{"title":"Assessment of lunar surface materials","authors":"M. Yaylı, S. Y. Kandemir, Y. C. Toklu","doi":"10.1109/RAST.2015.7208306","DOIUrl":null,"url":null,"abstract":"Lunar exploration is very important in the world. Investigation of lunar surface materials such as Agglutinitic Glass (A), Morris Is/FeO (M), LSCC Is/FeO (L), Total Pyx (T) and Plagioclase (P) is increase last years. The prediction of lunar surface materials including A, M, L, T and P is significant. In this study, the A (one of the important materials in the moon) were predicted by applying the linear regression analysis model. The R2 and R2adj are calculated that 81.20% and 75.83%, respectively. Finally, it was concluded that A can reliably be predicted by using the linear regression analysis model.","PeriodicalId":282476,"journal":{"name":"2015 7th International Conference on Recent Advances in Space Technologies (RAST)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Conference on Recent Advances in Space Technologies (RAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAST.2015.7208306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Lunar exploration is very important in the world. Investigation of lunar surface materials such as Agglutinitic Glass (A), Morris Is/FeO (M), LSCC Is/FeO (L), Total Pyx (T) and Plagioclase (P) is increase last years. The prediction of lunar surface materials including A, M, L, T and P is significant. In this study, the A (one of the important materials in the moon) were predicted by applying the linear regression analysis model. The R2 and R2adj are calculated that 81.20% and 75.83%, respectively. Finally, it was concluded that A can reliably be predicted by using the linear regression analysis model.
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