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#91




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I had an issue with Module 6 that prevented me from opening it. I would click "Open Item" then the page would eat up all of my computer's memory before ultimately crashing. Super weird. Anyways, whether that's your issue or not, I called SOA customer service and they had me send an email describing the issue for their IT department to fix. It took about 48 hours to get my Mod6 access restored so I'd get the ball rolling now.
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#92




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#93




If we have a categorical variable numbered, for example, 06, and the model's summary coefficient is like 0.5, do we essentially multiply whatever number in the data (06) by 0.5 to get the predicted value? If so, what about the 0? I guess this question also applies to binarization.

#94




Also has anyone tried to manually calculate the predicted face amounts as provided by the Sample Project Report under Findings and Recommendations? So essentially, we multiply $88 by all the factors in the table below, which leads to the predicted face amount? My number was way off when using that formula on my calculator.

#95




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Last edited by mistersunnyd; 11162018 at 10:36 PM.. 
#96




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A few thoughts: 1) 6 levels is a lotbut if is an important variable, you may have work with it. 2) If the predictor is ordinal, you may want to divide the 6 levels into fewer groups. For example, if the predictor is a survey result on a 05 scale and all respondents answering 0 trend one way and the 15s trend away from the 0s but similarly to oneanother, then you may wish to group 0 and >0 as a flag factor variable. For instance, if the survey asked how frequently do you do meth, fentanyl and heroin at the same time, you can assume someone who has done it once (say, a 1) is pretty messed up like 2s, 3s, etc. Bad goofy example, but perhaps we will all remember it. The 0s are pretty much a completely different animal. 3) Even with ordinal data, you can't always assume that the distance between 0 and 1 is the same as the distance between, say, 1 and 2. Dummy code or manipulate the predictor. 4) The summary coefficient works the way you mentioned if the categorical predictor is binary. So make it binary or don't do that. 5. Unless I am misunderstanding your question, this is something you need to know by now or VERY soon if you want to perform well on the exam.
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#97




I understand what you're saying, but what if one of the predictors is race and labeled from 06? Assuming that race is legal to use as a predictor for some science experiment.
From what I see, you basically just add the coefficient for whatever factor level it is to the formula. Then you apply the log link function. Last edited by mistersunnyd; 11172018 at 10:19 PM.. 
#98




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x_1 x_2 high 1 0 med 0 1 low 0 0 So you have E(y)=b_0+b_1x+b_2x_2 and from our definitions we have: E(yhigh)=b_0+b_1(1) E(ymed)=b_0+b_2(1) E(ylow)=b_0 If you had something like y=.2+.5x_1+.4x_2 and you want to make a prediction for someone in the highrisk group, E(yhigh)=.2+.5.
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#99




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#100




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Set Variable color = C Then C is: Blue = 0 Yellow = 1 Green = 2 Pink = 3 Then you have to create 3 dummy variables: C_1 = 1 if blue, 0 otherwise C_2 = 1 if yellow, 0 otherwise C_3 = 1 if green, 0 otherwise You don't have to create C_4, because if all C_1, C_2 and C_3 equal 0, then it automatically means you predicted that the color is pink.
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