Exemplos de uso de Polynomial regression em Inglês e suas traduções para o Português
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The study calculated leprosy indicators andanalyzed time trends using polynomial regression.
The effect of N levels was measured by polynomial regression analysis tested up to cubic degree.
The polynomial regression, however, did not show significant effect for the fortification of flours with iron.
We used SPSS software version 15.0 in the preparation of polynomial regression and scatterplots.
Polynomial regression was performed with averages of∆E00 as a function of thickness of dentin shade cylinders;
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Were calculated mortality rates andtrend analysis conducted using polynomial regression models.
A polynomial regression model was used for trend analysis according to gender, age, education and region of the country.
The statistical analysis was performed by analysis of variance of the treatments,followed by tukey test and the polynomial regression.
Polynomial regression models were estimated to analyze trends of mortality in Brazilian regions and in the states of the Central-West Region.
Data was analysed by F test(ANOVA) and polynomial regression for the osmotic potential for each parameter evaluated.
Polynomial regression models were used, with the dependent variable y the mortality rate and the independent variable x the year of the study.
It were compared the linear mixed models of third degree polynomial regression, spline and piecewise regression, both with a single point of change in the average time of pups ey.
In order to evaluate whether there were statistically significant changes in the increase of Doctoral Theses productions, Polynomial Regression models were used.
And, finally, there is the use of polynomial regression as one more limitation, since it is not possible to control the serial autocorrelation.
Mortality rates both crude and standardized, age specific by region of residence and sex were calculated andtrends analyzed using polynomial regression models.
From this relationship, polynomial regression models were estimated, which, in addition to their statistical power, become easy to interpret.
Were used in the spatial distribution of eto the spline interpolation method and polynomial regression model adjusted for latitude, longitude and altitude.
The polynomial regression of third degree showed that, for males, the decline has slowed since 2000, with further acceleration from 2008 on Figure 1B.
To adjust the Hb curves followingthe gestational month and the independent variables, polynomial regression models were built, being the dependent variable the level of Hb.
The polynomial regression model was considered capable of describing the relation between the dependent and independent variables when the p-value was.
In order to evaluate the correlation between MCA-PSV and gestational age, the Pearson's(r) correlation coefficient andscatter plots were utilized, and models of polynomial regression were created.
The obtained data were submitted to polynomial regression in order to evaluate the birefringent areas in light of the different treatments and treatment lengths.
These models have the objective of finding the best curve to adjust the data, relating the outcome variable, HIS rate, and mortality Y,with the independent variable year of study X. We considered the following models of polynomial regression.
After incubation, polynomial regression were adjusted from ph data of the mixtures of the soil- casio3 and soil- caco3 as a function of the doses of each material applied.
To estimate tendency,we used analysis via models of polynomial regression given the large statistical power and ease of elaboration and interpretation with this model.
Polynomial regression was utilized for evaluating the correlation between YS volume and gestational age, with adjustments by the determination coefficient R2.
The trend analysis was performed with polynomial regression models, based on data from the Brazilian Ministry of Health's Mortality Information System.
Polynomial regression was utilized for evaluating the correlation between YS volume and gestational age, with adjustments by the determination coefficient R. The significance level of 0.05 p 0.05 was utilized for such analyses.
IMR trend was assessed using graphs and polynomial regression models, adjusted for each category of interest general, age group, and group/subgroup of causes.
We performed polynomial regression models. The best adjustment with the exponential equation was: interventricular septum area 0.125 x IG+ 0.043, with R 0.65 Figure 3.