| Course Name: | PROBABILITY AND STATISTIC II |
| Code | Course type | Regular Semester | Theoretical | Practical | Credits | ECTS |
| MATH 315 | 2 | 6 | 3 | - | 3 | 6 |
|
| Name of Lecturer(s)-Academic Title: | Younis Sabawi - |
| Teaching Assistant: | Younis Sabawi |
| Course Language: | English |
| Course Type: | Main |
| Office Hours | 9:00-11:00 Monday |
| Contact Email: | [email protected]
Tel:07709341261
|
| Teacher's academic profile: | Lecturer
|
| Course Objectives: | This course introduces the basic notions of probability theory and develops them to the stage where one can begin to use probabilistic ideas in statistical inference and modeling, and the study of stochastic processes. The study of statistics is important in the elementary grades because society frequently organizes and expresses data numerically. For that purpose, we will introduce the concept of random variables (discrete and continuous) to map our abstract probability problems into Euclidean real space. We will also discuss Multivariate Distribution, Conditional Distributions, Moment Generating Functions. The main objective is to provide students with the foundations of statistical inference mostly used in business and economics. |
| Course Description (Course overview): | The concept of the normal distribution, the characteristics of normal distribution, standard normal curve areas, discrete approximation to normal distributions, binominal normal approximation, normal approximation of the Poisson distribution, hyper geometric distribution approach and applications to normal. Short of theoretical knowledge about the theory of sampling, the distribution of sample averages, the distribution of sample proportions, means the difference between the sample distribution, sample distribution of the difference between the rates and practices. Forecast for short, theoretical knowledge about the theory, the point estimate and confidence limits, confidence intervals for means, confidence intervals for proportions, confidence intervals for standard deviations, confidence intervals for the differences between means, confidence intervals for differences between proportions and applied studies. |
COURSE CONTENT| Week | Hour | Date | Topic | | 1 |
3 |
3-7/2/2019 |
General discrete probability space, Discrete random variable | | 2 |
3 |
10-14/2/2019 |
Poisson random variable |
| | 3 |
3 |
17-21/2/2019 |
Poisson distribution | | 4 |
3 |
24-28/2/2019 |
Definition of geometric distribution, Expectation and Variance of a geometric variable |
| | 5 |
3 |
3-7/3/2019 |
Continuous distributions | | 6 |
3 |
26-28/3/2019 |
Probability Density Function, Cumulative distribution function |
| | 7 |
3 |
31/3-4/4/2019 |
Variance of continuous random variable | | 8 |
3 |
7-11/4/2019 |
Continuous uniform distribution |
| | 9 |
3 |
14-18/4/2019 |
Midterm Exam | | 10 |
3 |
21-25/4/2019 |
Normal (Gaussian) random variables |
| | 11 |
3 |
28/4-2/5/2019 |
Using the standard normal table | | 12 |
3 |
5-9/5/2019 |
Joint Marginal Probability Distribution, Some Examples |
| | 13 |
3 |
12-16/5/2019 |
Exception of Marginal Probability Distribution | | 14 |
3 |
19-23/5/2019 |
Variance of Marginal Probability Distribution |
| | 15 |
3 |
26-30/5/2019 |
Old exams | | 16 |
3 |
9-13/6/2019 |
Final Exam |
| | 17 |
3 |
16-20/6/2019 |
Final Exam |
|
COURSE/STUDENT LEARNING OUTCOMES | | | 1 | Discrete Probability Distributions 2 Continuous Random Variables: • The probability density function • Expectation and variance • The Uniform | | 2 | distribution • The Gamma distribution • The Exponential distribution • The Chi-Square distribution • The Beta Distribution • Mean and variance of the Uniform(a, b), G(a), Beta(a, b) distributions, respectively. | | 3 | The Normal Distribution and the Central Limit Theorem (CLT). Moment generating functions | | 4 | Multivariate Distribution | | 5 | Joint Marginal Probability Distribution • Joint Expectation Probability Distribution • Conditional Distribution. • Conditional variance • Co-variance • Correlation |
|
COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
(Blank : no contribution, I: Introduction, P: Profecient, A: Advanced ) | Program Learning Outcomes |
Cont. | | 1 | Demonstrate an understanding of the common body of knowledge in mathematics. | A | | 2 | Demonstrates an understanding of pedagogical content knowledge, technology and perfectible assessment. | | | 3 | Demonstrate the ability to think critically, research scientifically, and become modern and up-to-date. | I | | 4 | Understands the interrelationship of human development, cognition, and culture and their impact on learning. | | | 5 | Demonstrate the ability to apply analytical and theoretical skills to model and solve mathematical problems. | P | | 6 | Demonstrate the ability to effectively use a variety of teaching technologies and techniques and classroom strategies to positively influence student learning. | | | 7 | Understands how to form connections among educators, families, and the larger community to promote equity and access to education for his/her students. | | | 8 | Understands assessment and evaluation of student performance and learning and program effectiveness. | | | 9 | Communicates effectively and works collaboratively within the context of a global society. | |
|
| Prerequisites (Course Reading List and References): | 1] Wayne F. Bialas. (2005) Lecture Notes in Applied Probability. Department of Industrial Engineering, University at Buffalo, 301 Bell Hall, Buffalo, New York 14260, USA. [2] Murrayr R. Spiegel, John Schiller, and R. Alu Sriniv Asan. (2001) Probability and Statistics. [3] Javier R. Movellan. (2008). Introduction to Probability Theory and Statistics. [4] G. Mohan Naidu. Lecture notes, Course No: Stca-101, Statistics. [5] Peter J. Cameron. (2000). Notes on Probability. [6] Course Notes Stats 210, Statistical Theory. [7] Robert V. Hogg and Allen T. Craig. (1978). Intriduction to Mathematical Statistics. 4th edition. Macmillan Publishing Co., Inc. |
| Student's obligation (Special Requirements): | Students must follow all my class and bring all the needed items that we use during the lecture. They must merge the concepts of Probability and Statistics I-II. |
| Course Book/Textbook: | In this course, we will introduce the concept of random variables (discrete and continuous) to map our abstract probability problems into Euclidean real space. We will also discuss Multivariate Distribution, Conditional Distributions, Moment Generating Functions. The main objective is to provide students with the foundations of statistical inference mostly used in business |
| Other Course Materials/References: | Probability and Statistics I-II |
| Teaching Methods (Forms of Teaching): | Lectures, Excersises, Presentation, Assignments |
COURSE EVALUATION CRITERIA
| Method | Quantity |
Percentage (%) | | Quiz | 2 | 7.5 | | Homework | 2 | 5 | | Midterm Exam(s) | 1 | 30 | | Presentation | 1 | 5 | | Final Exam | 1 | 40 |
| Total | 100 |
Examinations: Essay Questions, True-False, Short Answers |
|
Extra Notes:
|
ECTS (ALLOCATED BASED ON STUDENT) WORKLOAD| Activities | Quantity | Duration (Hour) | Total Work Load | | Contact Hours (Theoretical hours + Practical hours/2) x Weeks | | | 0 | | Hours for off-the-classroom study | | | 0 | | Study hours for the Midterm Exam | | | 0 | | Study hours for the Final Exam | | | 0 | | Other | | | 0 | | ECTS | | | 0 | | | | 0 | | | | 0 | | Total Workload | 0 | | ECTS Credit (Total workload/25) | 0 |
|