Prof. dr. Siswanto Agus Wilopo, S.U., M.Sc., Sc.D

Classification: compulsory

This course provides understanding and basic skills of biostatistics which is one of core competencies of public health. Basic concepts of biostatistics will be introduced using examples of everyday problems in public health settings. Therefore problem based teaching is required to full-fill the competencies requested. Students will be involved in individual and group learning to apply those basic concepts. The problems will be taken from actual problem in the field, or problems presented in scientific journals in public health. For somebody who is willing to work as researcher or lecturer in public health field, he/she will be given sets of problem different from them who may work in health care management. Computer skills are prerequisite to this course, including word processing skills, so the students will be able to go through the course smoothly. Prerequisite course in mathematics and algebra are recommended but not mandatory to those who take this course. Upon completion of the course, students should be able to: (1) achieve competence in biostatistics in order to be able to solve public health problems quantitatively; (2) achieve competence in applying biostatistics for managerial purposes and public health services; (3) Achieve competence in employing quantitative evidences (statistics) to make scientific inferences, make decision, and to make health care policies (evidence based health care).

Session Topic
1 Introduction on teaching methods and basic concepts of biostatistics in public health
2 Population, parameter, statistics and the probability application on statistics distribution
3 Estimating the  values of single and two population parameters and the ratios of two population parameters
4 Testing hypotheses about the difference and ratio between two population parameters
5 Testing hypotheses about the equality of two or more population proportions
6 Association and correlation
7 Simple and multiple regressions
8 Multi – sample inference: ANOVA and ANCOVA
9 Logistic regression analysis
10 Survival analysis
11 Sampling and sample size estimation