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Analyses were done with each predictor variable separately and then in a multiple analysis with all three variables included.
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Multiple analysis was performed with LIMMA package in R computing environment (Smyth 2004).
There are multiple analysis techniques that deal with such problems, more specifically with system capacity analysis.
The combined data sets were first analyzed collectively with a multiple analysis of variance (MANOVA).
The Tukey test was applied for multiple comparison analysis with P < 0.05, indicating significant statistical difference.
To accomplish this, we conducted a multiple regression analysis with mathematics performance as the criterion.
We conducted a stepwise multiple regression analysis with normalized performance on our three spatial measures.
A multiple factor analysis with the logistic regression method was carried out to analyze related factors of PSD.
Table 6 shows the results from the multiple regression analysis with profitability as dependent variable and the outsourcing strategies as explanatory variables.
The experimental data were fitted to quadratic response surface models using multiple regression analysis with high coefficient of determination value (R) of 0.9906.
The obtained experimental data were fitted to a second-order polynomial equation using multiple regression analysis with high coefficient of determination value (R2) of 0.994.
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