MT-330 APPLIED PROBABILITY & STATISTICS
CREDIT HOURS
Theory = 2
Practical = 1
COURSE LEARNING OUTCOMES (CLOs)
| S. No. | CLOs | Taxonomy |
| 1 | Identify the fundamental concepts in Probability and Statistics | Coginitive Level 1* |
| 2 | Analyze on data & produce mathematical probabilistic models for different problems and to interpret the results | Coginitive Level 4* |
| 3 | Apply the rules and algorithms of Probability and Statistics to their relevant engineering problems | Coginitive Level 3* |
COURSE CONTENT
- Statistics: Introduction, types of data & variables, presentation of data, object, classifications, tabulations, frequency distribution, graphical representation, simple and multiple bar diagrams, sartorial and pie-diagram, histogram, frequency polygon, frequency curves and their types.
- Measure of Central Tendency & Dispersion: Statistics averages, median, mode, quartiles, range, moments, skew-ness and Kurtosis. Quartile deviation, mean deviation, standard deviation, variance and its coefficient. Significance in related problems.
- Curve Fitting: Introduction, fitting of a first and second degree curve, fitting of exponential and logarithmic curves, related problems. Principle of least squares and second order statistics and time series.
- Simple Regression & Correlation: Introduction, Scatter diagrams, Correlation & its Coefficient, Regression lines, Rank Correlation & its Coefficient, Probable Error (PE), related problems.
- Sampling & Sampling Distributions: Introduction, Population, Parameter & Statistic, Objects of sampling, Sampling distribution of Mean, Standard errors, Sampling & Non-Sampling errors, Random Sampling, Sampling with & without replacement, Sequential Sampling, Central limit theorem with practical significance in related problems.
- Statistical Inference: Testing of Hypothesis: Introduction, Estimation, Types of Estimates, Confidence interval, Tests of Hypothesis, Chi-Square distribution/test, one tails & two tails tests. Application in related problems.
- Probability: Basic concepts, Permutation & Combination, Definitions of probability, Laws of probability. Conditional probability, Baye's nile. Related problems in practical significance.
- Random Variables: Introduction, Discrete & Continuous random variables. Random Sequences and transformations Probability distribution, Probability density function, Distribution function, Mathematical expectations, Moment Generating Function (M.G.F), Markove random walk chain related problems.
- Probability Distributions: Introduction, discrete probability distributions, Binomial, Poisson, Hyper geometric & Negative binomial distributions. Continuous probability distribution, Uniform, Exponential & Normal distributions & their practical significance.
RECOMMENDED BOOKS
(01) Introduction to Statistics by Walpole
(02) Mathematical Statistics by Hogg and Craig
(03) Exploring Statistics by Larry J. Kitchens
*For details of Taxonomy Levels CLICK HERE!
