Examples of using Regression model in English and their translations into Ukrainian
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Linear regression model.
Regression models involve the following parameters and variables.
Prediction with regression models.
Were calculated regression models of the success of competitive activity on the level of severity of the main.
Multimodality in GARCH regression models.
Geo-economic regression model post-Soviet countries.
Forecasting with Dynamic Regression Models.
The traditional negative binomial regression model, commonly known as NB2, is based on the Poisson-gamma mixture distribution.
What is a simple linear regression model?
Next, the most important regression models used in medical research are introduced.
Decision tree ensemble-based Regression Models 6.
Regression modelling can be performed using maximum likelihood estimation methods by using Stata or R.[4][5].
Quarterly data from 1948-2016 is taken for the calculation. Regression model is the following.
Title: Multifactor non-linear regression model with feedbacks- regional climate simulation.
To estimate actual andpotential crop yield using EO-based regression models for Ukraine;
The estimation of acceptability of regression modeling method for bank lending controlling has been made.
To begin with, Russia still lacks a data set for formingdynamic series that would yield efficient regression models.
The author presents the multiple linear regression model of bank consolidated financial statements quality.
As regression model was selected for optimum scaling Regression(Regression with Optimal scaling(CATREG))(implemented in SPSS 17.0).
These include logistic regression, Poisson regression, analysis of'event history' data,and the Cox proportional hazards regression model.
A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.
The Statistical Toolbox was developed for dose responsible analysis andestimation in the regression models with uncertainties in the regressor(independent variable).
Regression models involve the following parameters and variables: The unknown parameters, denoted as β{\displaystyle\beta}, which may represent a scalar or a vector.
Responsibility in the project: development and application of the regression modelling approach to geomagnetic forecast, administrative issues(Point of Contact for the team).
The problems of regression models formation at the stages of experiment design, preliminary and final model specification, model identification stage and evaluation of result model quality were formulated.
Cook's distance D i{\displaystyle D_{i}} of observation i(∀ i= 1,…, n){\displaystyle i\;(\forall i=1,\dots,n)} is defined as the sum ofall the changes in the regression model when observation i{\displaystyle i} is removed from it[5].
That best fits the data in some sense, and the regression model when applied to the data can be viewed as an overdetermined system in β{\displaystyle\beta}.
The dependent and independent variables in a regression model do not need to be normally distributed by themselves--only the prediction errors need to be normally distributed.
We performed stepwise regression analysis andobtained statistically significant regression model(p< 0.01). According to it, T(- 786) C polymorfhism of eNOS gene with presence of C allele in patient's genotype was responsible for 3.0% of EDV increase(р= 0.001) and for 3.8% of ESV increase(р= 0.005).
Forecasting with regressions models.