Linearizing Data to Fit an Equation - A Homework Guide

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In summary, the question is about fitting data using two different equations: y = D/(x+C) and y = (-1/C) *(xy) + (D/C). The first solution uses the transformed variables X = xy and Y = 1/y, while the second solution uses the original variables X = x and Y = 1/y. The first solution provides a more accurate fit and is not absurd, as the book suggests. Plotting the results using the un-transformed equations reveals that the second solution is indeed absurd. This is because linear regression requires the use of independent variables, which is not the case in the second solution.
  • #1
rootX
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Homework Statement


(Data fittings)

for y = D/(x+C)
my book do:
y = (-1/C) *(xy) + (D/C)
I do:
1/y = (1/D)*x + C/D

so using y=Y and X = xy
book finds A and B in Ax+b =y
so, finds an equation that fits the data

It hints that my way is absurd. I really couldn't get what's wrong with using 1/y = ..

Homework Equations





The Attempt at a Solution

 
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  • #2
What exactly are you trying to do? Your formula is not absurd: if y= D/(x+C) then
1/y= (x+C)/D= (1/D)*x+ C/D. y= (-1/C)*(xy)+ (D/C) is also true. Which of those true statements works for whatever you are trying to do, depends on what you are trying to do! And you haven't told us what that is!
 
  • #3
here's the question:
x, y
1,2
2,5
3,10
4,17
5,26

when using X = xy and Y = 1/y for y = D/(x+C) the least square fit is
y = -17.719403/(x-5.476617)

and when using X = x and Y = 1/y the least square fit is:
y = 1/(-0.106253x+0.4987330)

Determine which fit is best and Why one of the solutions is completely absurd

Since, the book has provided us the formula in the text, so it should be obvious that other one is absurd. I plotted both, and found the first one fits more accurately.
 
  • #4
RootX - did you really plot out the results using the un-transformed equations? Plot out the original form using the C and D results from the first transformed model. I think you'll see something.

After that, to answer the 'absurd' question; what can you say about linear regression and the [hint] independent variables?
 

What is linearizing data?

Linearizing data is the process of transforming a non-linear dataset into a linear one by applying mathematical operations such as logarithms or power functions. This allows for easier analysis and fitting of an equation to the data.

Why is it important to linearize data?

Linearizing data is important because it allows for easier visualization and analysis of relationships between variables. It also makes it easier to fit an equation to the data, which can then be used for prediction and further analysis.

What are the common methods for linearizing data?

The most common methods for linearizing data include logarithmic transformation, power transformation, and inverse transformation. Other methods include using a linear regression model or plotting the data on a semi-log or log-log scale.

What is the purpose of fitting an equation to linearized data?

The purpose of fitting an equation to linearized data is to describe the relationship between variables and make predictions about future data points. This can also help identify any patterns or trends in the data and provide insights for further analysis.

What are some potential limitations of linearizing data?

Linearizing data may not always accurately represent the true relationship between variables, especially if the data is highly non-linear. It can also be time-consuming and may require a good understanding of mathematical concepts. Additionally, linearizing data may not always result in a perfect fit to the data points.

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