Fitting the Potato Yield Model with Superphosphate Fertiliser

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Homework Help Overview

The discussion revolves around fitting a statistical model to potato yield data based on varying levels of superphosphate fertilizer. The original poster presents a model involving average yields and a design matrix, seeking assistance in formulating the design matrix and understanding the subsequent calculations related to residuals and fitted values.

Discussion Character

  • Exploratory, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants discuss the formulation of the design matrix and the model representation. There are attempts to clarify the process of calculating residuals and fitted values, with some questioning the dimensions of matrices involved in the calculations.

Discussion Status

Several participants have provided feedback on the correctness of the design matrix and the calculations for fitted values and residuals. There is an ongoing exploration of matrix multiplication and its implications for the model parameters.

Contextual Notes

Participants are working under the constraints of homework guidelines, which may limit the extent of guidance provided. There is a focus on ensuring that the calculations align with statistical principles, particularly in the context of ordinary least squares.

squenshl
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Homework Statement


Suppose (Y1, Y2, Y3, Y4) = (5.2, 6.8, 11.9, 17.0) are the average yields (in tonne/ha) of potato grown in soil with 4 different levels of superphosphate fertiliser, x1 = 1.20, x2 = 1.75, x3 = 2.30, x4 = 2.85. We want to fit the model E[Yi] = [tex]\beta[/tex]1 + [tex]\beta[/tex]2xi + [tex]\beta[/tex]3zi where zi = 3xi2 - 4.4875 for i = 1,...,4.
Suppose that the observations (Y1, Y2, Y3, Y4) are independent with common variance [tex]\sigma[/tex]2
How do I find the design matrix X and hence write the model in the form E(Y) = X[tex]\beta[/tex]

Homework Equations





The Attempt at a Solution


I found z1, z2, z3, z4 using x1, x2, x3, x4 to get z1 = -0.1675, z2 = 4.70, z3 = 11.3825, z4 = 19.88 so from E(Yi) do I get
X =
(1 1.20 -0.1675
1 1.75 4.70
1 2.30 11.3825
1 2.85 19.88)
hence E(Y) = X[tex]\beta[/tex] where [tex]\beta[/tex] = ([tex]\beta[/tex]i)T
 
Last edited:
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I tried it again and got the same but I'm not still not sure.
 
Yes, this seems right if you are doing ordinary least squares.
 
But what happens to (Y1, Y2, Y3, Y4), do we use those to find the residuals, fitted values and leverages.
 
To find the residuals you need to find the estimates of [itex]\beta[/itex] first. Then

a) fitted values are [itex]X \hat{\beta}[/itex]

b) residuals are original y - fitted values

c) your text (or your notes) will explain how to get the leverage values (if you use software (such as R or S+, for two examples) you can get these - everything you need, actually - from there
 
Is that [tex]\beta[/tex] = (XTX)-1XTY. I'm still not sure that my design matrix is right.
 
Last edited:
Yes, I think that's right.
 
Yes it is. Because my Hat matrix H is idempotent (H2 = H) and the trace of H = p = 3.
 
I have found my leverages and fitted values (my fitted values are a 4x4 matrix) but when it comes to finding the residuals r = Y - Y(hat) i get a 4x1 matrix - a 4x4 matrix and that is impossible.
 
  • #10
How did you get your fitted values to be 4x4? Your beta hat is a column vector, since Y is a column vector, and then X*beta hat is a column vector.
 
  • #11
I think I did my matrix multiplication wrong for beta hat. I get a 3x3 matrix for (XTX)-1 which is
(77.3577 -59.0788 4.7523
-59.0788 45.4737 -3.6883
4.7523 -3.6883 0.3036).
For my XTY i get the matrix
(Y1 + Y2 + Y3 + Y4
1.2Y1 + 1.75Y2 + 2.3Y3 + 2.85Y4
-0.1675Y1 + 4.7Y2 + 11.3825Y3 + 19.88Y4).
How do I get a column matrix from this?
 
  • #12
Now multiply (XTX)-1 by XTY to find your beta hat vector. The dimensions should work out.
 
  • #13
How do I multiply the 2 matrices. I've never seen that type of matrix multiplication before.
Is it just (77.3577 - 59.0788 + 4.7523)/(Y1 + Y2 + Y3 + Y4) etc.
 
Last edited:
  • #14
Never mind. Got it. The fitted values are
(5.023
7.409
11.580
17.534)
and the residuals follow on from that.
 

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