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HU Credits:
2
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Public Health - International Prog.
Semester:
2nd Semester
Teaching Languages:
English
Campus:
Ein Karem
Course/Module Coordinator:
Mr. Wiessam Abu Ahmad
Coordinator Office Hours:
Teaching Staff:
Dr. Wiessam Abuahmad,
Ms. Chaya Mazuz
Course/Module description:
The course will cover hypothesis testing at the Univariable analysis step and at the Multivariable analysis: the functional form of the Linear regression, General linear models (Univariate and Repeated measures) and the logistic regression model, including checking the model’s assumptions and interpreting the model’s coefficients and results. The course is accompanied by a series of examples using the statistical Rstudio and WinPepi statistical packages, where the emphasis is on practical application and interpretation rather than theory.
Course/Module aims:
Learning outcomes - On successful completion of this module, students should be able to:
Criticize and appraise scientific publications and presentations and apply statistical tools correctly and intelligently
Attendance requirements(%):
75%
Teaching arrangement and method of instruction:
Frontal and online lectures.
Course/Module Content:
1. Measures of Association in contingency tables: Pearson’s chi-square test, G-test, Fisher’s Exact test, Cochran’s rule, Odds ratio, Relative risk and Yates', Walds’ and Williams’ corrections.
2. Hypothesis testing for means: Normality assumption, independent samples t-test, paired t-test, non-parametric alternative tests: Wilcoxon Signed- Rank Test and Mann-Whitney U-Test.
3. Correlation and Regression: Scatter Diagram, Pearson’s coefficient of correlation, linear regression models: principal of least square, regression lines, regression coefficient, properties of regression coefficients, F-test for model significance, t-test for coefficients significance, total variance decomposition (amount of variance explained), Multicollinearity, dealing with nominal variables and comparing between models.
4. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA): Theoretical background, testing hypothesis, graphing interactions, multiple comparisons for main effects and for interaction effect, comparison with linear regression models, Repeated Measures ANOVA, non-parametric alternative: Kruskal-Wallis and Friedman tests.
5. Logistic regression: Principal of likelihood ratio, theoretical background, chi-square test for model significance, Z-test for coefficients significance, goodness of fit.
6. Other regression models: Ordered logistic, Multinomial, Poisson and Negative binomial.
Required Reading:
1. Rao, P.V. (1998).Statistical research methods in the life sciences. Duxbury Press.
2. Zar, J. (2007). Biostatisticalanalysis (5th ed.). Prentice-Hall, Inc.
3. Gordis, L. (2008). Epidemiology (4th Ed.) Saunders: Philadelphia.
4. Vogt, W. P., Vogt, E. R., Gardner, D. C., &Haeffele, L. M. (2014). Selecting the right analyses for your data: quantitative, qualitative, and mixed methods. The Guilford Press.
Additional Reading Material:
Woolson Robert F. and Clarke William R.
Statistical Methods for the Analysis of Biomedical Data
2nd Edition, 2002
John Wiley & Sons.
Grading Scheme :
Written / Oral / Practical Exam / Home Exam 60 %
Submission assignments during the semester: Exercises / Essays / Audits / Reports / Forum / Simulation / others 40 %
Additional information:
A copy of the presentations prepared by the teacher will be distributed
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