curves similar to those for regression coefficients. The 

 major difference is that the magnitude of the effect of in- 

 troducing nonrandom missing data is much less in the case 

 of autocorrelation coefficients. The ratio of ordinates 

 averages about 1. 2 instead of the 2. for the regression 

 coefficient case. The dashed curve is about 0.05 unit to 

 the left of the continuous curve rather than 0. 15 unit. If 

 the analyses of regression and autocorrelation coefficients 

 are of equal importance, then the limitations on nonrandom 

 missing data are determined by the regression coefficient 

 results above. 



A comparison of the variance determined from the 

 40 years of Scripps Pier, near-zero autocorrelation co- 

 efficients with the variance of the Monte Carlo analysis in- 

 dicates that 70 percent of the data may be randomly missing 

 before the two variances are equal. 



COMMENTS AND CONCLUSIONS 



In the analysis above, certain compromises are made 

 with computer techniques and computing times required: 



(1) The distribution of missing day sequences based on a 

 simple use of random numbers will never agree exactly 

 with the observed distribution of missing day sequences 

 for a given station. Nevertheless, the techniques used 

 provide good initial estimates of the effect of missing data. 



(2) The use of 120 Monte Carlo runs per case is a compro- 

 mise between computer time required and the apparent rate 

 of convergence to a limit of the parameters estimated. 



It is concluded that random missing data in a time 

 series result in regression coefficients whose variances in- 

 crease over those of a complete time series by an amount 

 as predicted by the reduction in sample size. However, the 

 presence of longer sequences of nonrandom missing data 

 may have a pronounced effect in estimating regression co- 



31 



