Course Contents

Basic concepts, advantages of sampling methods, requirement of a good sample. Steps and problem involved in planning and conduct of census and sample surveys. Simple random sampling. Probability and non-probability samples. Estimation of mean, total, proportion and variance confidence limits, determination of sample size. Stratified random sampling. Estimation of mean, total, proportion and variance. Arbitrary, proportional and optimum allocation, Neyman allocation and their comparisons. Determination of sample size. Gain in precision in stratified sampling as compared with simple random sampling. Construction of strata. Ratio and Regression Estimates. Estimation of total, mean square error and bias in classical ratio estimate. Estimation of total and variance in linear regression estimates. Best linear unbiased estimator (BLUE). The Linear regression estimator under the linear model. Case Study: Analyze the real data and present the work in the form of report.

Course Synopsis

Concept building, Ability to apply simple random and stratified random sampling designs in different situation to solve real world problems. Students will be able to understand, ratio and regression type of estimators in both designs and their precision.

Course Learning Outcomes

Students should be able: • To define principle concepts about sampling • To apply the simple random sampling (SRS) and stratified random sampling methods • To expresses sample selection process on SRS and Stratified sampling • To Formulates and calculates the estimators of population mean, population total, population ratio of two variables, the percentage and the total number of units in the population that possess some characteristic • To Identifies and interprets confidence intervals • To Compares SRS and Stratified Random Sampling methods. • To Apply the Ratio and Regression Estimation methods for SRS and Stratified Random Sampling.