Mean and Dispersion Additive Model (MADAM)
- Assumes the response is an additive (weighted) sum of predictor variables.
- Treats each predictor’s effect as constant and estimable via linear regression.
- Uses estimated regression coefficients (slope and intercept) to predict response values.
Definition
Section titled “Definition”The Mean and Dispersion Additive Model (MADAM) is a statistical model used to analyze the relationship between a response variable and one or more predictor variables. It is a type of generalized linear model that assumes a linear relationship between the response and predictor variables.
Explanation
Section titled “Explanation”MADAM assumes the relationship between the response and predictors is linear, meaning the response variable is a weighted sum of the predictor variables. Under this assumption, the effect of each predictor on the response is constant and can be estimated using a simple linear regression model. The model estimates regression coefficients—typically an intercept and slopes—that quantify average changes in the response per unit change in each predictor. With these estimated coefficients, MADAM can predict response values for given predictor values.
Examples
Section titled “Examples”Income and happiness
Section titled “Income and happiness”Suppose data are collected on income and happiness levels for 100 individuals and MADAM is used to analyze the relationship. The model estimates the effect of income on happiness by calculating the slope of the regression line (the average change in happiness for each unit change in income) and the intercept (the average happiness level of individuals with zero income). For example, if MADAM estimates a slope of 0.5 and an intercept of 5, then an increase of one unit in income is associated with an increase of 0.5 units in happiness on average, and individuals with zero income have an average happiness level of 5.
Education level and job satisfaction
Section titled “Education level and job satisfaction”When the response variable is job satisfaction and the predictor is education level, MADAM uses a linear regression model to estimate the effect of education level on job satisfaction and can predict job satisfaction levels based on education levels.
Use cases
Section titled “Use cases”- Psychology
- Sociology
- Economics
Related terms
Section titled “Related terms”- Generalized linear model