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ISE 130: Engineering Probability and Statistics Spring 2003 Instructor: Dr. Yasser Dessouky 485E Eng Building, 924-4133 Email:[email protected] Office hours: MW 1:30-2:30,4-5:30 Objective: To provide an introduction to the application of statistics in relation to engineering applications. Pre-requisite: Math 32 COURSE CONTENT 1. Discuss, with many examples, the subjects of sample space, event, probability, probability distribution, common discrete and continuous probability distributions, conditional probability distribution, probabilistic modeling, etc.. 2. Demonstrate the meaning and power of the Central Limit Theorem, the Law of Large Numbers, etc.. 3. Introduce the fundamentals of Engineering Statistics, including charting, interpreting, and analyzing the data; statistical hypothesis testing; estimation of the parameters of a probability distribution (point estimation and confidence interval). Textbook: Applied Statistics and Probability for Engineers, Montgomery and Runger, Wiley. Grading: Homework Exams ([email protected]) Final Percent 20 50 30 Course Conduct Homework assignments are to be completed individually and are due at the beginning of class on the date specified. If you plan to miss class on a day an assignment is due, submit it ahead of time. Late assignments will be penalized 20%. You have one week to turn in the assignment if it is late before you receive no credit for the assignment. It is appropriate to ask another student for help in clarifying a point in a problem you attempted alone. However, do not copy another student’s assignment nor allow another student to copy your assignment. This is academic dishonesty. In general, exams will be closed book and closed notes. Students will be allowed to bring in one 8.5” by 11” sheet with their own summarized material (commonly called a “cheat sheet”). NO: Cell phone and pager use during class Chewing gum during class Late arrival to class Week 1 2 2,3 3 4 5 6 7 8 9 Chapter 1 6 2 2 3 3 4 4 7 7 9 7 10 11 8 9 12 9,10 13 10 14 15 16 Final 13 11 These Dates May Flex Topics Introduction to probability and statistics Data Summary and Presentation Probability; sample space; events Conditional Probability; Independence Discrete Random Variables and Distributions Discrete Random Variables and Distributions Continuous Random Variables and Distributions Continuous Random Variables and Distributions; Random Sampling; Sampling Distributions Parameter Estimation; Estimators Sum and Average of Random Variables; Central Limit Theorem; Law of Large Numbers Confidence Interval Application of Parameter Estimation and Hypothesis Testing: Statistical Inference for One Sample Application of Parameter Estimation and Hypothesis Testing: Statistical Inference for One Sample and Two Samples Application of Parameter Estimation and Hypothesis Testing: Statistical Inference for Two Samples Analysis of Variance Linear Regression Review Friday, May 16 12:15-14:30