Examples of using Polynomial regression model in English and their translations into Portuguese
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A polynomial regression model was used for trend analysis according to gender, age, education and region of the country.
In order to evaluate whether there were statistically significant changes in the increase of Doctoral Theses productions, Polynomial Regression models were used.
Polynomial regression models were estimated to analyze trends of mortality in Brazilian regions and in the states of the Central-West Region.
Were used in the spatial distribution of eto the spline interpolation method and polynomial regression model adjusted for latitude, longitude and altitude.
From this relationship, polynomial regression models were estimated, which, in addition to their statistical power, become easy to interpret.
Mortality rates both crude and standardized, age specific by region of residence and sex were calculated andtrends analyzed using polynomial regression models.
Polynomial regression models were used, with the dependent variable y the mortality rate and the independent variable x the year of the study.
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.
Data were grouped into four periods¿1996 to 2000; 2001 to 2004; 2005 to 2008; 2009 to 2012¿to analyze mortality trends by suicide, andstatistically analyzed by polynomial regression models.
The trend analysis was performed with polynomial regression models, based on data from the Brazilian Ministry of Health's Mortality Information System.
The transformation of variable year into variable centralized year(year minus the midpoint of the time series)was required, since, in polynomial regression models, the terms in the equation are often self-correlated.15.
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.
The infant mortality rate was calculated by the direct method andwas analyzed by graphs and polynomial regression models for age groups(early neonatal, late neonatal and post-neonatal) and for groups of avoidable causes of death.
Scatter diagrams were constructed between the rate of hospitalization and the years, in order to identify the function to expressthe relationship between them, and with that, the polynomial order and the polynomial regression models were chosen for the analysis.
The data fitted polynomial regression models since the coefficients of determination R are high for each cultivar and low for overall.
After 56 days of incubation, the ph values were determined in water;this was plotted versus liming and adjusted polynomial regression models, to determine the quantity of lime necessary to achieve ph 5.5; 6.0 and 6.5.
The analysis through polynomial regression models showed a decrease in coverage in the early years, with trends change after 2007, and growth maintenance up to the historical series final years.
Scatter plots of the mortality rates and years of the study were built to identify the function to express the relationship between them and thus choose the polynomial order for the analysis,estimating from this functional relationship the polynomial regression model.
IMR trend was assessed using graphs and polynomial regression models, adjusted for each category of interest general, age group, and group/subgroup of causes.
If, on the one hand, their coverage medians were all below 90%, on the other hand, most of these regions showed increasing trends on Live Births underreporting, exceptfor"Araguaia Xingu" and"Vale do Arinos", which did not present a polynomial regression model capable of describing Sinasc coverage behavior in the historical series.
The technique used to estimate the tendency was the polynomial regression model, whose response variable Yi is the proportion of the indicator, and the explanatory variable Xi is time year of survey.
The dependent variable Y was average spending on admissions by main diagnosis, and the independent variable X was yearsof the trend study. A year-centered variable was used year minus the midpoint of the study period X-2005 because the equation terms of polynomial regression models are correlated to be expressed as an independent variable X, since a deviation from the average reduces the autocorrelation.
The advantages of estimating trends using polynomial regression models include the great statistical power of such models, as well as the fact that they are easy to construct and interpret.
In this study it is presente d analytical procedures data for such situation using manova technology(multivariate var iance analysis), growth multivariate linear models(mlmc) for wood basic de nsity estimation from eucalyptus considering 5 sample heights on tree trunks from ba se to top(sampled disks 0%- base-, 25%, 50% 75%, 100% of tree commercial height)and a lso comparing the precision of polynomial regression model estimators to those obt ained from mlmc technique.
They also did not mention any of these two methods for trend analysis:adjustment of a polynomial function of time polynomial regression models and analysis of the behavior of the series around a point, estimating the trend at that point self-regression models. .
Trend analysis was performed using the polynomial regression model considering the rates of hospitalization as the dependent variable Y and the years as independent variable X. To avoid collinearity between the terms of the regression equation, the variable was centralized, so 2005 was the midpoint.
The results showed higher Hb levels in the After-fortification group in all gestational months,except at the beginning =8th month, without statistically significant difference by the polynomial regression model, a result suggesting absence of effect from the fortification of flours with iron in the Hb level through the pregnancy of Brazilian women.
The trend in hospitalization rates was performed by the polynomial regression model, considering the rates as the dependent variable Y and the years of study as the independent variable X. The X variable was centralized year minus the midpoint of the time series, with 2005 as the midpoint.
Although the regions of"Alto Tapajós","Araguaia Xingu","Médio Norte Mato-Grossense","Noroeste Mato-Grossense","Teles Pires" and"Vale do Arinos" have not presented a polynomial regression model capable of explaining the coverage percentage trend during the analyzed period trend analysis not included in Table 2, all of them showed coverage significant decrease, in comparison with the year 2000 Table 1.