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MTH 104 PROB. & STATISTICS. FOR ENG . 201 1 -201 2 Spring Term

DEPARTMENT of INFORMATION TECHNOLOGIES. INTERNATIONAL BURCH UNIVERSITY. MTH 104 PROB. & STATISTICS. FOR ENG . 201 1 -201 2 Spring Term. Dr . Huseyin Padem hpadem@ ibu.edu.ba. Class Schedule: Wednesday 14:00-16: 45 Office Hour: Open Door Policy. Course Objectives.

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MTH 104 PROB. & STATISTICS. FOR ENG . 201 1 -201 2 Spring Term

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  1. DEPARTMENT of INFORMATION TECHNOLOGIES INTERNATIONAL BURCH UNIVERSITY MTH 104 PROB. & STATISTICS. FOR ENG. 2011-2012 Spring Term Dr. HuseyinPademhpadem@ibu.edu.ba

  2. Class Schedule: Wednesday 14:00-16:45 • Office Hour: Open Door Policy

  3. Course Objectives • Descriptive statistics. Sets, events, and probability. Random variables, discrete and continuous distributions. Mathematical expectation and correlation analysis. Discrete probability and popular distributions, Poisson process. Continuous probability distributions. Introduction to reliability theory and failure. Functions of random variables. Introduction to estimation theory. Simple and multiple regression and correlation, least squares. Statistics of extreme events. Testing of hypothesis. Engineering application

  4. Textbooks 1. Probability, Statistics and Random Processes Dr.K.Murugesan & P.Gurusamy by Anuradha Agencies, Deepti Publications. 2. Advanced Engineering Mathematics (Eighth edition), Erwin Kreyszig, John Wiley and Sons (ASIA) Pvt. Ltd., 2001. 3. Probability and Statistics for Engineers: G.S.S.Bhishma Rao,sitech., Second edition 2005.

  5. Brief Contents • Sample space and events – Probability • Some elementary theorems • Random variables – Discrete and continuous – Distribution • Distribution - Binomial, Poisson and normal distribution – related properties. • Sampling distribution: Populations and samples • Sampling distributions of mean (known and unknown) proportions, sums and differences • Estimation: Point estimation – interval estimation - Bayesian estimation. • Test of Hypothesis – Means and proportions – Hypothesis concerning one and two means • Type I and Type II errors. One tail, two-tail tests. • Tests of significance – Student's t-test, F-test. • Estimation of proportions • Curve fitting: The method of least squares • Inferences based on the least squares estimations • Regression – multiple regressions – correlation for univariate and bivariate distributions

  6. Grading • Midterm 20% • Project 30% • Final Examination 50%

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