Respected Scholar,
kindly help me in knowing the situation when we shift from OLS to gamma GLM ...
Tahnk you
A data-oriented answer - when your dv has a floor of zero and a longer upper tail than lower one.
A theoretical answer - when the component processes are multiplicative rather than additive. This is an abstract answer but some researchers know their process well enough to make this judgment.
Hello Saira, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The general linear model incorporates a number of different statistical models: ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression model to the case of more than one dependent variable. They can be used for a model which follows probability distribution other than the Normal distribution such as Poisson, Binomial, Gamma and others. The link function is used to model responses when a dependent variable is assumed to be nonlinearly related to the predictors. In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. The exponential distribution, Erlang distribution and chi-squared distribution are special cases of the gamma distribution. The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma) is widely used in modeling continuous, non-negative and positive-skewed data, such as insurance claims and survival data.
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