1- In the Income linear regression example consider the distribution of the outc
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Question
1- In the Income linear regression example consider the distribution of the outcome variable Income.Income value tends to be highly skewed to the right ( distribution value has a large tail to the right ) .Does such a non-normally distributed outcome variable violate the general assumption of a linear regression model ? Provide supporting arguments.
2- In the use of a categorical variable with n possible value ,explain the following
a. why only n-1 binary variable are necessary
b. why using n variables would be problematic
3- In the example of using wyoming as the reference case , discuss the effect on the estimated model parameters ,include the intercept ,if another state was elected as the reference case.
4.Describe how logistic regression can be used as a classifier .
5.Discuss how the Roc curve can be used to determine an appropriate threshold value foe=r a classifier
6- If the probability of an event occurring is 0.4, then
a. what is the odds ratio ?
b. what is the log odds ratio ?
Explanation / Answer
Solution of question(1)
Normality always depends on the errors terms , not on the output dependent variable . Thus in a linear regression model, dependent variables are not needed to be normally distributed, it is residuals that need to be normally distributed. Thus clearly such non normality distributed dependent variable will not violet the general assumption of the linear regression model. So our answer is "NO".
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