Linear regression. Logistic regression. Outcome variable . Models continuous outcome variables. Models binary outcome variables. Regression line. Fits a straight line of best fit. Fits a non-linear ...

Logistic regression. Linear regression. Outcome variable . Models binary outcome variables. Models continuous outcome variables. Regression line. Fits a non-linear S-curve using the sigmoid function .

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

Log–binomial and Poisson regression are generalized linear models that directly estimate risk ratios.7, 8 The default standard errors obtained by Poisson regression are typically too large; therefore, ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

In the first model, where Gall is the only predictor variable (Output 39.9.1), the odds ratio estimate for Gall is 2.60, which is an estimate of the relative risk for gall bladder disease. A 95% ...

9.1.3 Model quality and statistical significance. We will come back to the question of whether the linear model is valid (whether it satisfies the assumptions of the technique). First we want to ...

A solid coverage of the most important parts of the theory and application of regression models, and generalised linear models. Multiple regression and regression diagnostics. Generalised linear ...