Another example where you could use a binomial logistic regression is to understand whether the premature failure of a new type of light bulb (i.e., before its one year warranty) can be predicted from the total duration the light is on for, the number of times the light is switched on and off, and the temperature of the ambient air.
![minitab regression minitab regression](https://i.stack.imgur.com/9NVVf.png)
Physical activity level (in minutes per week), cholesterol concentration (mmol/L) and glucose concentration (mmol/L) are continuous independent variables and body composition is a nominal independent variable (i.e., with three groups: "Normal", "Overweight" and "Obese"). Heart disease is the dichotomous dependent variable (i.e., presence of heart disease is either "Yes" or "No"). In many ways a binomial logistic regression can be considered as a multiple linear regression, but for a dichotomous rather than a continuous dependent variable.įor example, you could use a binomial logistic regression to understand whether the presence of heart disease can be predicted from physical activity level, cholesterol concentration, glucose concentration and body composition. However, in Minitab they refer to it as binary logistic regression. It is the most common type of logistic regression and is often simply referred to as logistic regression.
![minitab regression minitab regression](https://datasciencelk.com/wp-content/uploads/2020/03/image-10.png)
Binomial logistic regression using Minitab IntroductionĪ binomial logistic regression is used to predict a dichotomous dependent variable based on one or more continuous or nominal independent variables.