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. The variables x1 and y1 are data points from the "true" function without error. 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. An Export list box to store fit results into variables.
A Close button to end the demonstration.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.A data entry box to change the degree of the polynomial fit.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.Partial least squares (PLS) constructs new. When you fit multivariate linear regression models using mvregress, you can use the optional name-value pair 'algorithm','cwls' to choose least squares estimation. The polytool demo has the following features: Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage. The polytool demo is an interactive graphic environment for polynomial curve fitting and prediction. Demos (Statistics Toolbox) Statistics Toolbox
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