Equation/mathematical model to a fitted line

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In summary, the conversation discusses the process of fitting a mathematical model to different types of curves, such as linear, exponential, and sinusoidal curves. The participants mention using precalculus or algebra textbooks to learn the necessary formulas and methods. Nonlinear regression is suggested as a way to fit a curve to data, and the use of software like 'R' is recommended. The conversation also touches on the importance of choosing the right guess function for the data.
  • #1
microbiek
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Hi I hope someone can either help me directly and give me an overview of what needs to be done and also direct me to a textbook or some source which will allow me to learn this in depth.

Basically If I have data and I graph it, I fit a line to it (it can be curved) how then do I determine an equation to represent that line and allow me to extrapolate.

For example if the line is sinusoidal or straight and then beginning to curve etc how can I make a mathematical model to represent these different types of curves.

Thanks very much,

R
 
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  • #2
Sounds like precalculus, or maybe even algebra two. You can take points from the line and decipher its equation using various formulas.. Hmm try rea's problem solvers "algebra and trig" it's a pretty good book. Or any precalculus/college algebra book.. We used Larson but I never liked the textbook layout much.
 
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  • #3
But would that be able to make a model for data which starts of linear and then turns exponential? if so great!
 
  • #4
I think you'd do a best fit line for the data.
 
  • #6
If you have the data, and you know the general form of the curve you want to fit, then you can use non-linear regression to find the exact form of the function which best fits your data.

I just had a quick look at the wikipedia page and it's fairly awful, and my knowledge of stats books isn't great / I don't know what level you're used to reading. Do you have a particular computer software package that you have the data in? eg. if it's in MATLAB, their documentation gives a very concise explanation of it.

In short... non-linear regression is the word you want to be searching for.

You give a guess function f(x,[parameters]) and the data [(x1,y1), ... (xn,yn)] and it will find the parameters that minimises the sum of the residuals squared, [y1 - f(x1)]^2 + ... + [yn - f(xn)]^2. Sometimes it takes a while and if the function is tricky then it might not work.
 
  • #7
Yeah, whichever line fits the data best with the smallest SSE.

Ahah yeah definitely not algebra 2. Finite mathematics, discrete math or a calc book might help you. I cannot!

The bottom example is two different lines but I get the joist of what you're saying. Maybe post the question in the statistic fourm?
 
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  • #8
I was going to use 'R' to put the data in (exporting from excel). Ok that's helpful so non linear regression! I was just worried that it wasn't going to be a simple curve and so I knew I'd need some other way to make a formula. I still havnt finished collecting my data so I have plently of time to read up on it. I kind of want to know how to do it by hand so il peruse amazon.

I did engineering in college and so I am no mathematician!

Cheers!
 
  • #9
R should be fine. If you buy a book, check it's got a chapter on nonlinear regression, but what you mainly need to do is pick the right guess function. The maths of fitting it to the data is pretty hard work, and best left to a computer!
 

Related to Equation/mathematical model to a fitted line

1. What is an equation or mathematical model to a fitted line?

An equation or mathematical model to a fitted line is a way to represent a relationship between two variables in a linear manner. It is used to describe the trend or pattern in the data and can be used to make predictions or inferences about the relationship between the variables.

2. How is a fitted line created using mathematical modeling?

A fitted line is created by finding the best fit for the data points using a mathematical model. This is usually done by minimizing the sum of the squared errors between the data points and the line. The resulting line is the one that best represents the trend in the data.

3. What is the significance of the slope and intercept in a fitted line?

The slope of a fitted line represents the rate of change between the two variables in the model, while the intercept represents the value of the dependent variable when the independent variable is equal to zero. These parameters can provide important insights into the relationship between the variables and can be used to make predictions or inferences.

4. How can a fitted line be used to make predictions or inferences?

A fitted line can be used to make predictions or inferences about the relationship between the variables by plugging in values for the independent variable and solving for the dependent variable. This can help to identify trends and patterns in the data and make predictions about future outcomes.

5. What are some limitations of using a fitted line as a mathematical model?

While a fitted line can be a useful tool for representing the relationship between two variables, it does have some limitations. It assumes a linear relationship between the variables, which may not always be the case. Additionally, it may not accurately represent the data if there are outliers or if the relationship is non-linear. It is important to carefully consider the data and the assumptions made when using a fitted line as a mathematical model.

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