Examples of using Regression models in English and their translations into Indonesian
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Arbitrary regression models can be used.
Adjusted R-square values are used to compare regression models.
Using Mallows' Cp to compare regression models is only valid when you start with the same complete set of predictors.
Air pollution exposure was assessed by averaging data from nearest air pollution monitoring stations,or using complex dispersion models or land-use regression models.
Regression models for prediction are often useful even when the assumptions are abstemiously violated, although they may not perform optimally.
They assessed air pollution exposure by averaging data from the nearest air pollution monitoring stationsor using complex dispersion models or land use regression models.
They then used Cox hazard regression models to adjust for the different periods of time that had elapsed between being diagnosed with CAD and being diagnosed with depression.
The automated calculationprovides role in the analysis of econometric models and regression models, heteroscedasticity, serial correlation, modelling or binary regressors.
The researchers used regression models to control for maternal smoking during pregnancy, season of birth, particulate matter air pollution, breast feeding duration, child's sex and age, height, technician and other covariates.
According to Sean Osbourne, Scotiabank's currency strategist,the dynamics of the loonie are significantly affected by short-term regression models, which include the cost of oil, bonds, stocks and current volatility.
This is particularly true for multiple regression models, where there is a temptation of adding many predictors to the model to see which ones come out as the“winners”.
If the value of the probability of their significance above 5% confidence level and a Scatterplot graphs, dots spread above or below zero on the Y axis,then the regression models can be concluded does not contain any heteroscedasticity(Ghozali, 2005).
From importing spreadsheets to creating regression models, to exporting charts, our courses cover all the basics, with an emphasis on clarity, interpretation, communicability, and application.
The internet economy could contribute up to $537.4 billion to India's GDP in 2020, of which a minimum of $270.9 billion could be attributed to apps,” says the study,using various economic analyses and logical regression models to isolate and calculate the impact of apps on the Indian economy.
Regression models that form the sigmoid curve of growth(i.e. Weibull, root, modified exponential, logistic, logistics power, Gompertz, two-exponential association, three-exponential association) were tested to generate growth curves.
Heterogeneity among ER subtypes was evaluated in a case-only analysis,by fitting binary logistic regression models, treating ER status as a dependent variable, with coffee consumption included as a covariate.
Results of multivariate logistic regression models show that the risk of developing the metabolic syndrome over a three-year follow-up period was more than two times higher in adults who reported frequent loud snoring(odds ratio= 2.30).
We used a range of sophisticated statistical techniques to assess these relationships,including multivariate regression models which estimated the effect of various forms of mistreatment on health outcomes, while accounting for other potential causes for ill health(age, low socioeconomic background, etc.).
Like other regression models, QSAR regression models relate a set of"predictor" variables(X) to the potency of the response variable(Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable.
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.
The researchers used conditional logistic regression models to compare full siblings exposed to anemia with unexposed siblings, adjusting for sex, birth year, and IPI to evaluate the possibility of shared genetic liability as a potential confounder.
The data collected willbe tested using multiple linear regression models, to see salaries, opportunities to progress and students' perceptions about the accountant profession about the desire of students to have a career as a qualified accountant.
Here long-short-term-memory models(LSTMs) were compared to linear regression models(LR) to explore the benefits of simple, deep neural networks in predicting ENSO, in addition to quantifying the relative importance of the sources of ENSO's predictability.
When using repeated measures linear regression models to make causal inference in laboratory, clinical and environmental research, it is typically assumed that the within-subject association of differences(or changes) in predictor variable values across replicates is the same as the between-subject association of[…].
The regression model can be written as.
Our regression model is linear in parameters.
Regression model is used to predict the.
In the univariate linear regression model, we assume that.
The regression model is linear in parameters.