Statistics Department

PO's & CO's

PO1: Scientific Knowledge & Understanding - Deep understanding of core scientific disciplines—mathematics, physics, chemistry, electronics, statistics, computer science, and data science—plus honours-level expertise in specialized areas.
PO2: Analytical & Problem-Solving Skills - Apply mathematical modelling, computational techniques, statistical analysis, and experimental approaches to solve complex scientific problems.
PO3: Laboratory & Technical Proficiency - Competence in using experimental methods and tools—for chemistry labs, electronics experiments, computer labs, and statistical/data science software.
PO4: Communication Skills - Clearly articulate scientific ideas and findings in both oral and written forms to technical and non-technical audiences.
PO5: Interdisciplinary Integration - Ability to synthesize concepts and methodologies across disciplines—such as applying statistics to physical science data or using programming to simulate physical systems.
PO6: Ethics & Responsibility - Uphold professional integrity, ethics, and social responsibility in scientific and technological applications.
PO7: Professional & Career Readiness - Equipped for internships, research, industry roles, or higher studies with a strong foundational training.
PO8: Adaptability & Lifelong Learning - Build a growth mindset to continuously adapt and learn in rapidly evolving scientific and technological landscapes.

Able to analyse the data sets by choosing appropriate statistical techniques either manually or using Excel & R, and preparing a basic statistical report.
Able to compute the probability using counting methods / appropriate probability theorems.
Able to evaluate the distribution function from probability mass/density functions and vice versa, and also evaluate the probability function for the transformations of random variables.
Able to evaluate the Moments, MGF, CGF, PGF, and ChF for the random variables and evaluate moments from the generating functions (MGF, CGF, and ChF).
Able to identify the real-time applications in various domains like Finance, business, insurance, clinical, medical, health, bio sciences, engineering, and technology, etc.

Derive and interpret properties (mean, variance, MGF, etc.) of standard discrete distributions: Binomial, Poisson, Geometric, Negative Binomial.
Derive and applications of discrete and continuous distributions like Hyper Geometric, Uniform and Normal distributions in real contexts.
Derive and applications of continuous distributions like Exponential, Gamma, Beta, Cauchy in real contexts.
Understand and derive exact sampling distributions (χ², t, F) and their properties. Understand sampling theory concepts and apply to standard error estimates.
Recognize interrelationships among distributions and their use in statistical inference.

Familiar to estimate the parameter (if exists) for the any standard probability distribution(s) using methods of maximum likelihood and method of moments, and estimation of parameters using method of least squares.
Examine the estimator satisfies the criteria of good estimation (Consistency, unbiasedness, sufficiency) for standard distributions.
Evaluate and familiar the confidence interval estimates for the parameter using pivot method.
Framing statistical hypothesis and knowing the statistical test procedure.
Identifying the suitable large/small sample parametric/non-parametric tests among the basic statistical tests.

Able to designing an experiment for the given task and carrying out its analysis.
Able to identify the suitable complete block experimental design and carrying out its analysis for the given data.
Able to identify the suitable model and carryout the complete analysis for the simple & multiple linear regression for the given data set.
Able to identify and compute the suitable measure of association / correlation for the given data set

Students will learn about sample surveys by using concepts of population sampling unit statistic and sample frame and also able to know standard error, differences between census and sample survey and advantages of limitations of sampling
Stratified random sampling with proportional and Nay man allocation, systematic sampling, time series and its components
Learning of Scope of operations research, convex sets and their properties and to learn about general LPP and how to write simplex algorithm
Learn about concepts of artificial variables big - M method, concept of Degeneresy and resolving it
Transportation problem and to know about initial basic feasible solution by various methods and balanced and unbalanced transportation problems
Hungarian method and travelling salesman problem and its solutions, optimal sequence of N jobs on 2 and 3 machines without passing
Growth Curves and modified exponential curves and logistic curve, determination of seasonal indices.

Importance of SQC in industry, statistical basis of Shewartz, construction of control charts for variable
Control charts for attributes and their interpretation, Natural tolerance limits, process capability
Concepts of acceptance sampling plans producers risk and consumers risk and Single and double sampling plans, OC curves and ASN functions.
Explain the concept, uses, and limitations of index numbers, Construct simple and weighted index numbers including Laspeyres, Paasche, and Fisher’s indices, Evaluate index numbers using tests of adequacy, and Perform base shifting, splicing, and deflation of index numbers.
Introduction to demand and supply, price elasticity of demand and supply, methods of Leontief's, Pigou's methods of determining demand curve from time series data and index numbers
Identify and decompose components of time series, Apply trend estimation methods such as least squares and moving averages, Fit growth curves like exponential, Gompertz, and logistic models and compute seasonal indices using standard methods.
Introduction to vital statistics and its sources, registration method and census method measurement of population and growth, crude rate of natural increase - pearl's vital index