Today, we shall look at regression estimation. We will begin by looking at the usual & simple straight line regression model: . Let and by the ordinary least squares (OLS) regression coefficients of the slope and intercept.

= =

Precision is increase, that is $latex SE(\hat{\bar{y}}_{reg}) < SE(\bar{y})$ [caption id="attachment_2634" align="alignnone" width="300"] Different estimators for population total[/caption]

We conclude here by observing that ratio or regression estimators give greater precision that when for the method is smaller than

Sampling & Survey #1 – Introduction

Sampling & Survey #2 – Simple Probability Samples

Sampling & Survey #3 – Simple Random Sampling

Sampling & Survey #4 – Qualities of estimator in SRS

Sampling & Survey #5 – Sampling weight, Confidence Interval and sample size in SRS

Sampling & Survey #6 – Systematic Sampling

Sampling & Survey #7 – Stratified Sampling

Sampling & Survey # 8 – Ratio Estimation

Sampling & Survey # 9 – Regression Estimation

Sampling & Survey #10 – Cluster Sampling

Sampling & Survey #11 – Two – Stage Cluster Sampling

Sampling & Survey #12 – Sampling with unequal probabilities (Part 1)

Sampling & Survey #13 – Sampling with unequal probabilities (Part 2)

Sampling & Survey #14 – Nonresponse