HU Credits: 5

Degree/Cycle: 1st degree (Bachelor)

Responsible Department: Statistics

Semester: 1st Semester

Teaching Languages: Hebrew

Campus: Mt. Scopus

Course/Module Coordinator: Micha Mandel

Coordinator Email: micha.mandel@mail.huji.ac.il

Coordinator Office Hours: Sunday 15:00-16:00

Teaching Staff:
Mr. ,
Prof Micha Mandel

Course/Module description:
The course defines the foundation principle for data analysis and in particular deals with point and interval estimation, testing statistical hypotheses, descriptive statistics and simple regression.

Course/Module aims:
Learning the foundation of Frequentist statistical inference, in particular, point and interval estimation, and hypothesis testing.

Learning outcomes - On successful completion of this module, students should be able to:
1. To calculate point estimators based on the method of moments and on maximum likelihood.
2. To deal with some properties of the estimators (biasedness, variance, mean square error), to compare between estimators, to select optimal estimators and to (weight) average between estimators.
3. To construct confidence intervals for the mean, variance, difference (or sum) of means and some other parameters. To compute the confidence level.
4. To conduct tests for simple and composite hypotheses. To compute and interpret the p-value.
5. To compute the required sample size for point and interval estimators and for hypothesis testing.
6. To understand and to apply graphical methods of descriptive statisticsץ
7. To calculate and to interpret simple linear regression.

Attendance requirements(%):
0

Teaching arrangement and method of instruction: Frontal lecturers will deal with the theory (live and video lectures + reading material). The tutorials will look again on theory with an emphasis on examples. The weekly assignments are designed in order to apply the studied material and to make sure of its understanding.

Course/Module Content:
Descriptive statistics, quantiles, histogram, scatter plot. Linear regression.
Population and samples, estimation, unbiased estimators, mean square error.
Estimation methods: the method of moments, maximum likelihood estimators.
Confidence intervals: ideas and principles. Examples: mean, difference in means, proportion, difference in proportions, variance.
Testing hypothesis: ideas and principles. Examples: as above for confidence intervals. Goodness of fit. A-parametric inference.

Required Reading:
None

Additional Reading Material:
4. Freund, Mathematical Statistics, Prentice Hall
5. Bertsekas and Tsitsiklis, Introduction to Probability, Athena Scientific.
6. Ross, A first Course in Probability, Prentice Hall.
7. Meyer, Introductory Probability and Statistical Applications, Addison Wesley.
8. Mood, Graybill and Boes, Introduction to the Theory of Statistics, McGraw Hill.
9. Hogg and Graig, Introduction to Mathematical Statistics, Macmillan.

Course/Module evaluation:
End of year written/oral examination 80 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 0 %
Reports 0 %
Research project 0 %
Quizzes 20 %
Other 0 %

Additional information:
Weekly assignments with solutions will be posted in Moodle.
In addition, weekly online assignments will be given and will comprise 20% of the final grade (based on the average of the best nine).

The final exam will be in the campus, if possible, otherwise an online exam will be given.