yes the constant is absorbed into other parameters in extended models
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Chapter 13 confusion!
- Thread starter Molly
- Start date
Since it's gone, shouldn't the degrees of freedom come back?yes the constant is absorbed into other parameters in extended models
no you are looking at nested models, each one building on a previous model, so you deduct the additional parameters from the degrees of freedom each time
But if there is no previous model, I may deduct 1df less, so I hope to find out why the constant of the later model is merged, but it needs to be calculated.no you are looking at nested models, each one building on a previous model, so you deduct the additional parameters from the degrees of freedom each time
I know this is the nested models, but we could only claculated 7 parameter from formal claculation.
YO*FS*TC=YO+FS+TC
+YO.FS+FS.TC+YO.TC
+YO.FS.TC
=(2-1)+(2-1)+(2-1)
+(2-1)*(2-1)+(2-1)*(2-1)+(2-1)*(2-1)
+(2-1)*(2-1)*(2-1)=7
Is there any other reason to explain it?
You start with 2 for YO say
YO*FS*TC=YO+FS+TC
+YO.FS+FS.TC+YO.TC
+YO.FS.TC
=2+(2-1)+(2-1)
+(2-1)*(2-1)+(2-1)*(2-1)+(2-1)*(2-1)
+(2-1)*(2-1)*(2-1)=8
8 parameters
YO*FS*TC=YO+FS+TC
+YO.FS+FS.TC+YO.TC
+YO.FS.TC
=2+(2-1)+(2-1)
+(2-1)*(2-1)+(2-1)*(2-1)+(2-1)*(2-1)
+(2-1)*(2-1)*(2-1)=8
8 parameters
You start with 2 for YO say
YO*FS*TC=YO+FS+TC
+YO.FS+FS.TC+YO.TC
+YO.FS.TC
=2+(2-1)+(2-1)
+(2-1)*(2-1)+(2-1)*(2-1)+(2-1)*(2-1)
+(2-1)*(2-1)*(2-1)=8
8 parameters
But is it a convention that the first factor must be df=2? I feel that no matter which factor is the first factor, it is 1df more than the factor added later.
Even the constant is obviously merged in the later model, and its existence must be deducted from the df.
I'm wondering if there's any particular reason this first df never returns, even though it has been merged and disappeared.
The starting point for a variable 2 for a factor the number of categories or for a constant 1
You only deduct the additional parameters each time for degrees of freedom
The constant is there but it has been combined into another parameter in the revised model
You only deduct the additional parameters each time for degrees of freedom
The constant is there but it has been combined into another parameter in the revised model
The starting point for a variable 2 for a factor the number of categories or for a constant 1
You only deduct the additional parameters each time for degrees of freedom
The constant is there but it has been combined into another parameter in the revised model
I thought I find out my blind point.Thank you.
Hi all,
sorry to restart this thread. just wondering, how do we know the initial dof? i understand from your response that the inital parameter for variable is 2, cosntant is 1 and factor is n and so would know what to take away from the dof, but am unsure what this would be to start with?
Also, what do parameters and dof represent in this context?
thanks so much in advance
sorry to restart this thread. just wondering, how do we know the initial dof? i understand from your response that the inital parameter for variable is 2, cosntant is 1 and factor is n and so would know what to take away from the dof, but am unsure what this would be to start with?
Also, what do parameters and dof represent in this context?
thanks so much in advance
Hi all,
sorry to restart this thread. just wondering, how do we know the initial dof? i understand from your response that the inital parameter for variable is 2, cosntant is 1 and factor is n and so would know what to take away from the dof, but am unsure what this would be to start with?
Also, what do parameters and dof represent in this context?
thanks so much in advance
The original of CH13-Q9 is given by question.
Df of constant model is7, so df of original data is 8.Only for this question.
What tutor said means constant always existed in calculation.
The formal calculation is a kind of incremental algorithm of decreasing df.
It is "The starting point for a variable 2 for a factor the number of categories or for a constant 1
You only deduct the additional parameters each time for degrees of freedom"
For example,the last one in formal calculation become
YO*FS*TC=Constant+YO+FS+TC
+YO.FS+FS.TC+YO.TC
+YO.FS.TC
=1+(2-1)+(2-1)+(2-1)
+(2-1)*(2-1)+(2-1)*(2-1)+(2-1)*(2-1)
+(2-1)*(2-1)*(2-1)
=8
It is "The constant is there but it has been combined into another parameter in the revised model".
"What do parameters and dof represent in this context?"
The parameters in equation.
all same category:YO0.FS0.TC0,YO1.FS1.TC1
one 1 category:YO0.FS0.TC1,YO1.FS1.TC0,YO1.FS0.TC0
two 1 category:YO1.FS1.TC0,YO1.FS0.TC1,YO0.FS1.TC1
This is what I understand from response of assignment X4-8 from tutor.
Last edited:
You would start with some n, where n is the number of data points, so in this question I assume it was 8, but it did not say instead it gave the starting model as 7 degrees of freedom
You would start with some n, where n is the number of data points, so in this question I assume it was 8, but it did not say instead it gave the starting model as 7 degrees of freedom
Hi ykai and andrea,
thank you so much for your help.
you have both cleared up my confusion on this!
Hi ykai and andrea,
thank you so much for your help.
you have both cleared up my confusion on this!
How would you compute the degrees of freedom for april 2013 ct6 q 7 please? we are not given data points but it says dof is 12 for the intercept in the solutions