#F1 help file equivalent for mac matlab how to#
I cant see how to get the C code equivalent of an m-file, without wrappers. Here's a screencast illustrating a theoretical p-th percentile. environment-specific details are included (include files, etc etc), because I havent written C for many years, and in particular Ive forgotten how to do it with Matlab.
The theoretical p-th percentile of any normal distribution is the value such that p% of the measurements fall below the value. On Mac OS X, use the COMSOL with MATLAB application available in the. If the resulting plot is approximately linear, we proceed assuming that the error terms are normally distributed. highlight a node or window, then press F1 to open the Help window, which then. Being a programming font, Fira Code has fantastic support for ASCII/box drawing. File folder containing many of the technical references cited in the MAC/GMC 4.0 manuals in PDF format: macpost: File folder containing MACPOST files required for MAC/GMC 4.0 results data post-processing using the MSC/PATRAN software package: matlab: File folder containing the MATLAB source files required for MAC/GMC 4.0 results data post. As I said, the other helpful things Matlab provides, like the variable editor, code completion, pressing F1 for documentation and so on, are nowhere to be seen. Extracted other features and classes into separate files, fixed few l. The ease of doing those same things is another matter. Since we are concerned about the normality of the error terms, we create a normal probability plot of the residuals. Besides that, in the sense of doing everything that Matlabs command window does, I guess its fairly decent. Here's the basic idea behind any normal probability plot: if the data follow a normal distribution with mean \(\mu\) and variance \(σ^\), then a plot of the theoretical percentiles of the normal distribution versus the observed sample percentiles should be approximately linear.
In this section, we learn how to use a " normal probability plot of the residuals" as a way of learning whether it is reasonable to assume that the error terms are normally distributed. Recall that the third condition - the "N" condition - of the linear regression model is that the error terms are normally distributed.
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