地质统计学变差函数人机对话拟合

变差函数的研究在地质统计学中具有十分重要作用，本文运用界面图形图像处理强的Ｃ￣（＋＋）语言实现了界面友好汉化人机对话变差函数的拟合，主要包括管理菜单的生成，实验变差值的求解，变差图的图形显示，标准函数模型的计算及变差函数人机对话求解等部分。最后对比一下回归分析与人机对话拟合结果。     

大坝变形监测数据回归分析中的因子选择

本文对我国大坝变形资料分析中常用的逐步回归分析进行了探讨,指出这种方法通常所选的因子数偏少,其原因来自自变量之间的复共线性的影响。为了克服复共线性对因子筛选的影响,文中对因子筛选提出了若干有益的建议。     

近代回归分析在交通调查分析建模中的应用

本文以出行发生量模型的建立为例,较为系统地讨论了近代回归分析中的自变量选择及回归诊断方法在交通调查分析建模中的应用。分析了在获取调查数据后,如何借助回归自变量选择方法来选择最佳自变量子集,以确定简捷的回归模型;文中应用回归诊断方法,讨论了修正回归模型、探测错误的调查数据的方法,从而为建立简捷、高精度的交通模型打下了基础。最后,作者提出了应用自变量选择及回归诊断方法建立出行发行量模型的一般步骤。     

矿体储量及其计算参数的误差公式和金属储量误差的回归模型

据个旧锡矿开发勘探资料,在计算机上实现不同网度稀空试验的结果、讨论了误差估计问题,提出了矿石品位、矿体厚度、矿体投影面积、矿体体积和金属储量等误差估值公式。以实例建立了不同穿透样锡品位变化系数值的金属储量误差与工程数量的回归模型,并将其应用于金属储量误差估计和工程效果的预测。     

小块体重的多元线性回归方程在锡铁山铅锌矿床储量计算中的应用

从锡铁山铅锌矿床小块体重样品原始测定数据入手，运用数学地质中的回归分析，建立了本矿床的小块体重多元线性回归方程，为矿床储量计算提供了科学参数。利用回归方程法计算小块体重进而可以推广到所有贱金属矿床的储量计算中。     

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares (PLS) regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and in-fluencing factors demonstrate the land use character of rural industrialization and urbaniza-tion in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.     

A spatial perspective for predicting enrollment in a regional pharmacy school

Having the ability to predict enrollment is an important task for any school’s recruiting team. The purpose of this study was to identify significant factors that can be used to predict the spatial distribution of enrollments. As a case study, we used East Tennessee State University (ETSU) pharmacy school, a regional pharmacy school located in the Appalachian Mountains. Through the application of a negative binomial regression model, we found that the most important indicators of enrollment volume for the ETSU pharmacy school were Euclidean distance, probability (based on competing pharmacy schools’ prestige, driving distance between schools and home and tuition costs), and the natural barrier of the Appalachian Mountains. Using these factors, together with other control variables, we successfully predicted the spatial distribution of enrollments for ETSU pharmacy school. Interestingly, gender also surfaced as a variable for predicting the pharmacy school’s enrollment. We found female students are more sensitive to the geographic proximity of home to school.     

Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield,Finland

Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.