Probability And Statistics
course in Probability and Statistics helps students solve problems and make
optimal decisions in uncertain conditions, select stochastic models, compute
probabilities and forecasts, and evaluate performance of computer systems and
The course is intended to:
an understanding of some of the basic ideas of reliability theory.
Give students experience in the use of a
statistical software packages
Equip students with basic techniques of probability
and descriptive Statistics.
Introduce students to the basic discrete and
continuous probability models.
Provide the students with the necessary background
for inferential statistics, mathematical modelling and related subjects
Equip students with the knowledge of the
application of probability and descriptive statistics to real- world problems
By the end of the course, students
should be able to:
- Demonstrate skills in the applications of probability and
statistics in engineering and science
- Describe several well-known distributions, including Binomial,
Geometrical, Negative Binomial, Pascal, Normal and Exponential Distribution.
- Describe the concepts of various parameter estimation methods, like
method of moments, maximum likelihood estimation and confidence intervals.
- Apply the central limit theorem to sampling distribution.
- Demonstrate estimation techniques to determine point estimates,
confidence intervals, and sample size.
- Apply the appropriate Chi-squared tests for independence and
goodness of fit
- Perform and analyze hypotheses tests of means, proportions and
variances using both one-sampled and two-sampled data sets.
- Implement the analyses in SAS, S-PLUS, R or MATLAB