Week 8 Class 1

Interesting:
1.)  I think that it is interesting that there are five assumptions that are required for a regression model to work, to be unbiased, to be interpretable, to be efficient and or consistent. I thought that a regression model just proved any correlation between variables but there is more that goes into it then just an explanation. If these assumptions are failed then something in the model has to be chased so that consequences can be accounted for. (Cha 4)
2.) I did not know that there are two types of explicit equations (probabilistic and deterministic). I thought the concept of an equation was that one side of the equation equals the other side of the equation but in probabilistic equations that is not the case. Since there is a ransom error term it changes the outcome of the other terms; the dependent variable, the constant, the independent variable, and the coefficient. (Cha 4)
3.) Logistic regression is similar to ordinary regression in that there is a dependent variable that depends on one or more independent variables. The difference between the two is that the dependent variable is binary which means that it has two values in a logistic regression and is continuous in an ordinary regression. This means that because of the variable being binary, then the result will be heteroskedasticity which means that the variance of a variable is unequal across the range of values of a second variable that predicts it. (Cha 5)

Questions:
1.) What is the easiest way to correlate price sensitivity, price/unit curves, and revenue?
2.) How many positive outliers can you have?
3.) Is there any way to avoid the consequences in collinearity?

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