Course Contents

Meaning of sampling, sample versus complete enumeration, advantages of sampling, probability and non-probability sampling. Sampling and non-sampling errors, bias in sampling. precision and accuracy, random sampling and use of random number tables. Description of sample design, simple random sampling. Concepts of parameter and statistics, sampling distribution of a statistic, standard error, sampling distribution of the mean and difference between two means, sampling distribution of proportions and difference between two proportions. Problems of estimation, estimator and estimate, point and interval estimation, properties of estimator, unbiasedness, consistency, efficiency and sufficiency. Confidence interval and its interpretation, large sample confidence intervals for mean and difference between two means, proportion and difference between two proportions, one sided confidence intervals. Statistical hypothesis, null and alternative hypothesis, simple and composite hypothesis. Type-I and type-II errors, one-sided and two-sided tests. Large and small sample test of hypothesis for mean and difference between two means, proportion and difference between two proportions. Confidence interval for single variance, Chi Square test for single variance. Chi square test for goodness of fit of proportions (multinomial distribution), Binomial, Poisson and Normal distributions. Contingency tables, Test of Association / Independence. Yates correction for continuity, Co-efficient of contingency. Inference for variance ratio (F-test). Case Study: Analyze the real data and present the work in the form of report.