Hi,
I have a dependent continuous variable and 3 dependet categorical variables (for instance - day in week(1-7), day in month (1-31), and month (1-12))
and I want to know according history data - what weight to give to each level in each category so I can predict the dependent continous variable based on the day.
The wight needs to be in precentage.
I made proc glm on the three variables and got a significancy in every one of them.
How do I determine what precentage to give to each level of each variable, what test / procedure will give me that precentages?
Thanks in advance,
Liat
Hi,
Generalized Additive Model is another choice. Smoothing and other flexibility in modeling is all in PROC GAM.
Thanks for your answer. But I dont understand, Can you be more specific?
here is the data for example -
volume , day_in_week, day_in_month, month
1200,1,24,11
801,4,31,7
600,7,5,2
When I use Proc GLM it gave me beta estimatores for dummi variables so it is already include the volume
my line looks like this where X1=X2=X3=1
Y= intercept+ b1*X1+b2*X2+b3*X3
So I cannot user the beta estimatores as precentage.
What proc will out[ut the bet's as a precentage and not as a number of volume.
Thanks
PROC FREQ will certainly do something to give percentages, although they won't be beta-hats, or weights.
I don't know what good this will do, though. What is the dependent variable? Is it volume? Do you wish to predict volume, given a day-month-year? What are you going to do about trends/seasonality/autocorrelation? I think a time series analysis might be of far more utility.
More information on the proposed analysis would be helpful.
Steve Denham
Hi Steven, here is answers for your questions -
What is the dependent variable? Volume
Is it volume? Yes
Do you wish to predict volume, given a day-month-year? Yes, exactly.
What are you going to do about trends/seasonality/autocorrelation? I was thinking of adding another indicator variables for fast day and and holiday day. which will take care of the seasonality and the rest the model will deal/find.
More Clarifications - I wish to predict volume, given a day-month-year + day in week (sunday/monday/...and holiday indicator)
looking to know what weight contribute each of the component (all categorical variables).
As the volume is not necessarily spread uniqly (linear) throw the month. for example: if I'm at the begining of the month I might see a bad picture of what it may look at the end of the month. So I'm looking for some beta-hats, or weights for the day/month/day_in_week
This is starting to sound like a combination time-series/data mining problem. I'm going to have to defer to others for an approach. You may wish to post a similar question in the SAS Forecasting and Econometrics forum, where you may get eyes on the problem from those who deal with this sort of data more regularly.
Steve Denham
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