You might also consider other geometry creation options. 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.The polytool demo has the following features: The polytool demo is an interactive graphic environment for polynomial curve fitting and prediction. You can export gometry related information from pdetool from the Draw menu, last option. 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. To start the demonstration, you must first load the data set. The variables x and y are observations made with error from a cubic polynomial. The variables x1 and y1 are data points from the "true" function without error. If you do not specify the degree of the polynomial, polytool does a linear fit to the data. 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. The bulk of the data with x-values between zero and two has a steeper slope than the fitted line. The two points to the right are dragging down the estimate of the slope. In the Degree box at the top, type 3 for a cubic model. Then, drag the vertical reference line to the x-value of 2 (or type 2 in the X Values text box). This graph shows a much better fit to the data. The confidence bounds are closer together indicating that there is less uncertainty in prediction. Our company manufactures electronic components in which various polymers are used. Include: matlab_support_toolbox_model_predictive_control include: matlab_support_toolbox_mapping include: matlab_support_toolbox_instrument_control include: matlab_support_toolbox_image_processing include: matlab_support_toolbox_image_acquisition include: matlab_support_toolbox_genetic_algorithms include: matlab_support_toolbox_fuzzy_logic include: matlab_support_toolbox_fixed_point include: matlab_support_toolbox_fixed_income include: matlab_support_toolbox_financial include: matlab_support_toolbox_financial_derivatives include: matlab_support_toolbox_filter_design_hdl_coder include: matlab_support_toolbox_excel_link include: matlab_support_toolbox_datafeed include: matlab_support_toolbox_database include: matlab_support_toolbox_data_acquisition include: matlab_support_toolbox_curve_fitting include: matlab_support_toolbox_control_systems include: matlab_support_toolbox_communications include: matlab_support_toolbox_bioinformatics include: matlab_support_toolbox_aerospace Identifier: '\b_]()=\s]* ' # non bracket characters (and also non-whitespace, parens) The data at both ends of the plot tracks the fitted curve.# We support unicode here because Python 3 is the future
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