiple linear regression for modeling the effect of multiple variables on a continuous outcome and confounder control. 12 Multiple linear regression II.  Tutorial/Computing LT    C 45 m    1h 45m Harris 82-85 At the end of this lecture students will understand the evaluation of confounders in multiple regression and the use of indicator variables to model categorical predictors. 13 Practical data analysis/ Interpretation of statistical results.  Tutorial/Computing LT      C 45 m      1h 45m At the end of this lecture students will understand basic issues and common mistakes for data analysis in practice and the basics of correct interpretation of statistical results.