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Note: Ordinal independent variables can be used, but they must be treated as either continuous or nominal variables.
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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. 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. Assumption #2: You have one or more independent variables that are continuous or nominal (including dichotomous variables).The two categories of the dependent variable need to be mutually exclusive and exhaustive. If you are unsure about types of variables, see our Types of Variable guide. Examples of such independent variables include gender (two groups: male or female), treatment type (two groups: medication or no medication), educational level (two groups: undergraduate or postgraduate), health insurance (two groups: yes or no), intensity of religious practice (two groups: practicing or non-practicing), personality type (two groups: introversion or extroversion), and so forth. Assumption #1: Your dependent variable should consist of two categorical, independent (unrelated) groups (i.e., a dichotomous variable).Assumptions #1 and #2 are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these assumptions before moving on. You cannot test the first two of these assumptions with Minitab because they relate to your study design and choice of variables. Minitab Assumptionsīinomial logistic regression has seven assumptions. If you would like us to add a premium version of this guide, please contact us. Note: We do not currently have a premium version of this guide in the subscription part of our website. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a binomial logistic regression to give you a valid result.
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.
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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.