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HU Credits:
2
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
School of Pharmacy
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Ein Karem
Course/Module Coordinator:
Wiessam Abu Ahmad
Coordinator Office Hours:
Teaching Staff:
Mr. Wiessam Abuahmad,
Dr. Arieh Moussaieff,
Mr. Nir Treves
Course/Module description:
Learn fundamental concepts in data analysis, data science and statistical inference, the application of procedures for drawing conclusions from data while allowing for random variation. Topics include: Point estimation, hypothesis testing, t-test, chi-square tests of goodness-of-fit and independence, non-parametric tests, machine learning – which includes multivariable models for classification and data reduction
Course/Module aims:
The course aims at providing a broad overview of principles and practices in management, conducting and analyzing medical research. Students will be able to perform statistical analyses in both the univariable and multivariable steps, and will be able to use the skills developed as a roadmap for more complex inferential problems
Learning outcomes - On successful completion of this module, students should be able to:
1. Write the statistical hypotheses for a given research questions as a function of population parameters;
2. Perform inference on parameter estimates, by testing hypotheses;
3. Select the appropriate test statistics and interpret results in relation to the research questions;
4. Calculate and interpret type I and type II error probabilities and statistical power under given scenarios;
5. Understand results of multiple linear and logistic regression models, including goodness of fit measures and total variance explained;
6. Demonstrate theoretical knowledge about basic concepts in machine learning and supervised learning;
7. Demonstrate theoretical knowledge about unsupervised learning: clustering and dimension reduction
Attendance requirements(%):
75%
Teaching arrangement and method of instruction:
Presentations and using statistical programming software
Course/Module Content:
1. Point estimation, sampling distribution, central limit theory, and introduction to testing hypotheses
2. Hypothesis testing about population mean when the variance is known and when the variance is unknown
3. Hypothesis testing about difference between two independent means and between two means paired samples
4. Hypothesis testing about proportion, and measures of association in frequency tables: OR, RR
5. chi-square tests of goodness-of-fit and independence
6. Types of errors in hypothesis testing and statistical power
7. Non-parametric tests for two independent populations: Wilcoxon rank-sum and Fisher
8. Non-parametric tests for paired samples: Wilcoxon signed-rank test, sign test and Mcnemar test
9. Multiple linear regression
10. Multiple logistic regression
11. Introduction to machine learning and factor analysis
Required Reading:
1. Gordis, L. (2008). Epidemiology (4th Ed.) Saunders: Philadelphia.
2. Koh, T., & Owen, W. L. (2000). Introduction to nutrition and health research. Kluwer Academic Publishers.
3. Pyrczak,F. (2010). Making sense of statistics: a conceptual overview. Pyrczak Publishing.
4. Rao, P.V. (1998).Statistical research methods in the life sciences. Duxbury Press.
5. 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.
6. Zar, J. (2007). Biostatisticalanalysis (5th ed.). Prentice-Hall, Inc.
Additional Reading Material:
Course/Module evaluation:
End of year written/oral examination 70 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 30 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
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