… methods: the method of least squares or the linear … The logistic regression model is designed
specifically for this … the hypothesis generating process from the hypothesis testing process. …

… Data are shown as % for categorical variables and as means … stated in Methods, traditional
significance is not observed for all … First, we developed a logistic regression model that allows …

… the explanatory variables previously found to be significant … outcome variable in the
multivariate analysis, providing a more … two categorical variables Pearson’s chi-square tests were …

… chi-square test and binary logistic regression analysis. In the … like albumin, platelets were
changed into categorical variables. … regression was considered to be a better method to choose …

… compromising the model accuracy by using the correlation method. … testing of the data are
concerned, the exploratory variables are grouped quantitatively and categorically in the model…

… For categorical outcomes (classification), the logic of the procedure is the same, but instead
of performing adjustment using linear regression, we use logistic regression. Our goal is the …

… machine learning algorithms (eg, logistic regression [LR]), … or discrete variables and number
(%) for categorical variables. … Mann-Whitney U test for continuous or discrete variables and …

… So, in this study, both of these techniques are used for the first … that is used to examine the
relationship between a categorical … Normality assumptions were tested by the Shapiro wilk test …

… the machine learning methods to create prediction models. Such … For the categorical variables,
we presented the results as … We employed multinomial logistic regression to analyze the …

… square test for categorical variables and t-test for … models have been developed based on
logistic regression. If the relationship among the data is linearly separable, traditional methods …