Curve-Fit
Curve Fitting by Least Squares
GratisOffers In-App Purchases
6.0for iPhone, iPad and more
Age Rating
لقطات الشاشة لـ Curve-Fit
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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تحديث لأحدث إصدار 6.0
Last updated on 23/11/2020
الإصدارات القديمة
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)
Show More
Version History
6.0
23/11/2020
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
26/02/2019
Curve-Fit FAQ
انقر هنا لمعرفة كيفية تنزيل Curve-Fit في بلد أو منطقة محظورة.
تحقق من القائمة التالية لمعرفة الحد الأدنى من المتطلبات Curve-Fit.
iPhone
Requiere iOS 12.3 o posterior.
iPad
Requiere iPadOS 12.3 o posterior.
iPod touch
Requiere iOS 12.3 o posterior.
Curve-Fit هي مدعومة على اللغات Inglés
في Curve-Fit عمليات شراء داخل التطبيق. يرجى التحقق من خطة الأسعار على النحو التالي:
Curve-Fit
USD 2.99