Course Title: Probability and Statistics
Type of Course: Compulsory, Theory, Non-departmental
Offered to: EEE
Pre-requisite Course(s): None
Introduction. Sets and probability. Random variables. Properties describing distributions. Treatment of grouped sample data. Some discrete probability distributions. Normal distribution. Sampling theory. Estimation theory. Tests of hypotheses. Regression and correlation. Analysis of variance.
To understand fundamental concepts in probability and statistics.
To apply rules and algorithm of probability and statistics in various logical problems.
To enable students maneuver mathematical probabilistic models for different problems, to analyze them and to interpret the results.
Familiarity with basic properties of set, real number system and function, fundamental concepts of calculus and preliminary knowledge to solve algebraic equations.
| CO No. | CO Statement | Corresponding PO(s)* | Domains and Taxonomy level(s) | Delivery Method(s) and Activity(-ies) | Assessment Tool(s) |
|---|---|---|---|---|---|
| 1 | Demonstrate the idea of frequency distribution, mean, median, mode and other measures of central tendency. | PO(a) | C3 | Lectures, Homework | Written exams; assignment |
| 2 | Develop the preliminary concept of standard deviation, moments, skewness, kurtosis, and other measures of dispersion for statistical data. | PO(c) | C6 | Lectures, Homework | Written exams; assignment |
| 3 | Apply the probability theory including discrete probability distribution and continuous probability distributions in real life problem. | PO(a) | C3 | Lectures, Homework | Written exams; assignment |
| 4 | Illustrate the basic idea of sampling theory including estimation, hypothesis testing, regression analysis and correlation coefficients. | PO(c) | C3 | Lectures, Homework | Written exams; assignment voce; presentation; assignment |
* Cognitive Domain Taxonomy Levels: C1 – Knowledge, C2 – Comprehension, C3 – Application, C4 – Analysis, C5 – Synthesis, C6 – Evaluation, Affective Domain Taxonomy Levels: A1: Receive; A2: Respond; A3: Value (demonstrate); A4: Organize; A5: Characterize; Psychomotor Domain Taxonomy Levels: P1: Perception; P2: Set; P3: Guided Response; P4: Mechanism; P5: Complex Overt Response; P6: Adaptation; P7: Organization
Program Outcomes (PO): PO(a) Engineering Knowledge, PO(b) Problem Analysis, PO(c) Design/development Solution, PO(d) Investigation,
PO(e) Modern tool usage, PO(f) The Engineer and Society, PO(g) Environment and sustainability, PO(h) Ethics, PO(i) Individual work and team work,
PO(j). Communication, PO(k) Project management and finance, PO(l) Life-long Learning
* For details of program outcome (PO) statements, please see the departmental website or course curriculum
| K1 | K2 | K3 | K4 | K5 | K6 | K7 | K8 | P1 | P2 | P3 | P4 | P5 | P6 | P7 | A1 | A2 | A3 | A4 | A5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Weekly schedule: For Probability and Statistics
| Week | Topics | Teacher's Initial/Remarks |
|---|---|---|
| Week-1 | Course introduction, grading policies etc. Concepts of statistics and probability, sample space, population, Experiments, Events, Sure, Impossible, Complementary, and mutually exclusive events, Frequency distribution. | |
| Week-2 | Treatment of Grouped sample data, Descriptive Statistics, Measures of central tendency. | |
| Week-3 | Computing AM, GM, HM, median and mode for group and ungrouped data, Measures of Dispersion and related topics including CV. | |
| Week-4 | Shape characteristics, moments, skewness and kurtosis, Probability theory. | |
| Week-5 | Conditional probability, partitions, total probability, Bayes’ theorem. | |
| Week-6 | Random Variables and related probability distribution, Discrete random variables, Mathematical expectation, variance, standard deviation; binomial distribution, use of statistical tables. | |
| Week-7 | Poisson distribution, Multinomial distribution, Continuous random variables, probability density function, cumulative distribution function, expected values. | |
| Week-8 | Class Test | |
| Week-9 | Normal distribution, normal approximation to binomial, Exponential distribution, Uniform distribution, Gamma distribution. | |
| Week-10 | Functions of random variables, expected value, variance, standard deviation, Two dimensional random vectors. | |
| Week-11 | Joint-distribution functions, Marginal distributions, Conditional distributions. | |
| Week-12 | Covariance, correlation, conditional expectation, central limit theorem. | |
| Week-13 | Special mathematical expectations, properties of variances, Sampling theory, sampling distribution, sampling with and without replacement. | |
| Week-14 | Class Test |
Class Participation: Class participation and attendance will be recorded in every class.
Continuous Assessment: Continuous assessment for any of the activities such as quizzes, assignment, presentation etc. The scheme of the continuous assessment for the course will be declared on the first day of classes.
Final Examination: A comprehensive term final examination will be held at the end of the term following the guideline of academic council.
Class Participation 10%
Continuous Assessment 20%
Final Examination 70%
Total 100%
Probability and Statistics for Engineers and Scientists by Walpole, Myers, Myers, and Ye, Pearson Education, Inc., Ninth Edition, 2012.
Elements of Probability and Statistics by Frank L. Wolf.
Probability and Statistics with Applications by Y. Leon Maksoudian.
Probability and Statistics for Engineers by Erwin Miller and John E. Freund.
Besides going through relevant topics of the textbook, it is strongly advised that the students follow the class Lectures and discussions regularly for a thorough understanding of the topics.