Curve-Fit
Curve Fitting by Least Squares
免費Offers In-App Purchases
6.0for iPhone, iPad and more
Age Rating
Curve-Fit 螢幕截圖
About Curve-Fit
'CurveFit' uses regression analysis by the method of least squares to find best fit for a set of data to a selected equation.
The curve-fitting technique used in this app is based on regression analysis by the method of least squares. The free version fits a straight line through a data-set using least squares analysis.
One In-App purchase is required to fit the other equations to the data set:
Straight Line : Y = C0 + C1*X (free)
Power Curve : Y = C0 + X^C1 Exponential I : Y = C0 * EXP(C1*X)
Exponential II : Y = C0 * X * EXP(C1*X)
Hyperbolic : Y = (C0 + C1*X)/(1 - C2*X)
Square Root : Y = C0 + C1*SQRT(X)
Polynomial : Y = C0 + C1*X + --- + CN*X^N
Exponential Poly : Y = C0 * EXP(C1*X + --- +
Natural Log : Y = C0 + C1*(LN(X)) + --- +
Reciprocal : Y = C0 + C1/X + --- + CN/X^N
Most literature deals with least squares analysis for straight lines, 2nd degree polynomials, and functions that can be linearized. The input-data is transformed into a format that the can be put into linear forms with undetermined constants. These types of equations are applicable for least-squares regression.
The regression routine is needed for determining values for the set of unknown quantities C1, C2,- - - ,Cm in the equation:
Y = C1 x F1(X) + C2 x F2(X) + - - - + Cm x Fm(X)
The constants are determined to minimize the sum of squares of the differences between the measured values (Y1, Y2, - - - , Yn) and the predicted equation Yc = F(X) which is found by curve-fitting the given data.
The principle of least squares is to find the values for the unknowns C1 through Cm that will minimize the sum of the squares of the residuals:
n
∑(ri) = r12 + r22 + - - - + rn2 = minimum
i=1
This is done by letting the derivative of the above equation equal zero. Thereby there will be generated as many algebraic equations as given data points, and the number of equations will be larger than unknowns.
The curve-fitting technique used in this app is based on regression analysis by the method of least squares. The free version fits a straight line through a data-set using least squares analysis.
One In-App purchase is required to fit the other equations to the data set:
Straight Line : Y = C0 + C1*X (free)
Power Curve : Y = C0 + X^C1 Exponential I : Y = C0 * EXP(C1*X)
Exponential II : Y = C0 * X * EXP(C1*X)
Hyperbolic : Y = (C0 + C1*X)/(1 - C2*X)
Square Root : Y = C0 + C1*SQRT(X)
Polynomial : Y = C0 + C1*X + --- + CN*X^N
Exponential Poly : Y = C0 * EXP(C1*X + --- +
Natural Log : Y = C0 + C1*(LN(X)) + --- +
Reciprocal : Y = C0 + C1/X + --- + CN/X^N
Most literature deals with least squares analysis for straight lines, 2nd degree polynomials, and functions that can be linearized. The input-data is transformed into a format that the can be put into linear forms with undetermined constants. These types of equations are applicable for least-squares regression.
The regression routine is needed for determining values for the set of unknown quantities C1, C2,- - - ,Cm in the equation:
Y = C1 x F1(X) + C2 x F2(X) + - - - + Cm x Fm(X)
The constants are determined to minimize the sum of squares of the differences between the measured values (Y1, Y2, - - - , Yn) and the predicted equation Yc = F(X) which is found by curve-fitting the given data.
The principle of least squares is to find the values for the unknowns C1 through Cm that will minimize the sum of the squares of the residuals:
n
∑(ri) = r12 + r22 + - - - + rn2 = minimum
i=1
This is done by letting the derivative of the above equation equal zero. Thereby there will be generated as many algebraic equations as given data points, and the number of equations will be larger than unknowns.
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最新版本6.0更新日誌
Last updated on 2020年11月23日
歷史版本
Upgraded to latest IOS SDK
Minor Bug-fixes
Added 'Share' button to enable sharing, saving and printing of graph/data
Changed color-scheme to work in Dark Mode
Updated layout and fonts to work better with iPads (larger fonts/better layout)
Minor Bug-fixes
Added 'Share' button to enable sharing, saving and printing of graph/data
Changed color-scheme to work in Dark Mode
Updated layout and fonts to work better with iPads (larger fonts/better layout)
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Version History
6.0
2020年11月23日
Upgraded to latest IOS SDK
Minor Bug-fixes
Added 'Share' button to enable sharing, saving and printing of graph/data
Changed color-scheme to work in Dark Mode
Updated layout and fonts to work better with iPads (larger fonts/better layout)
Minor Bug-fixes
Added 'Share' button to enable sharing, saving and printing of graph/data
Changed color-scheme to work in Dark Mode
Updated layout and fonts to work better with iPads (larger fonts/better layout)
4.0
2019年02月26日
Curve-Fit FAQ
點擊此處瞭解如何在受限國家或地區下載Curve-Fit。
以下為Curve-Fit的最低配置要求。
iPhone
須使用 iOS 12.3 或以上版本。
iPad
須使用 iPadOS 12.3 或以上版本。
iPod touch
須使用 iOS 12.3 或以上版本。
Curve-Fit支持英文
Curve-Fit包含應用內購買。 以下為Curve-Fit應用內購買價格清單。
Curve-Fit
$2.99