## Sampling Theory and MethodsSampling Theory and Methods presents in detail several sampling schemes like simple random sampling, unequal probability sampling methods, systematic, stratified, cluster and multistage sampling. In addition to sampling schemes a number of estimating methods which include ratio and regression estimators are also discussed. The use of superpopulation models is covered in detail along with recent developments including estimation of distribution functions, adaptive sampling schemes etc. New to the Second Edition: *Contents reorganized to establish a coherent link between various concepts *Several numerical examples associated with real life solutions for bringing out the relevance of theory in real life context |

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### Contents

Preliminaries | 1 |

Equal Probability Sampling | 13 |

Systematic Sampling Schemes | 33 |

Unequal Probability Sampling | 61 |

Stratified Sampling | 85 |

Use of Auxiliary Information | 110 |

Regression Estimation | 138 |

Multistage Sampling | 157 |

Nonsampling Errors | 169 |

Recent Developments | 182 |

References | 195 |

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### Common terms and phrases

approximate bias approximate mean square assuming auxiliary variable balanced systematic sampling bias and mean Biometrika chapter cluster sampling conventional estimator coprime defined denote difference estimator draw drawn independently estimate the population expected value finite population following theorem gives given cost Hence the proof Hence the solution Horvitz-Thompson estimator initial sample interviewer linear regression linear systematic sampling linear trend mean square error modified systematic sampling MSE(P Multistage sampling Note number of units obtained optimum allocation order inclusion probabilities phases of sampling population mean population total population units PPSWR primary stage units probability proportional Problem product estimator proportional allocation random number ratio estimator regression estimator respect rth unit sample mean sample size Sampled units samples are drawn sampling design second order inclusion simple random sampling strata stratified sampling stratum h subsample Substituting Taking expectations unbiased estimator unit is selected units with labels

### References to this book

Maple: Programming, Physical and Engineering Problems Victor Aladjev,Marijonas Bogdevicius Limited preview - 2006 |