Sampling & Survey # 9 – Regression Estimation

Today, we shall look at regression estimation. We will begin by looking at the usual & simple straight line regression model: y = B_0 + B_1 x. Let \hat{B_1} and \hat{B_0} by the ordinary least squares (OLS) regression coefficients of the slope and intercept.
\hat{B_1} = \frac{\sum_{i=1}^n (x_i - \bar{x})(y_i - \bar{y})}{\sum_{i=1}^n (x_i - \bar{x})^2} = \frac{s_{xy}}{s_x^2} = \frac{r s_y}{s_x}
\hat{B_0} = \bar{y} - \hat{B_1} \bar{x}

Precision is increase, that is SE(\hat{\bar{y}}_{reg}) < SE(\bar{y})

Different estimators for population total

Different estimators for population total

We conclude here by observing that ratio or regression estimators give greater precision that \hat{t_y} when \sum_{i=1}^n e_i^2 for the method is smaller than \sum_{i=1}^n (y_i - \bar{y})^2

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

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