Group A (Marks: 30)
(Related Course - STAT-208: Programming (R & Python) and Simulation)
This course is planned to explore the concept of programming (R & Python) and simulation modeling related to problems in practical life.
Course Objectives:
The objectives of this course are to:
• Install different libraries and packages.
• Install R and Python.
• Perform database conversion in different formats.
• Process, summarize, analyze data using introduced software and interpret the results.
• Generate random numbers and fit different probability distributions using simulation modeling.
Learning Outcomes:
At the end of the course, students will be able to:
• Evaluate data types and their relative parameters.
• Perform statistical analysis with computer applications.
• Construct models with dependent and independent variables.
• Create loops, density functions, and statistical models.
• Display statistical graphs, tables, and analyze correlation and regression.
• Calculate probability generating functions and hence probabilities from different well-known distributions.
• Produce random numbers using different generators with validity tests.
• Generate continuous and discrete random variates from some probability functions and random numbers using the Monte Carlo simulation method.
• Apply goodness of fit tests for both discrete and continuous data.
Contents:
• Problem Solving through R and Python: Solving different statistical problems by R and Python (measures of central tendency, measures of dispersion, correlation, and regression), graphical presentation of statistical data by R, analysis of data by R, writing and running syntax in R and Python to solve different statistical problems.
• Simulation: Generating random numbers from uniform, binomial, Poisson, normal, exponential, and gamma and Weibull distributions by different Monte-Carlo methods using standard software and computer programs; testing uniform random numbers using chi-square tests, Kolmogorov-Smirnov tests, and graphical methods; assessing different statistical properties of generated data; integration by Monte-Carlo simulation.
Rationale:
Group B (Marks: 20)
(Related Course - STAT-204: Sampling Technique-1)
This course is designed to choose appropriate sampling methods for drawing samples from a population and find the relative efficiency of different sampling schemes.
Course Objectives:
The objectives of this course are to:
• Select a suitable sampling design, given available information and resources.
• Draw samples from populations using appropriate sampling techniques such as Simple Random Sampling, Stratified Sampling, Systematic Sampling, and Cluster Sampling.
• Summarize data collected from a probability survey and understand methods to determine the accuracy of the estimators.
Learning Outcomes:
After completing this course, students will be able to:
• Select a suitable sampling design, given available information and resources.
• Draw samples from populations using appropriate sampling techniques such as Simple Random Sampling, Stratified Sampling, Systematic Sampling, and Cluster Sampling.
• Summarize data collected from a probability survey and understand methods to determine the accuracy of the estimators.
Contents:
• Drawing samples by simple random sampling, stratified sampling, systematic sampling, and cluster sampling.
• Estimation of parameters in each case.
• Estimation of the variance of estimates of parameters.
• Determination of precision of estimates.
• Relative efficiency of different sampling schemes.
• Ratio, difference, regression, and product methods of estimation.
• Estimation for population total, mean, variance, and proportion.