Program Specific Outcomes Course Outcome
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.No | SEM/Course code | Course Title | CO | Course Outcome |
| 1. | I STA101 | Descriptive Statistics and Probability | CO 1 | Acquire 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 2 | Distinguish 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 3 | Explain discrete and continuous random variables and illustrate knowledge related to their probability distributions including expectations and moments. | |||
| CO4 | Define expectation of discrete and continuous random variables, derive generating functions and solve them to obtain descriptive measures. | |||
| STA 111 | Descriptive Statistics and Probability | CO1 | demonstrate 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. | II STA 202 | Probability Distributions | CO 1 | define Bernoulli trials |
| CO 2 | demonstrate 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 3 | evaluate generating functions for discrete and continuous distributions; derive their descriptive measures. | |||
| CO4 | express approximations of discrete and continuous probability distributions. | |||
| STA 212 | Probability Distributions | CO1 | . fit Binomial, Poisson, Geometric, Negative Binomial and Hyper-geometric, Normal, Exponential, Beta and Gamma distributions and draw the respective curves | |
| 3. | III STA 303 | Linear Regression Analysis & Statistical Inference I | CO 1 | identify the types of data reflecting quality characteristics and explain the independence and association between two attributes. |
| CO 2 | acquire knowledge of curve fitting using Legender’s Principle of Least Squares, correlation for quantitative and ranked data, regression analysis, partial and multiple correlations. | |||
| CO 3 | explain the basic concepts of estimation, exact sampling distributions and derive their interrelationships. | |||
| CO4 | define point and interval estimation procedures, identify a good estimator and construct confidence intervals. | |||
| STA 313 | Linear Regression Analysis & Statistical Inference I | CO1 | Simulate 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. | IV STA 404 | Statistical Inference II | CO 1 | Estimate unknown population parameters using maximum likelihood method and method of moments. |
| CO 2 | Acquire knowledge about important inferential aspects; derive the most powerful critical region/test using Neyman Pearson Lemm | |||
| CO 3 | Describe 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. | |||
| CO4 | Differentiate 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 414 | Statistical Inference II | CO1 | Based on normal distribution, Chi-square, Student’s t and Snedecor’s F distributions and draw appropriate inference. | |
| CO2 | Non-parametric test – run test, sign test, Wilcoxon-signed Rank test, Wilcoxon-Mann Whitney test and Median test and draw inference. | |||
| V STA 505 | Sampling Techniques, Time Series and SQC | CO 1 | demonstrate the knowledge of basic concepts of sample surveys and compare the sampling techniques – SRSWR, SRSWOR, time series and SQC | |
| CO 2 | Estimate population mean, total, and proportion, their variances by Stratified Random Sampling and Systematic Random Sampling. | |||
| CO 3 | explain the methods to measure trend, seasonal variations and forecast a business series. | |||
| CO4 | explain the concepts and importance of quality control, 7QC tools and construct control charts for variables and attributes and draw interpretations. | |||
| STA 515 | Sampling Techniques, Time Series and SQC | CO1 | fit trend and compute seasonal indices by various methods for a time series and forecast a time series using exponential smoothing. | |
| CO2 | construct control charts for variables, attributes and draw interpretations. | |||
| 5. | VI STA 606(A) | ANOVA, DoE, Vital Statistics and Index Numbers | CO 1 | Explain the basics of Analysis of Variance (ANOVA), design of experiments, vital statistics and index numbers. |
| CO 2 | differentiate 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 3 | describe the methods of computing National Income, different mortality, birth, reproductive, fertility rates and life tables. | |||
| CO4 | summarize the purpose and problems in construction of index numbers, describe various index numbers, base shifting, splicing and deflation. | |||
| STA 616 | ANOVA, DoE, Vital Statistics and Index Numbers | CO1 | analyze and interpret the experiment results by ANOVA – one way and two way classifications and compute the efficiency of CRD over RBD. | |
| CO2 | compute various vital rates – mortality, birth, reproductive, fertility rates. | |||
| STA 607(A) | Essential Statistics | CO 1 | Understand the scope and basic concepts of statistics, summary statistics, bivariate data and inferential statistics | |
| CO 2 | Identify the scales of measurement, analyze the data using summary statistics and regression analysis | |||
| CO 3 | Illustrate the data with appropriate graphs and diagrams and analyze the data using MS Excel and R Programming | |||
| CO4 | Set up hypotheses and evaluate the various parameters using statistical inference |