intercept errors (Equations \[unc\_slope\] and \[unc\_intercept\]), from which the linear regression uncertainty depends (as described in Section 2.3), are inversely proportional to the number of data points so that when a regression is performed on few data points, its uncertainty increases. This dependence is exemplified in Figure \[Ex\_regression\], where we have compared the sampling sets of regression lines for two stations in the EIA data set: for these two cases, the standard errors of regression slope and intercept for the station with 8 data points (on the right) are 30–50 times larger than what is found for the station with 90 data points (on the left).

![image](f08){width="16cm"}

![image](f09){width="16cm"}

The number of data points used for the regression also has an impact on the reference wind speed data uncertainty. In fact, short periods of wind plant operation record can lead to different interpretations from the reference wind resource data sets used as to whether that short period of record was above, equal to, or below the long-term average resource. Over a longer period of record, these potential discrepancies between different wind resource data sets (in our case, reanalysis products) tend to average out; therefore, leading to a reduced uncertainty. We illustrate 