MT-330 Applied Probability & Statistics

MT-330 APPLIED PROBABILITY & STATISTICS

CREDIT HOURS

Theory = 2
Practical = 1

COURSE LEARNING OUTCOMES (CLOs)

 S. No. CLOs PLO Taxonomy 1 Discuss the fundamental concepts in Probability and Statistics PLO-1 Coginitive Level 2* 2 Analyze on data & produce mathematical probabilistic models for different problems and to interpret the results PLO-2 Coginitive Level 4* 3 Perform statistical analysis on data through computer software PLO-5 Psychomotor 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