COURSE SYLLABUS

IE 202 ProbabIlIty and StatIstIcs II (3-0) 3

Catalog Data:

One-and two sample estimation problems. One-and two-sample tests of hypothesis. Simple linear regression and correlation. Multiple linear regression. Forecasting. Time series analysis, Seasonality. Smoothing. Moving Averages. Trend Projection. Applications in industrial engineering. (Prerequisite: IE 201).

Textbook:

Walpole, R.E., Myers, R.H., and Myers, S.L., Probability and Statistics for Engineers and Scientists, 6th edition, Prentice-Hall, 1998.

References:

· 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.

· Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc., 1999.

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

· Sipper,D., and Bulfin,R.L.,Jr., Production: Planning,Control, and Integration, The McGraw-Hill Companies, 1998.

· Anderson, Sweeney and Williams, Introduction to Management Science, 2000.

Prerequisites by Topic:

Probability and distributions.

Method for Assessing Student Knowledge of Prerequisite Topics:

A prerequisite exam will be given on the prerequisite topics in the first two weeks.

Goals:

The course aims to expose students to basic concepts of statistical inference, linear regression and correlation, and forecasting.

Objectives:

· To expose students to several types of decision making problems of industry that can be solved by statistical inference, hypothesis testing.

· To train students to develop simple and multiple-parameter linear models that can be utilized for prediction and forecasting problems of industries in planning and management.

Topics:

1. One-and two-sample estimation problems (2 weeks)

2. One-and two-sample tests of hypotheses (2 weeks)

3. Simple linear regression and correlation (3 weeks)

4. Multiple linear regression (2 weeks)

5. Forecasting-need for forecasting ( 1 week)

6. Causal forecasting (1 week)

7. Time series analysis-smoothing, moving averages-trend projection (1 week)

8. Application examples for industrial engineering (2 weeks)

Computer Usage and Laboratory Projects:

The student will gain expertise in synthesizing the theoretical knowledge and computer software capabilities in solving real-life industrial problems. The software MINITAB will be utilized to achieve the goal. At least one computer assignment on each course topic will be given.

Contribution to Professional Component:

1.Mathematics and Basic Science 2 Credits

2.Engineering Science or Design 1 Credit

3.General Education 0 Credits