Semester:

3

Course Code:

EM2020

Course Name:

Probability and Statistics

Credit Value:

2 (Notional hours: 100)

Prerequisites:

None

Core/Optional

Core

Hourly Breakdown

Lecture hrs.

Tutorial hrs.

Assignment hrs.

Independent Learning & Assessment hrs.

24

4

4

68

Course Aim: To introduce basic concepts of probability and inferential statistics.

Intended Learning Outcomes:

On successful completion of the course, the students should be able to;

➢    demonstrate fundamental probability and statistical concepts.

➢    apply standard discrete and continuous probability distributions and observe their role as the foundation for statistical inference.

➢    perform estimation and testing of hypotheses on common measures in decision making.

Course Content:

➢    Concepts of probability: Discrete and continuous random variables, probability distributions, mean, expectation and variance, moment generating functions

➢    Discrete probability distributions: Bernoulli (Point binomial) Distribution, Binomial distribution, Poisson distribution, geometric distribution, Hypergeometric distribution.

➢    Continuous probability distributions: Uniform distribution, exponential distribution, normal distribution, Student-t distribution, Weibull distribution and Chi-squared distribution.

➢    Sampling distributions: The central limit theorem and normal approximation to the binomial distribution, sampling distribution of sample mean and sample variance.

➢    Estimation and Confidence Intervals: Estimation and calculation of Confidence Intervals for mean, difference of means and variance.

➢    Test of Hypothesis: Test of hypothesis for mean and difference of means

Teaching /Learning Methods:

Classroom lectures, tutorial discussions and in-class assignments

Assessment Strategy:

Continuous Assessment
40%

Final Assessment
60%

Details:
Tutorials/Assignments/Quizzes   10%
Mid Semester Examination         30%

Theory (%)

 60%

Practical (%)

 -

Other (%)

 -

Recommended Reading:

➢    D.C. Montgomery and G.C. Runger Applied Statistics and Probability for Engineers, 6th edition,(2013), John Wiley and Sons Inc.

➢    Jay L. Devore, Probability and Statistics for Engineering and the Sciences, 8th edition, (2010), Cengage Learning.