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HU Credits:
6
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Medicine
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Ein Karem
Course/Module Coordinator:
Prof. Shai Carmi
Coordinator Office Hours:
by appointment
Teaching Staff:
Prof Shai Carmi,
Dr. Michael Zuckerman,
Mr. Ron Faibis,
Mr. Elnatan Bitensky,
Ms. Noa Adan,
Ms. Talia Baer,
Ms. Kian Maroun
Course/Module description:
The class will cover basic concepts and problems in statistics and data analysis relevant to medicine and life sciences.
The class is divided into three sections:
1. Descriptive statistics
2. Probability
3. Statistical inference
Course/Module aims:
To teach basic concepts of statistical methods and their application in the biomedical sciences.
Learning outcomes - On successful completion of this module, students should be able to:
* Describe and summarize data using graphs, tables and summary measures. Identify outliers and their impact.
* Describe the relationship between two quantitative variables, including calculation of the correlation coefficient and the regression line, and the odds ratio for categorical variables.
* Solve simple problems in probability, including using conditional probability and Bayes's theorem. Become familiar with basic concepts in medical tests and economic evaluations.
* Model probabilistic problems using random variables, and perform calculations using binomial and normal variables.
* Understand basic concepts of estimation and hypothesis testing.
* Compute and interpret confidence intervals for the population mean or proportion.
* Test hypotheses on the mean or proportion of one population.
* Test hypotheses on the mean or proportion of two populations.
Attendance requirements(%):
Teaching arrangement and method of instruction:
Lecture and tutorial
Course/Module Content:
* Introduction: what is statistics, motivation
* Descriptive statistics: types of variables, the distribution of the data, visualizations of the distribution, measures of location and spread, percentiles, outliers, survival analysis.
* Relationship between two quantitative variables: linear relation, covariance, the correlation coefficient, correlation vs causation, linear regression, regression goodness of fit.
* Relationship between two qualitative variables: contingency tables, visualizations, the odds ratio.
* Probability: basic concepts, calculation of probabilities in simple problems, basic combinatorics, relationships between events, the inclusion-exclusion principle, conditional probability, the multiplication rule, independence between events, the law of total probability, Bayes’ theorem, diagnostic tests.
* Random variables: the distribution of discrete variables, expectation and variance (including of a sum of variables), an introduction to economic evaluations in medicine, the binomial variable, the density of continuous variables, the normal distribution, the normal approximation to the binomial distribution.
* An introduction to statistical inference and estimation: properties of samples, the mean and variance of the sample mean, the central limit theorem and the sampling distribution of the sample mean, confidence intervals and their properties, estimating the proportion, estimating the mean with unknown variance, the t distribution.
* Hypothesis testing: basic concepts, testing hypotheses on the mean in one population with known and unknown variance, testing hypotheses on the proportion in one or two populations (the chi-squared test), testing hypotheses on the difference of the means of two populations (paired or independent),non-parametric tests (sign test and Wilcoxon test), inference errors and simple power calculations, the intuition behind hypothesis testing and common pitfalls.
Some subjects may not be covered in any given year.
Required Reading:
None
Additional Reading Material:
Grading Scheme :
Written / Oral / Practical Exam / Home Exam 100 %
Additional information:
Homework: problem sets ("exercises") will be posted in the class website (Moodle) approximately every week. The grades on the problem sets will not influence the final grade of the course.
Final exam: The final exam will take place online in the Moodle and will be taken from the computer farm on campus. The final exam will account for 100% of the final grade.
Prerequisites: high school-level mathematics and probability. Please thoroughly review the material before the semester or during the first few weeks.
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