All right, so my dependent variable is sold price, it is theoretically a
continuous variable, though most of the values are between 0 and 25
Sold Price = B0 + B1(age) + B2(gender) + B3(marital) + B4(educ) + B5(cars)
+ B6(license) + B7(credit) + B8(type) + B9(home) + B10(id)
age is a numeric var
Yes and no.
Thanks for that code, it was really useful, though not exactly what i was
getting at.
So I'm predicting sold on a number of independent variables.I'm trying to
find an estimated sold
price for each combination of the variables. So what is the expected sold
price for each possible
vari
I looked into what you suggested and got the following results.
> y.hat.new 1234567
> 89 10
1144.675 1190.714 1236.753 1157.829 1210.445 1203.868 1128.232
1282.792 1246.619 1154.540
11 12 13 14
Hi Abraham,
Please let us take a step back - what is your end goal from this analysis?
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Hi Abraham,
Isn't this what you wanted:
data.frame(all.x, y.hat.new)
p.s: it might be safer to use:
myData
mod1 = lm(sold ~ age + gender, data = myData)
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combind
?predict
and:
expand.grid(weather=1:2,gender=c("male","female"))
Contact
Details:---
Contact me: tal.gal...@gmail.com | 972-52-7275845
Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
www.r-sta
Lets say I have a linear model and I want to find the average expented
value of the dependent variable. So let's assume that I'm studying the
price I pay for coffee.
Price = B0 + B1(weather) + B2(gender) + ...
What I'm trying to find is the predicted price for every possible
combination of values
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