Add to ORKGIn this last video of the series, Dr Heini Väisänen discusses how to interpret interaction effects in binary logistic regression models. Using a simple regression model example, she presents two options: interpretation using predicted probabilities and interpretation using odds ratios
The capacity to correctly assess the existence of interaction is a high-value modeling capability am...
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a...
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a...
In this first video of the series, Dr Heini Väisänen introduces the binary logistic regression, expl...
In this second video of the series, Dr Heini Väisänen discusses binary logistic regression models wi...
In this video, Dr Heini Väisänen discusses multinomial logistic regression models with more than one...
<p>OR = odds ratio, CI = confidence interval.</p><p>Binary logistic regression model showing the...
This paper considers the role of covariates when using predicted probabilities to interpret main eff...
In this video, Dr Heini Väisänen talks about the proportional odds assumption when conducting ordina...
Re-parameterized regression models may enable tests of crucial theoretical predictions involving int...
An interaction effect exists when the influence of an independent variable X on an outcome variable ...
An interaction effect exists when the influence of an independent variable X on an outcome variable ...
An interaction effect exists when the influence of an independent variable X on an outcome variable ...
Logistic regression is a cornerstone of epidemiology and the method of choice for risk adjustment mo...
We provide practical advice for applied economists regarding robust specification and interpretation...
The capacity to correctly assess the existence of interaction is a high-value modeling capability am...
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a...
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a...
In this first video of the series, Dr Heini Väisänen introduces the binary logistic regression, expl...
In this second video of the series, Dr Heini Väisänen discusses binary logistic regression models wi...
In this video, Dr Heini Väisänen discusses multinomial logistic regression models with more than one...
<p>OR = odds ratio, CI = confidence interval.</p><p>Binary logistic regression model showing the...
This paper considers the role of covariates when using predicted probabilities to interpret main eff...
In this video, Dr Heini Väisänen talks about the proportional odds assumption when conducting ordina...
Re-parameterized regression models may enable tests of crucial theoretical predictions involving int...
An interaction effect exists when the influence of an independent variable X on an outcome variable ...
An interaction effect exists when the influence of an independent variable X on an outcome variable ...
An interaction effect exists when the influence of an independent variable X on an outcome variable ...
Logistic regression is a cornerstone of epidemiology and the method of choice for risk adjustment mo...
We provide practical advice for applied economists regarding robust specification and interpretation...
The capacity to correctly assess the existence of interaction is a high-value modeling capability am...
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a...
Logistic regression is a classical linear model for logit-transformed conditional probabilities of a...