![minitab regression minitab regression](https://miro.medium.com/max/1088/0*Gmu8H4Bb7VFW1qsj.png)
Examples of nominal variables include gender (e.g., two groups: male and female), ethnicity (e.g., three groups: Caucasian, African American and Hispanic), profession (e.g., five groups: surgeon, doctor, nurse, dentist, therapist), and so forth.
![minitab regression minitab regression](https://image.slidesharecdn.com/chapter051761/95/chapter05-48-728.jpg)
![minitab regression minitab regression](https://i.ytimg.com/vi/eXD64duWlV0/maxresdefault.jpg)
Examples of continuous variables include height (measured in inches), temperature (measured in ☌), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), firm size (measured in terms of the number of employees), reaction time (measured in milliseconds), grip strength (measured in kg), academic achievement (measured in terms of GMAT score), and so forth.
![minitab regression minitab regression](https://blog.minitab.com/hubfs/Imported_Blog_Media/regression_dialog-2.png)
#MINITAB REGRESSION HOW TO#
In this guide, we show you how to carry out a binomial logistic regression using Minitab, as well as interpret and report the results from this test. The other three variables used to predict the light bulb failure are all continuous independent variables: the total duration the light is on for (in minutes), the number of times the light is switched on and off and the ambient air temperature (in ☌). In this case, premature failure is the dichotomous dependent variable (i.e., the light bulb fails within its one year warranty: "Yes" or "No"). 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. 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. 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.