Examples of using Logistic regression in English and their translations into Greek
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Logistic regression.
Based on a logistic regression.
Logistic regression and probit regression for binary data.
B aModel estimates by logistic regression bp-value< 0.0001.
The logistic regression curve is approximately linear in the middle range and logarithmic at extreme values.
Response rates and p-values based on a logistic regression model.
For the analysis, logistic regression mathematical models were developed.
Confidence interval around observed difference of response rates; P-value< 0.0001 from logistic regression model, including stratification factors.
Multinomial logistic regression and multinomial probit regression for categorical data.
Following the approachof Kuss Kuss O: How to use SAS for logistic regression with correlated data.
P-value a a Logistic regression model adjusted for randomisation stratification variables.
Since the dependent variable had two categories, Killed/Severely Injured(KSI) and Slightly Injured(SI),the binary logistic regression analysis was selected.
The analysis was performed using a logistic regression model with treatment as the only factor.
Logistic regression and probit regression are more similar to LDA, as they also explain a categorical variable.
IDR a Primary endpoint from logistic regression adjusted for loading dose and patient status.
Logistic regression revealed a statistically significant association between nintedanib exposure and DCE-MRI response.
D p< 0.001;compared to vehicle by logistic regression with treatment, study and anatomical location.
Logistic regression models, they can be used to estimate the probabilities of things like lightning striking, and I can apply them to these circumstances.
Odds ratio andp-value were obtained from a logistic regression model adjusted for baseline ECOG Performance Score(0 versus 1).
A probit model is a popular specification for an ordinal[2] or a binary response model.As such it treats the same set of problems as does logistic regression using similar techniques.
Multinomial logistic regression and multinomial probit regression for categorical data.
In this context, we studied four different algorithms(decision trees, naive bayes,support vector machines and logistic regression) for a total of approximately one thousand images.
A series of logistic regression equations and chi-squares were assessed for proportional differences between users.
Along the specific diploma thesis, the theoretical framework of the Polynomial Logistic Regression method, as well as the ID3, C4_5 and CART Algorithms, is thoroughly presented.
In addition, Logistic Regression models are developed to study the impact of employment and other factors on mortality.
P< 0.001 compared to placebo(based on adjusted odds ratio comparisons from a logistic regression model using multiple imputation for missing data values).
Based on logistic regression with treatment effect and adjustment for each subject's age at screening and HFMSE score at baseline.
Response rates and p-values for PASI andPGA were estimated based on a logistic regression model where missing data were imputed using multiple imputation based on the MCMC method.
Logistic regression, model parameters interpretation, logistic regression inference, the case of categorical predictive variables, multiple logistic regression, model choice, model sufficiency test.
Values presented for GIOTRIF vs. erlotinib, p-value based on logistic regression b p-value for time to deterioration based on stratified log-rank test c p-values were not adjusted for multiplicity.