black zira (bunium persicum) is a medicinal plant and spice, naturally grows in iran. the aim of this study was to investigate the combined effects of different concentrations of bunium persicum essential oil (eo; including 0, 0.08, 0.16 and 0.24%), three incubation temperatures (15, 25 and 35˚c), three levels of ph (5, 6 and 7 adjusted by hydrochloric acid), and three inoculum size (102, 103 a...

Given a population described by p explanatory and one dependent categorical variables, we assume that the dependent variable defines a partition of the population into g groups. Discriminant Analysis studies the relation between the p explanatory variables and the dependent variable finding the subset of variables that has the most predictive power. Generally, in categorical discriminant analys...

Abstract— In some real case problems, a relationship between a response variable and one or more explanatory variables called as profile is desirable to be monitored over time instead of the response variable itself. There are many techniques in the literature for monitoring the profiles. A special case of the profiles is where the response variable follows a binomial distribution known as Logi...

True spreads are not directly observable and represent the continuous demand and supply schedule for stock liquidity by heterogeneously informed market participants. Observed spreads are true spreads quantized by minimum market tick size. A regression model of true spreads is developed using spread data from a pure limit order electronic exchange. True spreads are modelled as a continuous posit...

purpose of this section is to show how many observations are actually analyzed in multivariate analyses, such as multiple linear regression analysis or multiple logistic regression analysis, when there are different numbers of missing values in each explanatory variable. Let’s perform a multiple linear regression using the following hypothetical data (Table 1). In this data, explanatory variabl...

Although regression analysis has a great history, we consider that it has always continued being confused. For example, the fundamental terms in regression analysis (e.g., ”regression”, ”least-squares method”, ”explanatory variable”, ”response variable”, etc.) seem to be historically conventional, that is, these words do not express the essence of regression analysis. Recently, we proposed quan...

Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameterizes the covariance matrix of a multivariate response vector as a parsimonious quadratic function of explanatory variables. The approach can be seen as analogous to the mean regression model, and ha...

This paper presents a Bayesian analysis of a time series of counts to assess its dependence on an explanatory variable. The time series represented is the incidence of the infectious disease ESBL-producing Klebsiella pneumoniae in an Australian hospital and the explanatory variable is the number of grams of antibiotic (third generation) cephalosporin used during that time. We demonstrate that t...

Biomedical studies often measure variables with error. Examples in the literature include investigation of the association between the change in some outcome variable (blood pressure, cholesterol level etc.) and a set of explanatory variables (age, smoking status etc.). Typically, one fits linear regression models to investigate such associations. With the outcome variable measured with error, ...

A MIDAS regression involves a dependent variable observed at a low frequency and independent variables observed at a higher frequency. This paper relates a true high frequency data generating process, where also the dependent variable is observed (hypothetically) at the high frequency, with a MIDAS regression. It is shown that a correctly specified MIDAS regression usually includes lagged depen...