Examples of using Linear regression analysis in English and their translations into Spanish
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Results of multiple linear regression analysis for overall admissions.
The data were analyzed by probabilistic linear regression analysis.
Multiple linear regression analysis difference was significant(p<0.01).
Three models were constructed using linear regression analysis stepwise.
We use linear regression analysis with the successive step method.
The data were processed by performing simple linear regression analysis.
R2 determined by linear regression analysis adjusted by sex and age.
And 2 variables statistics,has only a simple linear regression analysis.
In the linear regression analysis, low FT3 was associated with IL-6(p=.002).
For the statistical treatment, a multiple linear regression analysis was employed.
Build linear regression, analysis of variance(ANOVA) and analysis of covariance(ANCOVA) models.
Descriptive analysis and, simple and multiple linear regression analysis were performed. Results.
A linear regression analysis applied to the 2006 sample allows us to estimate the predictors of support for the system in Bolivia for this year.
For checking the mediation, a linear regression analysis using bootstrapping was used.
Linear regression analysis provided a means for taking into account individual characteristics relevant to the determination of compensation awards. Second Report, paras. 33-34.
Most of the procedures used in linear regression analysis are still valid in the logistic regression. .
A linear regression analysis to identify the variables behind the feeling of insecurity(see Table VI-4, Appendix B) shows that gender and educational level are significant predictors of the feeling of insecurity, as can be seen in Figure VI-6.
He quantified this trend, and in doing so invented linear regression analysis, thus laying the groundwork for much of modern statistical modelling.
Given the nature of the data, i.e., a quantitative dependent variable(the amount claimed) and a mix of quantitative(e.g., age) and qualitative(e.g.,marital status) potential explanatory factors, we were of the opinion that linear regression analysis was the best suited standard statistical technique for the purposes.
To achieve the objective, linear regression analysis and pathway analysis were performed.
In a simple linear regression analysis, correlation is used to predict the value of the dependent variable based on the value of the independent variable.
The right panel of Figure 2 shows the QuAC score predicted by a linear regression analysis including interviewer field day, a squared term to capture nonlinearities by interviewer, and country fixed effects.
In Figure I.1246 we use linear regression analysis to assess the determinants of personal income among respondents who told us that they had a job at the time of the interview.47 Gender and location of residence are the two most consequential predictors of personal income.
In this tutorial series on linear regression analysis and modeling, will start with the general definition or topology of a regression model.
After running a linear regression analysis for each of the two dependent variables that include a person's age, sex, educational level and income as statistical controls, the differences between the ethnic majority and minority are evident in only a handful of countries.
The basic assumption underlying any linear regression analysis is that the dependent variable can be expressed as a linear combination(i.e., a weighted sum) of a given set of explanatory factors.
The results of the linear regression analysis indicate that, as with support for democracy, the political model has the best goodness of fit.
Stated simply, multivariate linear regression analysis involves a variable to be explained- the dependent variable- and additional variables relevant to explaining the dependent variable.
In Figure 48 and Figure 49,we use linear regression analysis to examine the personal characteristics and experiences that lead citizens to report high internal efficacy and strong perceptions of representation.
To do that I use correlation and linear regression analyses.