COURSE SYLLABUS

IE 220 Probability and Statistics (3-0) 3

Catalog Data:

Introduction to probability and counting. Probability laws. Discrete distributions. Continuous distributions. Joint distributions. Descriptive statistics. Estimation. Inferences on the mean and variance of a distribution. Inferences on proportions. Comparing two means and two variances. Simple linear regression and correlation. Multiple linear regression model. Analysis of variance. Factorial experiments. Categorical data. Statistical quality control. (for non-IE students).

Textbook:

· Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc., 2002.

References:

· Rosenkrantz, W.A., Introduction to Probability and Statistics Scientists and Engineers, Mc GrawHill , 1997.

· Barnes, J. Wesley, Statistical Analysis for Engineers and Scientists: A Computer-Based Approach, McGraw Hill, 1994.

· Hayter, A.J., Probability and Statistics for Engineers and Scientists, PWS Publishing Co., 1996.

· Papoulis, A., Probability and Statistics, Prentice-Hall, 1990.

· Vardeman, Stephen B., Statistics for Engineering Problem Solving, PWS Publishing Company, 1994.

· Moore,D.S., The Basic Practice of Statistics, W.H.Freeman and Company, 1999

Prerequisites by Topic:

Differential and integral calculus; multiple integration.

Method for Assessing Student Knowledge of Prerequisite Topics:

Students are given a set of review exercises, which cover prerequisite topics, during the first lecture. The instructor collects the solutions of these exercises and grades them. These grades are not included in the overall course grading. They are used to force those students who are poor in prerequisite topics to improve themselves so that they will follow the course more profitably.

Objective:

· To expose students to the basic concepts of probability and probability laws, various types of distributions, inferences on the Mean and variance of a distribution.

· To teach students the statistical methods which include linear regression and correlation, multiple linear regression modeling, analysis of variance, factorial experimentation, categorical data, statistical quality control.

Goals:

The students are prepared with these concepts so that they can solve practical problems of computer science discipline which requires statistical techniques of data analysis, simple linear and multiple linear regression model development and statistical quality control .

Topics:

1. Introduction to probability and counting (1 week)

2. Some probability laws (1 week)

3. Various types of probability distributions (2 weeks)

4. Descriptive statistics (1 week)

5. Estimation (1 week)

6. Continuous probability distributions (1 week)

7. Inferences on the mean and variance of a distribution. Inferences on proportions (1 week)

8. Comparing two means and two variances (1 week)

9. Simple linear regression and correlation ( 1 week)

10. Multiple linear regression model (1 week)

11. Analysis of variance. Factorial experiments (1 week)

12. Categorical data. Statistical quality control (1 week)

Computer Usage and Laboratory Projects:

The software MINITAB will be utilized to enable students to familiarize different types of probability distributions. At least one computer assignment related to probability distributions. The student will gain expertise in synthesizing the theoretical knowledge and computer software capabilities in solving real-life computer science problems. The software MINITAB will be utilized to achieve the goal. At least one computer assignment related to each course topic will be issued to the students to apply the theoretical knowledge in solving real-life statistical computing science problems.

Contribution to Professional Component

1. Mathematics or Basic Science 1 Credit

2. Engineering Science or Design 1 Credit

3. General Education Requirements 1 Credit