GLM.Basics — BattleActs Exam 8 Forum

GLM_ModelRefinement6 degrees of freedom

jstark — Sat, 12 Oct 2024 03:28:45 +0000

Should the denominator df here subtract 3 additional df in order to incorporate the new variable with 4 levels, since that will be in the "bigger" model?

Also, in the previous problem (GLM_ModelRefinement5), we said that a 2nd order polynomial added 2 parameters, but here we're saying that a 2nd order polynomial adds 3 degrees of freedom. Maybe this is a subtle distinction between parameters and degrees of freedom that I'm not picking up on. However, the wiki seems to make a direct connection between them.

Thanks in advance!

2018Q7a

xlto22 — Sun, 14 Jul 2024 00:52:41 +0000

The questions states that "The actuary has decided to offset all of the current model variables before fitting the new variable." Is this statement the reason that the "Fitted Probability w/o Insurance Score" being used to calculate the offset terms?

Response Curve for Negative Beta

Ajshepar36 — Wed, 19 Jun 2024 21:31:34 +0000

Could you please clarify page 12 of the GLM text where they describe the response curves for logged variables when beta is <0?

It seems to me like the response curve for any negative beta would decrease at a decreasing rate, being of the form c*x^(-b) for some constant "base AOI" c, and some beta b. What am I missing here? Thank you!

Goldburd.Basics practice questions

jmoore13 — Mon, 16 Oct 2023 16:19:01 +0000

In the first example question, why don't we take the ln of age since it's a continuous variable and there's a log link? Is it because it doesn't specifically say to like it does in 2016 #6a? Can we do it either way? Thanks in advance!

PE1_Exam8_Q19

dxsholden — Sun, 01 Oct 2023 22:19:19 +0000

Can you explain why exposure are related in ILF factors? To solve this problem you have to figure out the portions of the losses in each layer that would be subject to the reinsurance layer. You do so by using the ILFs, why can you do this?

How to Handle Categorical Variables

tracyguo8 — Tue, 12 Sep 2023 03:23:51 +0000

"When the (1−p) confidence interval, also known as the error bar, crosses the level indicated by the base level category then that estimate is not significant."

Could you please elaborate what does it mean by "confidence interval crosses the level indicated by the base level category"?

Thanks.

Types of Variables to Use in a GLM

tracyguo8 — Tue, 12 Sep 2023 02:21:52 +0000

"For a logged original predictor then if the coefficient is larger than 1, then the original predictor increases at an increasing rate."

Is it the original predictor or the target variable that increases at an increasing rate when coefficient if larger than 1?

Setting Base Levels for Categorical Variable

downthesun01 — Thu, 07 Sep 2023 12:08:55 +0000

Hi. I'm looking at the GLM_DesignMatrix.pdf.

I picked the B1 base level to be [Shutters: Yes] in the number of claims table (there are no exposures given so I just used this) there 49 claims with shutters and 41 with no shutters. Likewise, I selected [distance />25] because there were 53 claims />25 miles and 37 claims /<25 miles.

My design matrix ended up looking exactly like the solution, but the solution says the following:

Alice: "It's important you follow the given description of the parameters because this tells you the base levels. Here it's implicit the base levels are: 1. Homes with no hurricane shutters 2. Homes less than or equal to 25 miles from the coast. Remember the base level is usually the one with the most exposures. This makes sense here as people tend to live close to the coast and not always have hurricane shutters."

I'm confused because the solution doesn't seem to match the above statement. Any clarification would be helpful. Thanks.

2015 Fall Question 2c

jstark — Fri, 15 Jul 2022 02:32:47 +0000

How do we classify vehicle type for levels that we eliminate? I understand how it would work if we, for instance, rolled all Trucks into the base level. But the model solutions say "eliminate." In this context, does eliminate imply that we are rolling it into the base level?

Practice Exam Questions

kennysmart — Sat, 22 Oct 2022 02:03:49 +0000

Hi, I've just worked through the Practice Exam 1 provided, and had 2 quick questions that I was hoping to get clarification on.

Problem #2: The question states the drivers age is 25, however when getting the linear predictor term for age, the solution takes the natural log of 75 * the coefficient for age. Is this an error? If so should the u probability be 5.11%?

Problem #8: The way the question is setup, it says "50 It's estimated the portfolio policies will generate this many "large losses" during the next two years". I take this to mean E(# of claims next year) + E(# of claims in 2 years) = 50. However the solution to part a seems to take this as the E(# of claims this year) = 50.

Fall 2016 Q6.b - A little confused about offsets

hreese4567 — Sat, 27 Aug 2022 18:40:14 +0000

So in the reading it says "We do this via an offset which is a predictor variable with the coefficient set equal to 1." However, in the solution they say we add the variable to the model as an offset, and that we would estimate B4. I would have thought that since that the prior claim count variable was being added as an offset, its coefficient should be 1, so B4=1 instead of estimating it.

To me, it seems like really every other variable is being added as an offset, that is, we have already modeled their coefficients, and now we're adding them with them to a new model where we're keeping their coefficients fixed and using them to estimate a new coefficient for the new prior claim count variable. Is this correct, and the issue is just that the language in the solution is a little loose, or is there something that I'm getting wrong?

Fall 2013 2.c

ecasp12 — Thu, 07 Oct 2021 11:34:24 +0000

The model solution feels like it no longer relates to Goldburd, which is focused on understanding and interpreting GLMs instead of solving them. I would not have thought to take partial derivatives to solve (at least, not without brushing up on MAS content 😊).

Is this still a valid question on the current exam?