Examples of using Linear regression in English and their translations into Spanish
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OBJ_REGRESSION Linear Regression Channel.
Interpreting the results of a multiple linear regression.
Linear Regression in R 2 years ago, 887 views, 0 comments.
An ANOVA, MANOVA and linear regression were performed.
Linear Regression With R 3 years ago, 1,773 views, 0 comments.
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The data was analyzed by applying linear regression along with MANOVA.
The linear regression model was used to verify the association of interest.
So you now know how to implement regularized linear regression.
Linear Regression With R Vor 3 Jahren, 1.773 Aufrufe, 0 Kommentare.
Thus, an independent linear regression is performed for each bank.
Linear regression and neural networks are used to build the models.
Below we make clear the connection between multi-way ANOVA and linear regression.
Thus, two methods, linear regression and the Reason-Normal were used.
To analyze the effect of this VAE on salaries, linear regression was used.
A bivariate and multiple linear regression statistical analysis were carried out.
We perform stratified and multivariate analysis using multiple linear regression.
For statistical analysis, multi pie linear regression and bootstrapping were used.
Statistical analysis consisted of descriptive statistics and simple and multiple linear regression.
Soil moisture estimation using multi linear regression with terraSAR-X data.
Standard curves: linear regression, curve fitting and multipoint standard calibration.
In methodological terms,we employed multiple linear regression and path analysis.
Data were submitted to descriptive statistical analysis and simple and multiple linear regression.
ROC curve of the multiple linear regression values obtained for overall admissions above the mean.
Descriptive analyzes, correlational and multivariate linear regression were performed.
Analytical descriptive, bivariate analysis(t-Student and Chi2)and multivariate linear regression.
The structural model estimates the latent variables by means of simple or multiple linear regression between the latent variables estimated by the measurement model.
The generalized linear model(GLM)is a flexible generalization of ordinary linear regression.
In this course are presented andstudied the basic results of the classical linear regression model: assumptions, estimation by least squares ordinaries, contrasts of hypothesis and prediction.
Diploma of Advanced Studies in Applied Mathematics Universityof Paris IX-Dauphine(France). Subject: Robust Linear Regression.
IBM Netezza Analytics are built directly into Db2 Warehouse on Cloud, with multiple algorithms,including linear regression, decision tree clustering, k-means clustering and ESRI-compatible geospatial extensions.