Fox Module 2: Basics of regression analysis HW


Fox Module 2: Basics of regression analysis HW

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NEAS
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Module 2: Basics of regression analysis

 

(The attached PDF file has better formatting.)

 

Homework Assignment: attributes of classical regression analysis

 

Claim severity and speed

 

Suppose a regression of Y = the logarithm of claim severity on X = the speed of the car satisfies the five attributes of classical regression analysis on pages 15-17. Explain whether of regression of Yʹ = claim severity on X = the speed of the car satisfies each attribute.

 

Jacob: What is this homework assignment asking?

 

Rachel: Yʹ = eY. If the conditional distribution of Y, given X, is symmetric, is the conditional distribution of Yʹ, given X, symmetric or skewed? Answer this question for each of the five attributes on page 15-17:

 


           symmetric vs skewed

           single mode vs multiple modes

           normal vs heavy tailed

           equal vs unequal spread

           linear vs non-linear


 

 

For four of these five attributes, the relation assumed in classical regression analysis does not hold for Yʹ if it holds for Y.

 

Jacob: Are the five attributes explicitly listed?

 

Rachel: The five attributes are implicit in Fox’s discussion: symmetric, unimodal, normal distribution, constant variance, and linear relation.


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Michelle2010
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NEAS:  Could someone help me with the equal vs unequal spread?

Here are my thoughts:

If Y is normal with equal spread, then the conditional variance of Y,  sigma, is constant for all the Xs.

I realize that this does not necessarily mean the conditional variance of Y' will be constant for all the Xs, but wouldn't it be constant in the case where the mean of Y is constant for all the Xs?

Is their someway I can insert formulas on this discussion board to make my question clearer?

[NEAS: If the mean of Y is constant at all X points, the estimated beta is zero: Y = alpha + 0 * X + epsilon. Classical regression analysis assumes the means of Y differ by X (so beta is not zero), but the variance of the distribution of Y does not differ by X.]

 


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