Troubleshooting a Singular Matrix When Modeling a Curve

In summary, the conversation discusses the process of modeling a curve using a sine regression with a graphic calculator. The individual initially encountered an error message but then attempted to solve the problem by hand using three points. They then discuss the desired format for the solution and provide their calculations for the variables A, B, D, and C. They also inquire about the correct units to use for the calculation and seek confirmation on the accuracy of their calculator's results.
  • #1
Peter G.
442
0
When I try and model a curve using a sine regression with my graphic calculator, it returns an error message that says: Singular Matrix

I decided then to do it by hand. Here are three points.

Minimum: (0,554.8), Maximum: (45, 1220.5) and a random point (20, 830.7)

I want something in the format of: A*sin B(x-c) + d

This is what I did, is it correct?

A = (1220.5-554.8)/2
B = (2*pi)/45
D = (1220.5+554.8)/2
C = One unknown, so I used the random point shown to solve:

(Should I have calc in radians or degrees?)

My steps: ( I will use letters for simplicity)

830.7 = A*sinB(x-c)+d
830.7 - D = A*sinB(x-c)
Ans/A = sinB(x-c)
sin-1(Ans/A) = B(x-c)
Ans/B=x-c
-c=(Ans/B)-x

But I am not getting anything sensible...

Is my calculator right or did I do anything wrong?

Thanks
 
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  • #2
Peter G. said:
When I try and model a curve using a sine regression with my graphic calculator, it returns an error message that says: Singular Matrix

I decided then to do it by hand. Here are three points.

Minimum: (0,554.8), Maximum: (45, 1220.5) and a random point (20, 830.7)

I want something in the format of: A*sin B(x-c) + d

This is what I did, is it correct?

A = (1220.5-554.8)/2
B = (2*pi)/45
D = (1220.5+554.8)/2
C = One unknown, so I used the random point shown to solve:

(Should I have calc in radians or degrees?)
Unless a problem specifically gives a value as the measure of an angle in degrees, you should always use radians. Unfortunately, you don't say how the given information was derived. What are the units for the x variablle? That "45" makes me wonder if it not in degrees. The distance from max to min for a sine function is [itex]\pi[/itex] radians or 180 degrees, NOT [itex]2\pi[/itex] radians or 360 degrees.

YOur formulas for A and D are correct.

30,7
My steps: ( I will use letters for simplicity)

830.7 = A*sinB(x-c)+d
830.7 - D = A*sinB(x-c)
Ans/A = sinB(x-c)
sin-1(Ans/A) = B(x-c)
Ans/B=x-c
-c=(Ans/B)-x

But I am not getting anything sensible...

Is my calculator right or did I do anything wrong?

Thanks
 
  • #3
So I should do pi over 45 years instead of 2pi over 45?
 

FAQ: Troubleshooting a Singular Matrix When Modeling a Curve

What is a singular matrix?

A singular matrix is a square matrix that does not have an inverse, meaning that its determinant is equal to 0. This means that the matrix cannot be inverted and certain operations, such as division, cannot be performed on it.

Why is a singular matrix a problem when modeling a curve?

When modeling a curve, a singular matrix can cause issues because it means that the curve cannot be properly fit to the data. This can result in inaccurate or incomplete models, making it difficult to analyze or make predictions based on the data.

How can I identify if the matrix I am using is singular?

To identify if a matrix is singular, you can use various mathematical methods such as calculating the determinant or performing Gaussian elimination. Additionally, most programming languages have built-in functions to check if a matrix is singular.

What are some common causes of a singular matrix?

Some common causes of a singular matrix include having duplicate or dependent data points, having too few data points for the complexity of the curve being modeled, or using an incorrect matrix size for the given data.

How can I troubleshoot a singular matrix when modeling a curve?

To troubleshoot a singular matrix when modeling a curve, you can try removing any duplicate or dependent data points, adding more data points to increase the complexity of the model, or adjusting the matrix size to better fit the data. Additionally, you can consult with other experts or refer to online resources for further guidance.

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