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Statistical analyses provide a valuable method for establishing initially the existence (or lack of existence) of a relationship between diverse data sets. Statistical methods also allow one to make quantitative assessments of the strengths of observed relationships. This paper reviews the essential techniques and underlying statistical bases for the use of correlative methods in solar wind-magnetosphere coupling studies. Techniques of visual correlation and time-lagged linear cross-correlation analysis are emphasized, but methods of multiple regression, superposed epoch analysis, and linear prediction filtering are also described briefly. The long history of correlation analysis in the area of solar wind-magnetosphere coupling is reviewed with the assessments organized according to data averaging time scales (minutes to years). It is concluded that these statistical methods can be very useful first steps, but that case studies and various advanced analysis methods should be employed to understand fully the average response of the magnetosphere to solar wind input. It is clear that many workers have not always recognized underlying assumptions of statistical methods and thus the significance of correlation results can be in doubt. Long-term averages (greater than or equal to 1 hour) can reveal gross relationships, but only when dealing with high-resolution data (1 to 10 …

Mixtures of hydrogen isotopes, primarily deuterium (D/sub 2/), protium-deuterium (HD), and protium (H/sub 2/) must be pumped by the vacuum system in the Mirror Fusion Test Facility at Lawrence Livermore National Laboratory. In this study, we used argon as an adsorbent for cryopumping these isotopes at 4.2 K and found that deuterium will displace already adsorbed protium. Thus, when we pump mixtures of the two, sufficient argon must be supplied to adsorb both species. We also found that without argon, deuterium will cryptrap protium in accord with Raoult's law.