Examples of using Regression models in English and their translations into Hungarian
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Separate regression models were developed for boys and girls.
Survival models can be usefully viewed as ordinary regression models in which the response variable is time.
Air pollution concentrations of nitrogen oxides andparticulate matter were estimated at the home addresses using land-use regression models.
Survival models may be viewed as normal regression models where the response variable is time.
Logistic regression models, they can be used to estimate the probabilities of things like lightning striking, and I can apply them to these circumstances.
In this course, you will explore regularized linear regression models for the task of prediction and feature selection.
I could have made some sexy regression models, could have gotten permission from Admin to get that deluxe bagel and lox spread we sometimes get;
During their fivedays in the classroom students review the basic theory of machine learning, regression models and classification problems.
You can define your own regression models, or choose from more than 350 predefined 2D/3D models. .
A broad spectrum of methods has been used so far, for example, the nearest neighbour based models, ARMA, ARIMA models, artificial neural networks, regression models, moving averages, exponential refinement etc.
Four specific classification and regression models were created: one trained only on patient costs, age, sex, and median zip code income;
Because the(natural log of the) odds of a record is estimated as a linear function of the explanatory variables, the estimated odds ratio for 70-year-olds and 60-year-olds associated with the type oftreatment would be the same in logistic regression models where the outcome is associated with drug and age, although the relative risk might be significantly different.
The researchers used logistic regression models to compare the risks of mental and physical health problems of children who had and had not spent time in foster care.
The automated calculationprovides role in the analysis of econometric models and regression models, heteroscedasticity, serial correlation, modelling or binary regressors.
In the second step, we ran OLS regression models with stepwise method, including all of the variables(except of course the contrast), and defined the preliminary function.
Coursework in the MANA finance andaccounting concentration explores topics including regression models, the practical application of financial theory, and accounting analytics through a combination of class formats, including real-world, team-based projects with sponsoring organizations.
Linear regression model was applied.
In order to answer this question, we built a multiple linear regression model.
The single equation regression model.
A number of models can be choosen to fit a regression model, e.g.
Suppose that we have a linear multiple regression model of the following form.
Figure 3: Results of the logit regression model.
In that case these redundantX columns should be omitted from the regression model.
We will demonstrate through practical examples how to build a regression model for the prediction of throughput time, how to cluster the orders for production scheduling or, how to predict qualitative parameters in advance.
A pre-specified sensitivity analysis using the negative binomial regression model treatment showed a statistically significant difference of -14.2%(rate ratio: 0.86; 95% CI: 0.74 to 0.99).
The process shows, using the Wine dataset, how a regression model can be fitted to a given dataset, and then how can a classification task be completed based on the received estimates.
The process shows, using the Wine dataset, how a regression model can be fitted to a given dataset.
Classification can also be done based on a regression model; in this case, approximate values for numerical labels can be defined based on the regression model, and afterwards, these values can be assigned to concrete class labels.
When creating the regression model, it can be chosen from among various types of regression, such as linear regression or logistic regression. .