Examples of using Logistic regression in English and their translations into Indonesian
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The logistic regression is.
Now let's talk about logistic regression.
Fit logistic regression with.
Let's start talking about logistic regression.
Logistic regression analysis was employed to find the best predictive model.
Tomorrow, we will discuss logistic regression.
To use logistic regression, the response must only have two outcomes, i.e. yes/no, 0/1.
If the data were collected using a case-control study design then you should be planning to do 2x2 tables with odds ratios andpossibly logistic regression to analyse the data.
Multivariate logistic regressions compared suicides with MVCs that adjusted for age, sex, and race/ethnicity.
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.
We used logistic regression to test for differences in mortality for each fiscal year and to examine the effect of age and sex on mortality.
Built on NumPy, SciPy, and Matplotlib, Scikit-learn is a machine learning library that implements classification, regression, and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, and gradient boosting.
In this case, Blumenstock used logistic regression, but he could have used a variety of other statistical or machine learning approaches.
The Hosmer-Lemshow statistic evaluates the goodness-of-fit by creating ten ordered groups of subjects and then compares the number actually in each group(observed)to the number predicted by the logistic regression model(predicted).
Logistic regressions were carried out to assess the link between the accuracy of the forecasted scores and the expertise of the participants(expert, amateur, layperson), controlling for age and gender.
The associations between sedentary behaviors andsleep duration were analyzed using logistic regression analyses and were adjusted for sex, body-mass index, self-rated health, socioeconomic status, smoking status, binge drinking, psychological distress and chronic disease/s.
Logistic regression was used to examine the relationship between food insecurity and obesity, controlling for income, race/ethnicity, education, country of birth, general health status and walking.
Given the data matrix and the desired output(e.g., whether the image was classified by a human as an elliptical galaxy), the researcher creates a statistical or machine learning model-for example, logistic regression- that predicts the human classification based on the features of the image.
In a logistic regression analysis, the TPH2 polymorphism emerged^ as the only significant variable that could reliably predict^ clinical placebo response(CGI-I) on day 56, homozygosity^ for the G allele being associated with better outcome.
In a classification setting, Assigning outcome probabilities to comment Can Be Achieved through the use of a logistic model, qui est Basically a method qui transforms information about the binary dependent variable into an unbounded continuous variable andEstimates has regular multivariate model(See Allison's Logistic Regression for more information on the theory of logistic regression).
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).
Logistic regression is very commonly used to analyse epidemiologic data from disease investigations where the disease outcome can be represented as a binary variable(0=no disease, 1=disease) and where multiple risk factors are being considered.
Regarding host-related factors, univariate analysis by conditional logistic regression of 687 matched pairs of cases and household controls showed that TB was associated with male sex, family history of TB, absence of a BCG scar, smoking, alcohol, anaemia, HIV infection, and history and treatment of worm infection.
Finally, a binary logistic regression for each of the 10 matches was done to predict the accuracy of the scores(correct vs. incorrect score) with the participants' expertise categories(expert, amateur, or layperson) as predictor, controlling for age and gender(female vs. male).
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.
Unlike OLS regression, however, logistic regression does not assume linearity of relationship between the raw values of the independent variables and the dependent, does not require normally distributed variables, does not assume homoscedasticity, and in general has less stringent requirements.
Unlike OLS regression, however, logistic regression does not assume linearity of relationship between the raw values of the independent variables and the dependent, does not require normally distributed variables, does not assume homoscedasticity, and in general has less stringent requirements.
Finally, the logistic regression models set up to predict the accuracy of the number of goals scored by each team may have failed to take account of other possible significant predictors, such as the quality of the teams(ability to attack and/or to defend), the team's league position at the time of playing, and the“home effect”(advantage in playing at home).