Examples of using Simple linear regression model in English and their translations into Portuguese
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To estimate the trend, the simple linear regression model was used.
Simple linear regression model was used to analyze and compare mortality trends.
Variables were compared according to the possession ornot of health insurance using simple linear regression model.
In this sense, simple linear regression models were adjusted for each temporal series.
Coefficient(b) of the straight-line gradient, assuming that the time trend of the measurement(4)follows the simple linear regression model;
If we have a simple linear regression model, we have some equation like Y=A1X1+A2X2. Plus.
In the attempt to evaluate the efficiency of this method,several researchers used a simple linear regression model between the components of energy balance.
The simple linear regression models were tested at the second and third orders and with exponential functions.
The inferential analysis took into consideration the simple linear regression model in order to study the association between the VRA and ASSR techniques.
A simple linear regression model was used to check the trends of male deaths during the study period.
In this sense, this paper analyzes the preparation of the results for this external evaluation andassessment of the results themselves, using a simple linear regression model.
A simple linear regression model was used to evaluate time-trends of mortality rates.
The estimation of the withdrawal period is normally obtained by the fitting of a simple linear regression model, followed by the calculation of a tolerance limit.
Simple linear regression models were used to assess temporal variation of the continuous variables in the years of monitoring.
The prevalence of overweight was estimated using the simple linear regression model and the calculation of the prevalence ratio according to gender, education level.
Simple linear regression models were used for obtaining beta coefficients, by analyzing mortality as a dependent variable and the year as independent variable.
Although the OLS article argues that it would be more appropriate to run a quadratic regression for this data, the simple linear regression model is applied here instead.
Initially, the simple linear regression model was tested 0+?1X and later, models of higher order, second degree 1X+?2X and third degree 2X+?3X.
The results of the estimation of net radiation were employed in the determination of evapotranspiration andflows of latent and sensibleheat by means of a simple linear regression model developed with data measured on the surface experiment of large scale biosphere- atm.
Initially, we tested the simple linear regression model 0+?1X and later, we tested the models of second degree ?0+? 1X+? 2X and third degree Y?0+? 1X+? 2X+?3X.
ASSESSMENT OF THE QUALITy OF THE SUM OF GRACILIS+ SEMITENDINOSUS AS A PREDICTOR OF GRAFT DIAMETER:To evaluate the sum of gracilis+ semitendinosus as a predictor of graft diameter, a simple linear regression model was adjusted, and the sum was considered as an explanatory variable independent and the diameter of the graft as the response variable dependent.
Initially, we tested the simple linear regression model 0+?1 X; and later models of higher order, the second 1X+?2X or third degree 2X+?3X.
To assess the correlation between interventricular septum areas with gestational age GA, scatter diagrams were determined by obtaining Pearson's correlation index r, and the equation adjustment was given by the coefficient of determination R2. For the construction of reference intervals for the interventricular septum areas as a function of gestational age,we followed the simple linear regression model using the Altman's method with a significance level of p< 0.05.
Simple linear regression models were adjusted for all plants sampled, the height the dependent variable and time in days after the first review as an independent variable.
When comparing postoperative serum creatinine andCBP time by means of a simple linear regression model b=0.0037; t=3.1797; p=0.0022; R2 adjusted 11.10%, only 11% of the dependent variable creatinine is explained by the independent variable CPB.
Simple linear regression models were estimated, defined as Y=?+? year,? being the mean coefficient in the analyzed period and? the mean increment increase or decrease in the period.
The residual analysis showed that the use of simple linear regression models was appropriate due to normal distribution, homoscedasticity and absence of outliers.
Afterwards, a simple linear regression model was applied, and the outcome- dependent variable- was the percentage of adults who declared their health status to be"poor/very poor.
To look at the trend of death due to CVD we used a simple linear regression model using the adjusted mortality rate as the dependent variable, and the year of death as the independent variable.
A simple linear regression model was used to assess the relationship between previous atherosclerosis and intimal growth as well as the relationship between intimal and vessel growth after 1 year, using Pearson correlation coefficients.