Group A (Marks: 20)
(Related Course - STAT-301: Statistical Inference)
This course is designed to provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in statistical research using STATA.
Course Objectives:
The objectives of the course are to:
- Learn how to enter data, define variables, and perform variable manipulation and transformation as well as analyze the data. Specifically, reading and writing STATA, and others files types with different formats; Survey coding and data entry; selected data management procedures; and Data analysis and interpretation with STATA.
- Estimate the point and interval for parameter,
- Draw samples from different parent distributions;
- Determine confidence interval for different estimator;
- Apply different parametric tests;
- Analyze different statistical problems related to this course using STATA.
Learning Outcomes:
After completing this part students will be able to:
- Find out the point and interval estimation;
- Draw samples from different distributions;
- Estimate the parameter of the sample drawn from different distributions;
- Determine confidence interval for mean, proportion, and variances;
- Test for mean, proportion, and variances;
- Perform tests of randomness for both one and two sample;
Contents:
Drawing Sample from Parent Population: Binomial, Poisson, Geometric, Hyper-geometric, Normal, Cauchy, Gamma, Beta, Incomplete Gamma and Beta.
Estimation of location and scale parameter of the sample drawn from the above distributions, maximum likelihood estimator, method of moment's estimator, method of least squares estimators. Determination of confidence interval for mean, difference of means, proportion, difference of proportions, correlation coefficient, regression coefficient, fitting of different distributions, different tests for mean, difference of means, equality of several means, proportion, difference of proportion, equality of several proportions, variances, equality of two and several variances, equality of several correlation coefficients and regression coefficients based on normal tests.
Rationale:
Group B (Marks: 15)
(Related Course - STAT-303: Design and Analysis of Experiments)
This course is designed to impart students a general practical view of the fundamentals of experimental designs, analysis tools and techniques, interpretation and applications.
Course Objectives:
The objectives of this course are to:
- Understand the issues and principles of Design of Experiments;
- Identify situations where one-way/two-way/three-way (fixed effects, mixed effects and random effects models) ANOVA is and is not appropriate;
- State the modeling assumptions underlying ANOVA and also state the null and alternative hypotheses for the ANOVA test;
- Make the students able to make report based on experimental designs.
Learning Outcomes:
After successful completion of this course students will be able to:
- Build and apply experimental designs for the real-world problems.
- Estimate one-way/two-way/three-way (fixed effects, mixed effects and random effects models) ANOVA;
- Test the appropriate hypothesis for the ANOVA test;
- Perform multiple comparison test;
- Interpret the results and computer output from all of the above designs and present clear, orderly and informative statistical summaries and technical reports.
Contents:
Analysis of variance in one way, two way and three way classification with equal number of observations per cell with fixed effects, mixed effects and random effects models, variance component analysis in one way, two way and three way classified data, covariance analysis in CRD, RBD and LSD with one concomitant variable, missing data in RBD and LSD, multiple comparison, test of additivity of the model in case of two-way classification.
Rationale:
Group C (Marks: 15)
(Related Course - STAT-304: Categorical Data Analysis)
This course is designed to implement practical applications for categorical data analysis.
Course Objectives:
The objectives of this course are to:
- Understand categorical Data;
- Explore the basic asymptotic tools;
- Analyze contingency tables and inference for two way contingency tables; Estimate and test the categorical data;
- Apply Regression models for Categorical and Count Models;
- Analyze models for multinomial responses and matched pairs;
- Analyze clustered categorical data and making decision;
- Inference for Log linear models.
Learning Outcomes:
After completing this course students will be able to:
- Familiar with a variety of methods for analyzing categorical or count data;
- Learn Statistical inference for categorical data;
- Describe and analyze contingency tables;
- Analyze generalized linear models and its likelihood;
- Learn how to estimate and test categorical data;
- Apply logistic regression models in several settings including cohort and case-control studies;
- Test independency of contingency tables;
- Demonstrate the concept of models for multinomial responses;
- Analyze models for matched pairs;
- Learn clustered categorical data analysis by likelihood approach;
- Estimate the parameters using different approach.
Contents:
Chi-square test, exact test for small samples, association in three-way tables, generalized linear model, GLMS for binary data, GLMs for count data, inference and model checking, fitting generalized linear models, logistic regression-building and applying: interpreting logistic regression, inference for logistic regression, multiple logistic regression, strategies in model selection, model checking, multi-category logit models, logit models for nominal response, model for matched pairs: comparing dependent proportions, measuring agreement, loglinear models, loglinear models for 2-way and 3-way tables, inference for loglinear model.