Glossary entry (derived from question below)
English term or phrase:
curve fit
English answer:
fit / fitting are statistical concepts
Added to glossary by
Elena Sgarbo (X)
Nov 25, 2004 01:48
19 yrs ago
6 viewers *
English term
curve fit
English
Medical
Mathematics & Statistics
"Calculate the R value and R2 value for the curve fit for the semi-log curve."
Sorry to bother you again, but does the "fit" have a synonym?
Sorry to bother you again, but does the "fit" have a synonym?
Responses
5 +3 | fit / fitting are statistical concepts | Elena Sgarbo (X) |
5 +2 | curve approach | Ernesto de Lara |
4 | coefficient of determination | Roddy Stegemann |
Responses
+3
25 mins
Selected
fit / fitting are statistical concepts
Hi Widell,
I'm not sure there is a synonymous for "fit" here. The concept of "curve / model fitting", "finding the best fit", etc, is central to statistical modelling. "Fit" probably has a standard translation in your target language. To help you find it, see if this Eng. website helps. Take a look at Figure 2 and the text underneath, "curve fitting":
http://www.jgp.org/cgi/content/full/115/5/533
You can also find numerous websites describing / depicting fitting. (for ex. by searching Google scholar: http://scholar.google.com).
Good luck!
Elena
I'm not sure there is a synonymous for "fit" here. The concept of "curve / model fitting", "finding the best fit", etc, is central to statistical modelling. "Fit" probably has a standard translation in your target language. To help you find it, see if this Eng. website helps. Take a look at Figure 2 and the text underneath, "curve fitting":
http://www.jgp.org/cgi/content/full/115/5/533
You can also find numerous websites describing / depicting fitting. (for ex. by searching Google scholar: http://scholar.google.com).
Good luck!
Elena
Peer comment(s):
agree |
Veronica Prpic Uhing
: "Curve fitting" in different languages - http://www.cbs.nl/isi/glossary/term850.htm
43 mins
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Thanks, VPUHING
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agree |
fcl
4 hrs
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Thanks, Francois
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agree |
Java Cafe
5 hrs
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Thanks, Java Cafe
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4 KudoZ points awarded for this answer.
Comment: "OK. "Fitting" is good enough for me."
+2
56 mins
curve approach
It is a common job in many fields. You have a set of data (x,y) from an experiment, for example. You make a graph with this set and you get a resulting curve joining the points on the graph with lines. Then, according to what you see, your next step is to derive an algebraic relationship for the curve you got from the data. Maybe you see that the points lay very close to a straight line or maybe a parable so you work on speciphic procedures (curve fitting) in order to have a mathematical relationship that approachs the curve.
That's it and maybe you want to state an hypothesis from your results that will bear you to a scientific law or just simplify your tasks.
That's it and maybe you want to state an hypothesis from your results that will bear you to a scientific law or just simplify your tasks.
Peer comment(s):
agree |
Attila Piróth
: Clear explanation
5 hrs
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thanks Attila
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agree |
Veronica Prpic Uhing
14 hrs
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thanks VPUHING
|
2 hrs
coefficient of determination
The r-square value is a measure of fit. It tells you how much dispersion occurs around the semi-log function that has been proposed as the true mathematical representation (functional form) of the hypothesized relationship.
By way of further explanation consider the following:
Suppose that height and weight are positively related. A mathematical expression that would express this idea is W = f(H) where f'(H)>0 -- namely, W increases with positive increases in H. Unfortunately, W = f(H) is not something you can measure empirically, because you do not know the true nature of f. So, you must hypothesize what that true nature is -- namely, you must propose a functional form.
A straight line of the form W = aH + m, where a > 0, would probably be inappropriate, because short fat people can be heavier than tall thin people. Nevertheless, still you believe that taller people are generally heavier than short people. So, you might hypothesize a different functional form such as W = a•log(H) + m, which, although expressed as a line, actually forms an upward sloping curve when drawn on a two-dimensional sheet of paper.
Both W = aH + m and W = a•log(H) + m, where a > 0, are functional forms of the same mathematical expression W = f(H), where f'(H) >0.
Plot the height (H) and weight (W) for each individual on a pair of coordinate (X,Y) axes, where H and W are X and Y, respectively. What do you obtain? A scatter plot.
Perform two statistical runs that estimate the values of a and m for each of your two functional forms. Plot each of the resulting two curves (one will be a straight line) on your scatter plot. The curve (line) that matches the scatter plot best demonstrates the best fit! It will have the highest r-square value.
R is the correlation coefficient and r-square is the coefficient of determination. The latter tells you the percent of total variation explained by your model -- i.e., selected functional form.
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Note added at 2 hrs 57 mins (2004-11-25 04:45:55 GMT)
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Disclaimer: My full use of this and other forums has been restricted for reasons unknown, so please forgive my lack of direct support for answers offered by other contributors and critical assessment of non-contributors who are misleading and/or abusive.
By way of further explanation consider the following:
Suppose that height and weight are positively related. A mathematical expression that would express this idea is W = f(H) where f'(H)>0 -- namely, W increases with positive increases in H. Unfortunately, W = f(H) is not something you can measure empirically, because you do not know the true nature of f. So, you must hypothesize what that true nature is -- namely, you must propose a functional form.
A straight line of the form W = aH + m, where a > 0, would probably be inappropriate, because short fat people can be heavier than tall thin people. Nevertheless, still you believe that taller people are generally heavier than short people. So, you might hypothesize a different functional form such as W = a•log(H) + m, which, although expressed as a line, actually forms an upward sloping curve when drawn on a two-dimensional sheet of paper.
Both W = aH + m and W = a•log(H) + m, where a > 0, are functional forms of the same mathematical expression W = f(H), where f'(H) >0.
Plot the height (H) and weight (W) for each individual on a pair of coordinate (X,Y) axes, where H and W are X and Y, respectively. What do you obtain? A scatter plot.
Perform two statistical runs that estimate the values of a and m for each of your two functional forms. Plot each of the resulting two curves (one will be a straight line) on your scatter plot. The curve (line) that matches the scatter plot best demonstrates the best fit! It will have the highest r-square value.
R is the correlation coefficient and r-square is the coefficient of determination. The latter tells you the percent of total variation explained by your model -- i.e., selected functional form.
--------------------------------------------------
Note added at 2 hrs 57 mins (2004-11-25 04:45:55 GMT)
--------------------------------------------------
Disclaimer: My full use of this and other forums has been restricted for reasons unknown, so please forgive my lack of direct support for answers offered by other contributors and critical assessment of non-contributors who are misleading and/or abusive.
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