2DI90 - Probability and Statistics

# Spring 2017

## Course overview

This course provides an introduction to probability, statistics, and its applications. In many situations, particularly when designing hardware and software systems, it is extremely difficult to predict exactly how a system will behave. It is therefore essential to model such uncertainty in a sensible way so that one can properly assess the system's performance. Probability theory, comprising the first part of the course, equips the students with powerful tools to do this in a sound and effective manner. The second part of the course covers both the theory and practice of statistics. Statistics concerns what can be learned from data, and comprises a body of methods for data collection and analysis across the whole range of science, encompassing areas as diverse as engineering, medicine, business, and law. Students will learn how to perform elementary statistical analyses of real data, and the theory behind these methodologies. List of contents of the lectures (with links to slides and other materials) homework assignments for each week Intermezzos, simulation lab, and R tutorial files

## Format

The course is comprised of interactive lectures (on Tuesday and Friday) and "office hours" (on Friday). Lectures will often include clicker quizzes and in class discussion of problems. This course has no instructions, but rather office hours. During the office hours instructors will be available in a small room to answer course related questions. You must come prepared for the office hours, as there is no place for you to work in that room. This is the rational behind the office hours.

### Lectures

Tuesday (8:45 - 12:30) (see your calendar for the exact location)
Friday (13:45 - 15:30) (see your calendar for the exact location)

### Office Hours

Friday (15:45 - 17:30) (see your calendar for the exact location)

## Student Assessment

The final grade in the course will be computed as follows. Let \$F\$, \$H\$, and \$T\$ denote respectively the grade of the final exam, homework assignments, and electronic test. These are on a scale 0-100. The final grade \$G\$ is computed as

G=Round((0.60 x F + 0.30 x H + 0.10 x T)/10) if F>=50
G=Minimum((0.60 x F + 0.30 x H + 0.10 x T)/10,5) if F<50 .

Note that, to pass the course, it is necessary to have a minimum grade of 50/100 in the final exam (before any rounding).

## Study Materials (SC): "Statistical Compendium", Dikt. nr. 2218 (the paper version of this can be purchased at the dictatenverkoop, in MF 1.552) (MR): "Applied Statistics and Probability for Engineers", by Montegomery, D. C. and Runger, G. C., Wiley, 6th Edition, Wiley (ISBN 978111874412-3) (note: this book is only recommended, and any edition from the 4th onward is also suitable) Statistical computing package R A Short Introduction to R R for beginners R short reference card. (Optional, as a convenient electronic alternative to the tables in the Statistical Compendium) Some probability tables in electronic form: http://www.stat.purdue.edu/~mccabe/ips4tab/bmtables.pdf

## Prerequisites

Students are assumed to have successfully completed a basic calculus course or equivalent, and have a functional knowledge of integral calculus. Furthermore, students are assumed to have a basic level of mathematical maturity.

## Aim and Learning Outcomes

The course is divided in two logical (but interconnected) parts: (i) Probability Theory; and (ii) Statistics. After successfully completing the two parts of the course students will be able to:
• Part I
• Perform elementary probability calculations with stochastic models
• Identify situations where probabilistic models are adequate
• Make use of probability distributions for modeling and analysis of situations where randomness occurs (or when it is considered to be a good modeling tool)
• Write simple computer programs to simulate such models.
• Part II
• Perform elementary statistical analyses of data
• Use statistical software in a proper way to obtain both qualitative and quantitative data analysis results
• Use and construct point estimators, confidence/prediction intervals, and hypothesis tests
• Recognize scenarios where linear regression is an adequate tool, and perform simple statistical analysis in such settings
In addition to the above learning outcomes, students are expected to adequately document calculations that form the base of their probabilistic or statistical analysis.

## Lecturers

### Responsible lecturer

Rui Manuel Castro
Web: https://www.win.tue.nl/~rmcastro
Phone: (+31) 40 247 2499
Office: MF 4.075
Email: (please include the code 2DI90 in the subject, when emailing me in relation to this course)

### Instructors

Mayank Mayank (Office: MF 4.073)
Carla Rusch-Groot (Office: MF 4.074)
Lorenzo Federico (Office: MF04.066)
Debankur Mukherjee (Office: MF 4.069)

### Student Assistants

Noud Kroon
Rolf Morel

To protected page

Home | Recent Changes | Edit Page

Last change: Wed May-03-17 20:25:29
Inspired by roWiki