3. The effect of nonrandom, longer sequences of 

 missing data on autocorrelation coefficients is less pro- 

 nounced than for regression coefficients. The increase in 

 the variances of autocorrelation coefficients attributable to 

 nonrandom missing data is 1.2 times the increase attributable 

 to random missing data. Alternatively, for fractions of 

 missing data greater than 0. 2, time series with nonrandom 

 missing data will have autocorrelation coefficient variances 

 equal to those the same series with 0. 05 more missing data 

 would have, if all the missing data were random. 



RECOMMENDATIONS 



1. Examine the nature of missing data in time series 

 of sea-surface temperatures as to the randomness of 

 occurrence in time. Then apply the appropriate results of 

 this report in estimating the variances of regression 

 coefficients and autocorrelation coefficients. 



2. Perform an investigation similar to the present 

 one on the effect of missing data for the regression prob- 

 lem but with several independent variables, namely, time, 

 depth, and geographical location. The dependent variable 

 will be water temperature. 



3. Examine the effect of missing data on the short 

 range prediction of sea-surface temperatures. 



ADMINISTRATIVE INFORMATION 



Work was performed under SR 004 03 01, Task 0586 

 (NEL L40551, formerly L4-5) by a member of the Computer 

 Center. The report covers work from October 1963 to 

 August 1964 and was approved for publication 5 January 1965. 



