This paper reviews several common challenges encountered in statistical analyses of epidemiological data for epidemiologists. We focus on the application of linear regression, multivariate logistic regression, and log-linear modeling to epidemiological data. Specific topics include: (a) deletion of outliers, (b) heteroscedasticity in linear regression, (c) limitations of principal component ana...

logistic regression (lr) was used to model urban growth between the years 1987 and 2001 in gorgan city, north east of iran. three groups of variables including economic-social, land use and biophysical variables were used in the modeling practice. using covariance of the independent variables, distance to administrative and sporting centers plus distance to cities were removed. roc (relative op...

abstract: despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. therefore, in most of these research the predictive variables were chosen randomly and accordin...

The recent recolonization of Central Europe by the European gray wolf (Canis lupus) provides an opportunity to study the dynamics of parasite transmission for cases when a definitive host returns after a phase of local extinction. We investigated whether a newly established wolf population increased the prevalence of those parasites in ungulate intermediate hosts representing wolf prey, whether...

PURPOSE The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. METHODS This study included 139 solid masses from 139 patients who underwent a ultrasonogr...

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstra...

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...

Roads remove habitat, alter adjacent areas, and interrupt and redirect ecological flows. They subdivide wildlife populations, foster invasive species spread, change the hydrologic network, and increase human use of adjacent areas. At broad scales, these impacts cumulate and define landscape patterns. The goal of this study was to improve our understanding of the dynamics of road networks over t...