Program Specific Outcomes Course Outcome

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Department of Statistics

Program Specific Outcomes Course Outcome

Program Specific Outcomes

S.NO

Program

Program Specific Outcome

1

BSc MSCs

PSO1

Acquire strong foundation from fundamental concepts to advanced areas of Mathematics, Statistics and Computer science; attain global competency exhibiting analytical, logical, programming and research abilities.

PSO2

Develop proficiency in data analysis & interpretation towardsresearch and collaborate efficiently both as a team player and a leader

PSO3

Gain employable skills through hands-on coding & computing abilities and inculcate a spirit of lifelong learning adapting to the new demands from industry

2

B.Sc. M.S.Ds

PSO1

Acquire strong foundation from fundamental concepts to advanced areas of Mathematics, Statistics and Data science; attain global competency exhibiting analytical, logical, programming and research abilities.

PSO2

Develop proficiency to apply core knowledge and skills for data analysis & interpretations and modelling towards research in evolving fields; demonstrate the aptitude to collaborate both as a team player and a leader

PSO3

Gain employable skills through hands-on coding & programming abilities and inculcate a spirit of lifelong learning developing ability to update to the new demands from industry.

Course Outcome
S.NoSEM/Course codeCourse TitleCOCourse Outcome
1.ISTA101Descriptive Statistics and ProbabilityCO 1Acquire knowledge of the importance of statistics in various domains, list various sources and types of data, identify scales of measurements, organise data and describe summary measures
CO 2Distinguish between random and non random experiments, define various approaches of probability, deduce results in probability and compute the probabilities of events using classical approach.
CO 3Explain discrete and continuous random variables and illustrate knowledge related to their probability distributions including expectations and moments.
CO4Define expectation of discrete and continuous random variables, derive generating functions and solve them to obtain descriptive measures.
STA 111Descriptive Statistics and ProbabilityCO1demonstrate basic skills of MS – Excel and R programming, compute descriptive statistics, moments, coefficients of skewness and kurtosis and interpret the same.
CO2 identify outliers in a given data set.
2.IISTA 202Probability DistributionsCO 1define Bernoulli trials
CO 2demonstrate knowledge of important discrete and continuous distributions such as Binomial, Poisson, Geometric, Negative Binomial and Hyper-geometric, Normal, Uniform, Exponential, Cauchy, Gamma, Beta and distributions.
CO 3evaluate generating functions for discrete and continuous distributions; derive their descriptive measures.
CO4express approximations of discrete and continuous probability distributions.
STA 212Probability DistributionsCO1. fit Binomial, Poisson, Geometric, Negative Binomial and Hyper-geometric, Normal, Exponential, Beta and Gamma distributions and draw the respective curves
3.IIISTA 303Linear Regression Analysis & Statistical Inference ICO 1identify the types of data reflecting quality characteristics and explain the independence and association between two attributes.
CO 2acquire knowledge of curve fitting using Legender’s Principle of Least Squares, correlation for quantitative and ranked data, regression analysis, partial and multiple correlations.
CO 3explain the basic concepts of estimation, exact sampling distributions and derive their interrelationships.
CO4define point and interval estimation procedures, identify a good estimator and construct confidence intervals.
STA 313Linear Regression Analysis & Statistical Inference ICO1Simulate random samples from Uniform (0,1), Uniform (a, b), Exponential, Normal and Poisson distributions, create a contingency table and perform the analysis for attributes data.
CO2 Analyse bivariate data – construct suitable mathematical relationships, perform simple linear regression analysis, compute multiple and partial correlation coefficients. Construct confidence intervals for mean.
4.IVSTA 404Statistical Inference IICO 1Estimate unknown population parameters using maximum likelihood method and method of moments.
CO 2Acquire knowledge about important inferential aspects; derive the most powerful critical region/test using Neyman Pearson Lemm
CO 3Describe and/or apply suitable large sample test based on normal distribution, small sample tests based on chi-square, Student’s t and Snedecor’s F distributions and draw inferences.
CO4Differentiate between parametric and non-parametric tests of significance; describe and/or apply suitable non-parametric test (run test, sign test, Wilcoxon-signed Rank test, Wilcoxon-Mann Whitney test and Median test) and draw inferences.
 STA 414Statistical Inference II CO1Based on normal distribution, Chi-square, Student’s t and Snedecor’s F distributions and draw appropriate inference.
CO2Non-parametric test – run test, sign test, Wilcoxon-signed Rank test, Wilcoxon-Mann Whitney test and Median test and draw inference.
   VSTA 505   Sampling Techniques, Time Series and SQC CO 1demonstrate the knowledge of basic concepts of sample surveys and compare the sampling techniques – SRSWR, SRSWOR, time series and SQC
CO 2Estimate population mean, total, and proportion, their variances by Stratified Random Sampling and Systematic Random Sampling.
CO 3explain the methods to measure trend, seasonal variations and forecast a business series.
CO4explain the concepts and importance of quality control, 7QC tools and construct control charts for variables and attributes and draw interpretations.
STA 515Sampling Techniques, Time Series and SQCCO1fit trend and compute seasonal indices by various methods for a time series and forecast a time series using exponential smoothing.
CO2construct control charts for variables, attributes and draw interpretations.
5.VISTA 606(A)ANOVA, DoE, Vital Statistics and Index NumbersCO 1Explain the basics of Analysis of Variance (ANOVA), design of experiments, vital statistics and index numbers.
CO 2differentiate between one-way and two-way classified data and CRD, RBD, LSD, Factorial Experiments – 22, perform ANOVA for all, estimate missing observations and derive the efficiencies.
CO 3describe the methods of computing National Income, different mortality, birth, reproductive, fertility rates and life tables.
CO4summarize the purpose and problems in construction of index numbers, describe various index numbers, base shifting, splicing and deflation.
STA 616ANOVA, DoE, Vital Statistics and Index Numbers CO1analyze and interpret the experiment results by ANOVA – one way and two way classifications and compute the efficiency of CRD over RBD.
CO2compute various vital rates – mortality, birth, reproductive, fertility rates.
 STA 607(A)  Essential StatisticsCO 1Understand the scope and basic concepts of statistics, summary statistics, bivariate data and inferential statistics
CO 2Identify the scales of measurement, analyze the data using summary statistics and regression analysis
CO 3Illustrate the data with appropriate graphs and diagrams and analyze the data using MS Excel and R Programming
CO4Set up hypotheses and evaluate the various parameters using statistical inference
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