12_models - Complex Surveys Lecture 11 STAT 651: Survey...

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Complex Surveys Lecture 11 STAT 651: Survey Sampling Methods, Kaizar – p.1/14
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Lecture 16: Model-Based Inference Reading: Lohr Sections 2.8, 3.4, 4.6, 5.7, 6.7, 11.1, 11.3 Summary of Design-Based Inference Premise of Model-Based Inference Implications for Single-Variable Inference Implications for Multiple-Variable Inference STAT 651: Survey Sampling Methods, Kaizar – p.2/14
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Design-Based Inference Goal: Estimate a finite population value: mean proportion total ratio regression coefficient STAT 651: Survey Sampling Methods, Kaizar – p.3/14
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Model-Based Inference Goal: Estimate the properties of the process that generated the current population. Estimate the properties of the “Super-Population” . process finite population sample μ ¯ y U ¯ y β 1 B 1 ˆ B 1 STAT 651: Survey Sampling Methods, Kaizar – p.4/14
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Example: Nursing Home Residents μ = “process" average age ¯ y U = “snapshot" average age ¯ y = sample average age STAT 651: Survey Sampling Methods, Kaizar – p.5/14
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This note was uploaded on 07/26/2011 for the course STA 651 taught by Professor Kaizar during the Winter '11 term at Ohio State.

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12_models - Complex Surveys Lecture 11 STAT 651: Survey...

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