Chap1_4p - Course Information Instructor Dr Ridha Hamila...

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ENG200 GENG200 Probability and Statistics for Engineers pring 2010 Spring 2010 1 Course Information Instructor: Dr. Ridha Hamila, E-mail: [email protected] Office Hours: Sunday, Tuesday, and Thursday from 11:00 a.m. to 12:00 noon, Or by appointment A: me ini Thomas E ail: [email protected] edu qa phone: 4951096 TA: Mme Mini Thomas, E-mail: [email protected], phone: 4951096 Lectures: Sunday, Tuesday, and Thursday 08:00 a.m. – 08:50 a.m. Course Webpage: 2 Instructor Ridha Hamila M.Sc., Licentiate Tech, and Dr. Tech. epartment f formation echnology, Department of Information Technology, Tampere University of Technology, Finland. Docent (Adjunct Professor) Institute of Communications Engineering, Tampere University of Technology, Finland. Industrial Experience: Nokia Research Center Nokia Networks Etisalat University College, Emirates Telecommunication Corporation, nited rab mirates 3 United Arab Emirates. Textbooks Textbook : Applied Statistics and Probability for Engineers, Douglas Applied Statistics and Probability for Engineers, Douglas C. C. Montgomery, George Montgomery, George C. C. Runger Runger, , 4 th edition , publisher John Wiley & Sons, Wiley & Sons, 2007 2007 BN: 978 BN: 978- - 71 71- 4589 4589- ISBN: 978 ISBN: 978 0 471 471 74589 74589 1 Reference: Probability and Statistics for Engineering and the sciences, Jay L. Devore, 6 th edition, publisher John Wiley & Sons, Inc. 007 2007 4
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Course Objectives 1. Provide students with statistical methods, both descriptive and analytical, for dealing with the variability in observed data. 2. Provide students with fundamental concepts of probability and random variables. 3. Introducing concepts of Statistical Inference and Hypothesis testing and confidence intervals of parameters. mphasize practical engineering ased applications and the use 4. Emphasize practical engineering-based applications and the use of real data examples. 5 Covered Topics Topics Weeks Introduction, Data Summary and Presentation 1 Probability: Addition rule, conditional probability, multiplication rule and Bayes Theorem. 1 i d i bl P b bili f i M d i f di Discrete random variables. Probability mass function. Mean and variance of discrete random variables. 1 Probability Distribution functions: Uniform, Binomial, Geometric and Negative Binomial, Hyper-geometric and Poisson Distribution. 2 Exam 1 Continuous random variables. Probability Density functions. 1 Normal Distribution. Approximation to Binomial and Poisson Distribution. Exponential distribution. Other continuous distributions. 1 Joint probability function. Multiple discrete and continuous random variables. 1 Covariance and correlation. Bivariate Normal Distribution. Linear combination of random variables. Functions of random variables. 2 Parameter estimation. Properties of estimators. Method of Moments. 1 Method of Maximum likelihood. Exam 2 Interval estimation. Inference on the mean of a population: variance known or unknown.
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This note was uploaded on 10/12/2011 for the course STATISTICS 101 taught by Professor Nazim during the Spring '10 term at Qatar University.

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Chap1_4p - Course Information Instructor Dr Ridha Hamila...

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