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The GWR showed improved model accuracy (~64%) and a similar ranking of variables compared to the multiple regression; however, soil type did not significantly contribute to this model.
With multiple regression, however, both the number of GRs and KD of the receptor were significant factors contributing to the relative decrease in DAS.
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By multiple regression analysis, however, only IL-6 was independently associated with C-reactive protein concentrations.
Chewing more than 33,000 pieces will lead to significant RABL and dose response confirmed by multiple regression analysis; however, mean PI and age were stronger contributing factors.
Ordered probit analysis is analogue to multiple regression analysis; however, in the ordered probit the dependent variable is scored on an ordinal scale, whereas in a multiple regression the dependent variable is scored on an interval scale.
For example, it would be relevant to have also included body mass index in the multiple regression analysis, however, this data was unavailable given that height is not routinely measured in the adult clinic.
An alternative model would be multiple linear regression, however as this assumes a linear relationship between the response and predictors, it may be unsuitable.
By multiple logistic regression, however, only age was a significant predictor of impaired EF (P < 0.001).
Multiple logistic regression, however, showed the opposite direction after adjusting for age, gender, BMI, and family history of gallstone disease.
Multiple regression analysis shows, however, that the number (P = 0.006) and total length of transcribed sequences (P = 0.035) in a region are more relevant than OTA (P > 0.4) predicting recombination rates at the 100-kb scale.
However, multiple regression analyses do not reveal the level of phonological sensitivity that children need in order to understand alphabetic reading instruction.
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CEO of Professional Science Editing for Scientists @ prosciediting.com