Overview and content
This course will present an introduction to the basic principles andvocabulary of probability and statistics.
The first part of the course is an introduction to probability, including models commonly used in engineering.
The second part of the course covers statistical methods for data analysis and the tools of statistical inference: drawing conclusions about a process or population from a sample.
Lecturer: Dr. Jacqueline Asscher
Email: email@example.com – please include “094480” in subject
Lecture: Tuesdays, 10:30-13:30, Rabin 511
Tutorial: Wednesdays, 15:00-17:00, Rabin 511
Office hours: by arrangement
Tutor: Rony Nir
Email: firstname.lastname@example.org – please include “094480” in subject
Office hours: by arrangement
A homework assignment will be given each week. All homework must be submitted; it can be prepared in pairs. Each week one randomly selected question will begraded. The overall homework grades will be based on the best 11 of 13 weekly home work grades. There is a midterm examination and a final examination. Both will be open-bookand open-note and will require a hand calculator (no laptops allowed). The examination swill cover only the material presented in the lectures and tutorials.
The final grade will be calculated as follows:
15% homework grade
60% final examination grade
25% maximumof midterm and final examinations grades.
It is essential to keep up by doing the homework, as current concepts and methods taught will be based on previously taught material.
- Probability model, conditional probability, Bayes’ Law, independence
- Discrete random variables: probability and distribution functions, mean and variance
- Special discrete random variables: binomial, geometric, Poisson, hypergeometric,uniform
- Joint random variables: bivariate distributions, covariance, independence
- Continuous random variables: density and distribution functions, mean and variance
- Special continuous random variables: exponential, uniform
- Normal distribution and central limit theorem, including normal approximation of binomial distribution
- Statistical inference, sampling distributions, point estimation – methods and properties
- Point estimates and confidence intervals for mean, standard deviation and proportion
- Hypothesis testing: type I and type II errors, significance, pvalue, power, meaning of“reject”/”don’t reject”, sensitivity to sample size
- Comparing means and variances of two independent populations, comparing means for paired data
- Tests for goodness of fit and for association
- Simple linear regression: definition of model, R2, sums of squares, confidence curves
All the relevant material will be provided in the lectures and tutorials. Lecture slides and tutorial material are on the Moodle and should be downloaded by the students.Three supplementary books that include more than required are:
1. Freedman D., Pisani R. and Purves R. Statistics, Norton, New-York, 1998, 3rd edition.
2. Welpole R.E. and Myers R.H. Probability and Statistics for Engineers andScientists, MacMillan, 1998.
3. Devore J.L. Probability and Statistics for Engineering and Sciences, 1991.
Contact Hours per Week:
Lecture: 3 hours
Recitation: 2 hours