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Code Listing by Antonio Trujillo-Ortiz

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Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), the subject of Model II regression is one on which research and controversy are continuing and...



The homogeneity of variances test is a useful tool in many scientific applications. Boos and Brownie (2004) and Conover et al. (1981) give a broad review.

Here, we developed an m-file as an alternative to the homogeinity of variances...



Levene's F test is used to test the null hypothesis that multiple population variances corresponding to multiplesamples are equal. Prior to the analysis the data are transforming to the absolute deviation of the mean. Then perform an one-way...



This m-file performs the Bowker's test for symmetry. It is an extension of the McNemar test to a KxK situation. There are now K response categories for the two dependent samples.

The null hypothesis is that the probabilities in the...



Probability of obtaining a Mann-Whitney's U of two random variables with continuous cumulative distribution. It's based on the Mann-Whitney (1947). This procedure is highly recommended for sample sizes n1 and n2 <=7. Although it gives an exact...



Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear...



Statistics fundamentals of the Correspondence Analysis (CA) is presented in the CORRAN and MCORRAN1 m-files you can find in this FEX author''s page. CA can be extended to more than two categorical variables, called Multiple Correspondence Analysis...



This file performs the Stuart-Maxwell's test for all the marginal homogeneity (i.e. across all the categories simultaneously) for each of the two sample times (nominal samples). Obviously, it need a square KxK table.

It is a...



This m-function returns the negative hypergeometric probability density function with parameters M, N and A at the values in X. Note: The density function is zero unless M, N and A are integers.

If a lot consists of M acceptable items...



Correspondence Analysis (CA) is a special case of Canonical Correlation Analysis (CCA), where one set of entries (categories rather than variables) is related to another set. Also, it can be seen as a special case of Principal Component Analysis...