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# List of Courses  ## Probability and Statistics (Math 6)

• Course Code :
MTH 312
• Level :
• Course Hours :
3.00 Hours
• Department :
Department of Mechanical Engineering

## Area of Study :

Demonstrate a conscious understanding of the concepts of mathematical expressions of statistical Science. Develop students’ mathematical skills for basic inferential statistical studies. Acquire skills for the application of statistic methods to the solution of electrical engineering problems.

## Probability and Statistics (Math 6)

Descriptive statistics and data analysis, Introduction to probability theory, conditional probability, Bayes theorem, Random variables and probability distribution, Discrete and continuous random variables, Mathematical expectation of random variables and some special expectation, Some discrete probability distribution (Binomial and Poisson). Some continuous distribution (Normal distribution, t-distribution), Introduction to estimation and tests of hypothesis. Correlation analysis, applied statistics.

## a. Knowledge and Understanding:

 1- Recognize the fundamental features of the probability theory, and other statistical topics. 2- Distinguish the meaning of conditional probability and its application. 3- Describe random variables, discrete and continuous distributions. 4- Define samples and population measures (point and interval estimate).

## b. Intellectual Skills:

 1- Summarize Statistical concepts essential and necessary for applications in mechanical engineering problems 2- Think logically and creatively to apply random theory in the solution of mechanical Engineering Problems. 3- Analyze the appropriate method for the solutions of statistical engineering problems using convenient methods.

## c. Professional and Practical Skills:

 1- Use the different data to obtain objective conclusions. 2- Apply a mathematical technique to solve engineering problems.

## d. General and Transferable Skills:

 1- Communicate effectively. 2- Effectively manage tasks, time and resources

## Course topics and contents:

Topic No. of hours Lecture Tutorial/Practical
Descriptive statistics and data analysis. Definitions and concepts. 10 6 4
Probability Introduction to probability theory, conditional probability, Bayes theorem 10 6 4
Mathematical expectation of random variables and some special expectation. 10 6 4
Some discrete probability distribution (Binomial and Poisson). 10 6 4
Some continuous distribution (Normal distribution). 10 6 4
Introduction to the estimation and tests of hypothesis. 10 6 4
Correlation analysis. 5 3 2
Random variables and probability distribution: Discrete and continuous random variables 10 6 4

## Teaching And Learning Methodologies:

Teaching and learning methods
Interactive Lecture
Problem-based Learning
Discussion
Report

## Course Assessment :

Methods of assessment Relative weight % Week No. Assess What
Participation 10.00 To assess understanding and problem solving skills
Assignments 5.00 To assess lecture material comprehension
Final exam 40.00 16
First Exam 20.00 5 To assess understanding and problem solving skills
Quizzes 5.00 To assess material comprehension & self study.
Second Exam 20.00 10 To assess understanding and problem solving skills

## Books:

Book Author Publisher

## Recommended books :

 • WARREN S. WRIGHT, DENNIS G. ZILL, “Advanced Engineering Mathematics “, Jones &Bartlett Learning Publisher Fifth Edition. • EARL W. SWOKOWSKI, “Calculus with Analytic Geometry”, PWS Publishers, alternate Edition, 1983.

# List of Courses 