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so today were 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 cant use ordinary least-squares and basically those assumptions are one is the outcome variable is a as a type of count and its discrete and its not continuous and OLS usually assumes that the variable of choice is is a continuous measure that theres values between 1 2 like a 1.5 but if youre counting maybe the number of children you dont really have 1.5 children so that violation is occurring also another thing that might be happening is that residuals might not have a bell shaped distribution which would also violate all of us in these situations you usually just make an adjustment by using a different type of distribution assuming a different type of distribution in the probability function and one is a Poisson distribution and another one is a negative binomial distribution basically you use the Pois