Examples of using Linear regression model in English and their translations into Greek
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Colloquial
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Official
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Medicine
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Ecclesiastic
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Financial
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Official/political
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Computer
Consider the simple linear regression model.
Linear regression models for insurance data.
The main tool is the linear regression model.
The combination of swept or unswept matrices provides an alternative method for estimating linear regression models.
This course examines applications of linear regression models to Business Administration.
The course also includes an extensive discussion of the multiple linear regression model.
The following are the major assumptions made by standard linear regression models with standard estimation techniques(e.g. ordinary least squares).
POLYGLOT can detect students' frustration by using the linear regression model.
This comes directly from the beta coefficient of the linear regression model that relates the return on the investment to the return on all risky assets.
A basic tool for econometrics is the multiple linear regression model.
Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, the response variables and their relationship.
Is still assumed,with a matrix B replacing the vector β of the classical linear regression model.
Classical linear regression models make predictions about continuous variables, where numbers have a logical order(such as age or number of wins on a champion).
Other regression methods besides the simple ordinary least squares(OLS)also exist(see linear regression model).
The study of econometrics covers linear and dynamic linear regression models, a wide variety of hypothesis tests, and techniques for estimating systems of equations.
This PhD thesis deals with topics relating to the problems of spatial dependence andspatial heterogeneity in the linear regression model.
Errors-in-variables models(or"measurement error models")extend the traditional linear regression model to allow the predictor variables X to be observed with error.
Baseline seizure frequency andTN-Cmin were both significant factors(α=0.05) in predicting the absolute seizure frequency in the linear regression model.
Linear regression models were created using the concentrations of TNF-α, IL-6 as the dependent variables, and the variances of cancer-related fatigue as independent variables.
Demonstrates how to use Partial Least Squares to fit a(multiple and multivariate) linear regression model from high-dimensionality data.
Also fits a multiple linear regression model for comparison purposes, and performs chi-square tests and computes Wald's statistics for the logistic regression coefficients.
In contrast, the marginal effect of xj on y can be assessed using a correlation coefficient or simple linear regression model relating only xj to y; this effect is the total derivative of y with respect to xj.
If the dependent variable was quantitative and>2 independent variables were significant at the 0.2(p< 0.2) level in bivariate analysis, multivariate linear regression was applied,using the backward stepwise linear regression model.
For example, weighted least squares is a method for estimating linear regression models when the response variables may have different error variances, possibly with correlated errors.
A fitted linear regression model can be used to identify the relationship between a single predictor variable Xj and the response variable y when all the other predictor variables in the model are“help fixed”.
Given a data set{ y i, x i 1,…, x i p} i= 1 n{\displaystyle\{y_{i},\, x_{i1},\ldots, x_{ ip}\}_{ i=1}^{ n}} of n statistical units,a linear regression model assumes that the relationship between the dependent variable y and the p-vector of regressors x is linear.
Econometrics. This course teaches the theory of the Linear Regression Model(least squares, vector autoregressive models, linear models with a unit root, cointegration) as well as topics such as Rational Expectations.
In statistics and numerical analysis,the problem of numerical methods for linear least squares is an important one because linear regression models are one of the most important types of model, both as formal statistical models and for exploration of data sets.
This outcome was consistent with the results of a linear regression model predicting the log of absolute seizure frequency during the maintenance period of the core phase, which indicated that for a 2-fold increase in TN-Cmin there was a statistically significant 28% reduction(95% CI: 12%, 42%) in absolute seizure frequency.
The numerical methods for linear least squares are important because linear regression models are among the most important types of model, both as formal statistical models and for exploration of data-sets.