rate 3.3 Multiplelinearregression analysis To establish a quantitative model between the browning area and multiple physical and chemical indicators, the principal component scores obtained were used as input variables in the MLR model, with the
A simple and rapid capillary electrophoretic procedure for analysis of matrine and oxymatrine in Kushen medicinal preparations has been developed and optimized. Orthogonal design was used to optimize the separation and detection conditions for the two active components. Phosphate concentration, applied potential, organic modifier content, and buffer pH were selected as variable conditions. The optimized background electrolyte contained 70 mM sodium dihydrogen phosphate and 30% acetonitrile at pH 5.5; the separation potential was 20 kV. Each analysis was complete within 5 min. Regression equations revealed linear relationships (r > 0.999) between peak area and amount for each component. The detection limits were 1.29 μg mL−1 for matrine and 1.48 μg mL−1 for oxymatrine. The levels of the two active compounds in two kinds of traditional Chinese medicinal preparation were easily determined with recoveries of 96.57–106.26%. In addition, multiple linear regression and a non-linear model using a radial basis function neural network approach were constructed for prediction of the migration time of oxymatrine. The predicted results were in good agreement with the experimental values, indicating that a radial basis function neural network is a potential means of prediction of separation time in capillary electrophoresis.
when direct experience is lacking ( Schnauber & Meltzer, 2005 ).
The study employs multiplelinearregressions, using SPSS in order to test whether or not media consumption predicts the
standard deviations for continuous variables and percentages for categorical variables). Trends and associations among variables were evaluated using Pearson’s correlation analyses. Stepwise multiplelinearregressions were performed to determine the risk
dependent variable, whereas multiplelinearregression analysis was used for CAT as it is a unidimensional instrument with a single dependent variable. Maximum likelihood estimation with robust standard error was used to calculate regression coefficients
compare proportions between the groups. The correlation of IAT scores with participants’ ages, impulsivity scores, 2D:4D ratios, and Internet use-related variables was analyzed using Pearson’s correlation tests. The multiplelinearregression analyses were
the point in question.
To determine which of the selected risk factors contributes to the development of problematic game behavior in the most notable manner, a multiplelinearregression analysis was used. The
. Based on results of univariate correlation analyses of all study participants, variables likely to influence sexual compulsivity were integrated simultaneously in a standard multiplelinearregression model (assumptions for the analysis were met, all
above .80 indicates a high effect size ( Cohen, 1992 ). Second, correlation coefficients were calculated among all the variables by using Pearson’s r in the group of pathological gamblers. Third, stepwise multiplelinearregression analyses were