Examples of using Linear regression models in English and their translations into Portuguese
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There were five multivariate linear regression models.
Multiple linear regression models were applied Y a+ b1x1+ b2x2+…+ bnxn.
Similar phenomenon occurred with the five linear regression models.
The multiple linear regression models initially included all variables with p.
The statistical analysis employed adjusted linear regression models.
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In this sense, simple linear regression models were adjusted for each temporal series.
The effect of co-variables was analyzed by linear regression models α 5.
In all multiple linear regression models, the use of medication was related to a lower QOL.
To evaluate the effect of polymorphism, linear regression models were used.
Linear regression models were estimated with logistic regression models. .
This shows that the linear regression models were adequate.
Analysis of the tendency of the indicators over time was carried out using simple linear regression models.
In this thesis, we consider linear regression models to deal with these three characteristics.
A significance level of 5% was used for the linear regression models.
The assumptions of linear regression models were analyzed graphically and through statistical tests.
Adjustment was performed using four sequential multiple linear regression models stratified by sex.
Multiple linear regression models estimated by the method of least squares and obtained through the stepwise method.
To predict patient satisfaction, linear regression models were built.
Linear regression models identified the differences between the energy percentage of macronutrients and the co-variables of the study.
The selection of independent variables for the multiple linear regression models was performed according to a stepwise model. .
Multiple linear regression models were used considering a selected sample of size 28 based on a 7x4 factorial design.
Pseudo-R2 is a global measure of the explained variation,analogous to the coefficient of determination used in linear regression models.
After adjusting the multiple linear regression models, with a 95% confidence interval, the p- values were estimated.
These corrections are available in the literature for the likelihood ratio and score tests in symmetric linear regression models and dispersion models. .
The linear regression models adjusted for sex, age, overall fat and central obesity indicated that RHR was associated with SBP p.
Statistics and Data Science II- Estimate and interpret multivariate linear regression models, including extensions appropriate for causal inference.
Linear regression models were developed to predict the values of the normal curves measured according to age, weight and height.
XY data can be modelled using a toolbox of linear regression models, nonlinear regression models, interpolation, or splines.
Simple linear regression models were used to assess temporal variation of the continuous variables in the years of monitoring.
Since the analysis showed both dependent variables did nothave a normal distribution, they underwent logarithmic conversion to meet the assumed normality required in linear regression models.