The data at both ends of the plot tracks the fitted curve. The confidence bounds are closer together indicating that there is less uncertainty in prediction. This graph shows a much better fit to the data. Then, drag the vertical reference line to the x-value of 2 (or type 2 in the X Values text box). In the Degree box at the top, type 3 for a cubic model. The two points to the right are dragging down the estimate of the slope. The bulk of the data with x-values between zero and two has a steeper slope than the fitted line.
If you do not specify the degree of the polynomial, polytool does a linear fit to the data. Added ability to zoom in domain with mouse click and mouse wheel rotation. The variables x1 and y1 are data points from the "true" function without error. PolyTool, version 0.2 Fixed bug in display of cursor location. The variables x and y are observations made with error from a cubic polynomial. To start the demonstration, you must first load the data set. You can use polytool to do curve fitting and prediction for any set of x-y data, but, for the sake of demonstration, the Statistics Toolbox provides a data set ( polydata.mat) to teach some basic concepts.
Bounds and Method menus to control the confidence bounds and choose between least squares or robust fitting.A draggable vertical reference line to do interactive evaluation of the polynomial at varying x-values.A data entry box to evaluate the polynomial at a specific x-value.The denominator should then be 2 n + 1 and you wind up subtracting 1 at the end. As I read it you are off by 1 because with 0 iterations you already know to root to b a 2 if you take your estimate to be the center of the interval. A data entry box to change the degree of the polynomial fit. 1 Answer Sorted by: 2 Your approach is fine.y-axis text to display the predicted y-value and its uncertainty at the current x-value.A graph of the data, the fitted polynomial, and global confidence bounds on a new predicted value.The polytool demo has the following features:
The polytool demo is an interactive graphic environment for polynomial curve fitting and prediction. Demos (Statistics Toolbox) Statistics Toolbox
0 kommentar(er)
