The dashed curve can be interpreted another way. 

 For fractions of missing data greater than 0. 2, the dashed 

 curve lies about 0. 15 unit to the left of the continuous curve. 

 When applicable, this quantity 0. 15 should be added to the 

 actual fraction of missing data. Then conclusions about 

 nonrandom missing data can be made as for random missing 

 data, but with the larger fraction of missing data used. 



THE AUTOCORRELATION COEFFICIENT 



The effect of missing data on autocorrelation coeffi- 

 cients will now be considered. The results are perhaps not 

 as straightforward to evaluate as for regression coefficients, 

 but are more encouraging as far as tolerating nonrandom 

 missing data. Figure 10 presents the results of Monte 

 Carlo analyses of autocorrelation coefficients similar to 

 those for regression coefficients. The autocorrelation 

 coefficients are for the time series of residuals remaining 

 after the regression analyses have been performed. The 

 same combinations of stations, series length, and fractions 

 deleted are used. The variances of autocorrelation coeffi- 

 cients are averaged for lags from 10 to 100 in steps of 10. 

 Assuming the variances are inversely proportional to 

 series length, the average variances are normalized to an 

 arbitrary series length of one year. In figure 10, results 

 for random missing data are plotted as circles; results 

 for nonrandom missing data are plotted as triangles. 



The variance of near-zero autocorrelation coefficients 

 based on 40 years of Scripps Pier data is 0. 000859. 

 Normalized to the series length of one year, the variance 

 is 0. 0344. This quantity is plotted as the dashed line at the 

 top of figure 10. 



Somewhat arbitrary curves have been fitted to the 

 two sets of points. The effect of missing data on the 

 variance of autocorrelation coefficients results in 



29 



