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HU Credits:
6
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Dr. Asaf Weinstein
Coordinator Office Hours:
Sundays 15:00-16:00
Teaching Staff:
Dr. Asaf Weinstein,
Ms. Nofar Gabay
Course/Module description:
1. Simple and multiple linear regression
2. Analysis of variance
Course/Module aims:
To build foundations for statistical inference in some basic linear models
Learning outcomes - On successful completion of this module, students should be able to:
1. Understand the theory behind the methods studied
2. Apply the methods studied in the lectures
3. Understand analyses that use the methods studied in the lectures
Attendance requirements(%):
None
Teaching arrangement and method of instruction:
Chalkboard, with occasional demonstration of computer analysis outputs. In addition, instructor course notes will update during the semester and will be available on the course website
Course/Module Content:
1. Preface
2. Chapters in linear algebra
2.1. Determinant of a matrix
2.2. Diagonalization, eigenvalues and eigenvectors
3. Linear regression in a single explanatory variable. The least squares method
4. Multiple linear regression. Properties of the least squares estimator and geometric interpretation. Projections
5. Probability background. Expectation of a random vector and covariance matrix of a random vector. The multivariate normal distribution
6. Statistical inference in multiple linear regression. Expectation and covariance of the least squares estimator, error variance estimation, the Gauss-Markov theorem, hypothesis testing and confidence interval for a single coefficient an for a linear combination
7. Practical aspects and diagnostics. Residual analysis, multicollinearity, influence metrics
8. Constructing a multiple linear regression model. Initial analysis, dummy variables, interactions, transformations, variable selection, goodness-of-fit measures
9. F-test for comparing two nested models
10. One-way and two-way analysis of variance
11. Random effects models
12. Advanced topics (time permitting): multiple hypothesis testing in linear regression models, post-selection (post-hoc) inference
Required Reading:
Coures notes
Additional Reading Material:
1. Weisberg, S. (1980). Applied Linear Regression
2. Freedman, D. A. (2009). Statistical models: theory and practice
3. Faraway, J. J. (2002). Practical regression and ANOVA using R
4. Ravishanker, N. & Dey, D. K. (2020). A first course in linear model theory
5. Searle, S.R., McCulloch, C.E. & Neuhaus, J.M. (2011).
6. Generalized, linear, and mixed models
7. Scheffe, H. (1999). The analysis of variance
Course/Module evaluation:
End of year written/oral examination 70 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 5 %
Assignments 5 %
Reports 0 %
Research project 0 %
Quizzes 20 %
Other 0 %
Additional information:
Assignments: pass/fail grading. To earn full points in this category you will be required to submit of the homework assignments, where &eq;total # of assignments for the semester. Project work: a project will be assigned in the second part of the semester, replacing two homework assignments
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