First we utilize the logistic regression model to distinguish the most important symptoms of influenza infection by fitting a logistic regression model to the binary influenza infection outcome in the sample, using binary indicators of the influenza-like symptoms as predictors.
Equation (1) presents the regression model I utilize to the estimate the persistent effects of leaving school in an economic downturn on access to employer-sponsored health insurance.
The regression model, the response surface model, is utilized to create more data.
In the second step, we utilized linear regression models to examine the specific association of different types of childhood maltreatment and psychopathology.
Localization analyses utilized a multiple regression model that included independent (i.e., uncorrelated) regressors modeling anticipation of gamble outcomes: (i) high versus low variance, (ii) skewed versus symmetric, and (iii) positive versus negative skew.
To assess for an association between in-hospital mortality, hemodialysis requirement, ventilator requirement and inotrope requirement we utilized a logistic regression model that adjusted for gender, age, urgency of procedure and American Society of Anesthesiologists (ASA) score.
In particular, we utilized a spatial regression model to account for autocorrelation in AIV cases among counties that are close together geographically.
In addition, we utilized a multilinear regression model that included age, presence of pain, stool toxin, lactoferrin, serum albumin, WBC count, and stools per day to investigate predictors of severe CDI.
The MGA is based on the algorithm of the MG and utilizes logistic regression models of the GC content and the di-codon frequencies (di-codon models).
We did not utilize a regression model for NDE1 DISC1 analyses, as NDE1 genotype did not have a significant main effect in initial tests.
