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So today weamp;#39;re going to talk about poison and negative binomial regression and these are regression models that you use when certain violations are occurring with data and you canamp;#39;t use ordinary least squares and basically those assumptions are one is the outcome variable is a is a type of count and itamp;#39;s discrete and itamp;#39;s not continuous and OLS usually assumes that the variable of choice is is a continuous measure in that thereamp;#39;s values between one and two like a 1.5 but if youamp;#39;re counting maybe the number of children you donamp;#39;t really have 1.5 children so that violation is occurring also another thing that might be happening is the residuals might not have a bell-shaped distribution which would also violate all OS in these situations you usually just make an adjustment by using a different type of distribution assuming a different type of distribution and the probability function um one is a poison distribution and another one is
