Links to all courses

basic concepts of sampling, sampling frame, sample survey vs census, requirements of a good sample, selection bias, measurement bias, sampling and nonsampling errors, probability & nonprobability samples, framework for probability sampling.

Simple probability Samples:

Simple random sampling (SRS), estimates of population characteristics, standard errors, confidence intervals, sampling for proportions, randomization theory results for SRS, a model for SRS, situations where an SRS is appropriate.

Use of auxiliary data in ratio estimation, regression estimation, regression models, design implications of regression models, comparison of ratio & regression estimation method.

Estimation of population characteristics, systematic sampling in some special population.

Stratified Sampling:

Definition and basic ideas, theory of Stratified Sampling, allocation of observations to strata, a model for stratified sampling, post-stratification, stratified vs quota sampling.

Cluster Sampling with equal probabilities:

Notation for cluster sampling, one-stage cluster sampling, designing a cluster sample, models for cluster sampling. Comparison with SRS and systematic sampling. Determination of optimum cluster size. Stratified cluster sampling.

Sampling with unequal probabilities with replacement:

One-stage sampling with replacement, two-stage sampling with replacement, examples, randomization theory results and proofs, models for unequal probability sampling with replacement.

Sample Size:

Concept of sample size estimation. Determination of sample size for estimating mean and proportions. Design effect, sample size for comparisons of two means or proportions.

**Introduction**:basic concepts of sampling, sampling frame, sample survey vs census, requirements of a good sample, selection bias, measurement bias, sampling and nonsampling errors, probability & nonprobability samples, framework for probability sampling.

Simple probability Samples:

Simple random sampling (SRS), estimates of population characteristics, standard errors, confidence intervals, sampling for proportions, randomization theory results for SRS, a model for SRS, situations where an SRS is appropriate.

**Ratio Estimation:**Use of auxiliary data in ratio estimation, regression estimation, regression models, design implications of regression models, comparison of ratio & regression estimation method.

**Systematic Sampling:**Estimation of population characteristics, systematic sampling in some special population.

Stratified Sampling:

Definition and basic ideas, theory of Stratified Sampling, allocation of observations to strata, a model for stratified sampling, post-stratification, stratified vs quota sampling.

Cluster Sampling with equal probabilities:

Notation for cluster sampling, one-stage cluster sampling, designing a cluster sample, models for cluster sampling. Comparison with SRS and systematic sampling. Determination of optimum cluster size. Stratified cluster sampling.

Sampling with unequal probabilities with replacement:

One-stage sampling with replacement, two-stage sampling with replacement, examples, randomization theory results and proofs, models for unequal probability sampling with replacement.

Sample Size:

Concept of sample size estimation. Determination of sample size for estimating mean and proportions. Design effect, sample size for comparisons of two means or proportions.

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