Goodness Of Fit Test Regression. $$y = x\beta + \epsilon$$ There are a number of tests based on comparing the observed values to expected values.
Goodness of fit i goodness of fit measures for linear regression are attempts to understand how well a model fits a given set of data. A unified approach for testing goodness of fit is now available for binary, multinomial, and ordinal logistic regression models. Regression line the regression equation is jk = l +mn:
R squared, the proportion of variation in the outcome y, explained by the covariates x, is commonly described as a measure of goodness of fit.
This of course seems very reasonable, since r squared measures how close the observed y values are to the predicted (fitted). Goodness of fit i goodness of fit measures for linear regression are attempts to understand how well a model fits a given set of data. I models almost never describe the process that generated a dataset exactly i models approximate reality i however, even models that approximate reality can be used to draw useful inferences or to prediction future Hosmer and lemeshow goodness of fit (gof) test data:
