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Description of MATH 281 PROBABILITY & STATISTICS | Course Name: | PROBABILITY & STATISTICS | | Course Code: | MATH 281 | | Type of Course: | Undefined | | Level of Course: | Undergraduate (First Cycle) | | Year of Study: | 2 | | Semester/Trimester: | Fall | | ECTS Credits: | 5 | | FU Credits: | 3 | | Name(s) of Lecturer(s): | Cevdet CERİT A-355 ( cerit@itu.edu.tr )
İbrahim EMİROĞLU ( emir@yildiz.edu.tr )
Mustafa BAYRAM A327 ( mbayram@fatih.edu.tr ) | | Course Coordinator: | Mustafa BAYRAM | | Objectives of the Course: | The aim of this course is to teach: Basic concepts and rules of probability; Random variables, expectation and variance, covariance, bivariate marginal and conditional distributions. The popular distributions. The law of large numbers, and central limit theorems. Sampling and descriptive statistics, introduction to estimation theory, methods of maximum likelihood and moments, interval estimation, test of hypotheses, two population problems, simple linear regression and correlation, topics from analysis of variance and design of experiments | | Course Description: | Basic concepts and rules of probability; Random variables, expectation and variance, covariance, bivariate marginal and conditional distributions. The popular distributions. The law of large numbers, and central limit theorems. Sampling and descriptive statistics, introduction to estimation theory, methods of maximum likelihood and moments, interval estimation, test of hypotheses, two population problems, simple linear regression and correlation, topics from analysis of variance and design of experiments | | Learning Outcomes: | 1. Understand Central Limit Theorem and its application to confidence intervals of mean and proportion
2. conduct hypothesis testing for mean, deviation, and proportion.
3. Understand correlation and regression; know how to perform linear regression analysis
4. Test hypotheses involving one or two variances by using Chi-square and F distributions
5. perform one-way and two-way analysis of variance. | | Mode of Delivery: | Face-to-Face | | Prerequisites: | None | | Co-requisites: | None | Course Contents:
( Weekly Lecture Plan ) | | Week | Topics | | 1 | Basic concepts and rules of probability | | 2 | Random variables, expectation and variance | | 3 | Random variables, expectation and variance | | 4 | covariance, bivariate marginal and conditional distributions | | 5 | covariance, bivariate marginal and conditional distributions | | 6 | The popular distributions. The law of large numbers, and central limit theorems | | 7 | The popular distributions | | 8 | The law of large numbers, and central limit theorems | | 9 | Sampling and descriptive statistics, introduction to estimation theory | | 10 | methods of maximum likelihood and moments, interval estimation | | 11 | test of hypotheses, two population problems | | 12 | simple linear regression and correlation | | 13 | topics from analysis of variance and design of experiments | | 14 | topics from analysis of variance and design of experiments |
| | Recommended Reading: | Introduction to Probability and Statistics for Engineers and Scientists, Fourth Edition by Sheldon M. Ross | | Planned Learning Activities and Teaching Methods: | Lectures, Exercises, Assignments, Recitation | | Assessment Methods: |
| Method | Quantity |
(%) | | Quiz | 2 | 5 | | Homework | 2 | 5 | | Midterm Exam(s) | 1 | 40 | | Final Exam | 1 | 50 | | | Language of Instruction: | English | | Work Placement(s): | N/A |
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