Regression Estimator
lRegression used to relate categorized pixel counts to the ground reference data
–Independent variable - satellite data - pixels
–Dependent variable - JAS acreage estimate
lSatellite data - lower variance than with only JAS
lOutlier segment detection - correction or removal from regression analysis
•Where available, regression is chosen as the preferred type of estimation. This approach essentially corrects the area sample (ground only) estimate based on the relationship found between reported data and classified pixels in each stratum where it is used.
•A regression relationship should be based on 10 or more segments for any stratum used, however, as few as five segments have been used in the past.  Where there are not enough segments in each stratum, a pixel based ratio estimator may be used which essentially combines data across stratum to get the relationship.
•Finally, the direct expansion (total number of possible segments times the average for sampled segment) may be used in the absence of pixel based methods.
•Regression adjusts the direct expansion estimate based on pixel information. It usually leads to an estimate with a much lower variance than direct expansion alone.
•Segments, called outliers, which do not fit the linear relationship estimated by the regression are reviewed; if errors are found, they are corrected or that segment may be removed from consideration in the regression analysis.
•This graph shows the approximately linear relationship between acres reported during the ground survey and pixels categorized to rice by the classification process.
•Several possible outliers are visible.
•The R-squared term measures how close the relationship is to a straight line; the closer it gets to 1.00, the better it is.