The scientific Reverse Polish Notation calculator can be used for mathematical calculations and as a tool to input data points into datasets. The calculator has hundreds of unit conversions built in to make complex calculations simple.

Used the Dataset Module to determine “Best Fit” linear or polynomial equations for sets of data with this IOS universal device application. It has the ability to store multiple datasets, calculate the goodness of fit, graph the given values verses the predicted values and calculate predicted values from the user’s input.

It is especially useful for scientists and science students because there are two modes of curve fitting: Calibration mode and Standard mode. In the Calibration Mode, an inverse prediction of X (abscissa) from Y (ordinate) is performed. As an example, if x is standard concentration values in mg/L and y is absorbance, then you can predict the concentration in mg/L from any absorbance value. The application derives X in second and third degree polynomials by using equation roots. The standard mode is available to fit datasets where high (up to 9th degree) degree polynomial fits are required or the Y needs to be predicted from X.

Both modes provide the goodness of fit by calculating the correlation coefficient (r^2) and the relative percent difference (RPD) between the measured values and the predicted values.

For best results, when constructing a calibration curve space the standard values equally. This is to avoid high-leverage point error. For instance, if you perform a linear least squares fit of a set of data, the fitted line will always pass though the average X, average Y point. If the points are spaced equally then they will contribute equally to the fit. Also, do not use higher degree polynomials to compensate for detector saturation. In either mode, known values (for instance, standard concentration values) should be on the x-axis and the response variable (eg. instrument response measured with error) is on the y-axis. With this setup, the measurement errors are in the vertical direction and the least squares criterion works optimally.

Please note that the data is sorted by X values in the database and this affects the analysis and graphing functions. So, consider this when choosing modes.

**Click for Instructions: CalcuCrvFitUserGuide**

CalcuCrvFit App Store Link

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