To predict honey production as a
response variable, several potential predictors in the survey were recorded such as age of enterprise, province of enterprise (Agri, Kars and Erzurum), enterprise's educational level, membership status of enterprise to association of beekeepers (member and nonmember), other activities except for beekeeping (yes and no), number of full beehives, bee race (Caucasian, Carniolan, Italian and Crossbred), and frequency of changing queen bee.
Based on the variety of experimental procedures reported or producing MOF-199 and the absence of information in literature concerning the importance of understanding the significant experimental conditions for MOF-199 production, the aim of this work was to apply a [2.sup.3] factorial design to evaluate the influence of [Cu.sup.2+] salts counterions, acetate (OAc) and chloride ([Cl.sup.-]), and the synthesis parameters (time, temperature and metal concentration) on the reaction yield (
response variable) of MOF-199.The obtained compounds were characterized by infrared vibrational spectroscopy (FT-IR spectroscopy), thermogravimetric analysis (TGA) and powder X-ray diffraction (XRD).
Statistical models are usually expressed as linear models with the overall mean of the
response variable, fixed or random variables that are known to influence the
response variable, and unexplained experimental random error.
Feature extraction using PCA ignores the
response variable and its equivalence.
Under the assumption of heteroscedasticity of the variances, it is considered that [[epsilon].sub.ij] ~ N (0, [[sigma].sup.2.sub.i]) where [[sigma].sup.2.sub.i] = [[sigma].sup.2.sub.i] [x.sup.[lambda].sub.j] is the parameter that characterizes the variance of the
response variable in the jth explanatory variable.
For this purpose, we constructed a
response variable and explanatory variables for the study area of our study and used various machine learning models such as decision trees, bagging, random forests, and boosting to develop prediction models for heavy rain damage based on big data.
Truncated spline approach is used as a solution to solve the problem of spatial data analysis modeling; that is, the relationship between the
response variable and the predictor variable does not follow a certain pattern and there is a changing pattern in certain subintervals.
One such technique is single target regression trees (STRT) conducting binary recursive partitioning producing a set of rules and a regression model to predict a single
response variable [27, 28].
where M is the membership value of
response variable being analyzed, [X.sub.i] is the data value of the
response variable being analyzed, [X.sub.max] is the maximum value of the data column, and [X.sub.min] is the minimum value of the data column.
This study considered crash severity as the
response variable for the model assigning a binary value of 0 or 1.