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2021
In this final chapter we demonstrate some of the under-appreciated non-parametric tests. These are the cousins (not siblings) of the parametric ones. For example, if your dataset does not meet the assumptions for the t-test (parametric), then Mann-Whitney test (non-parametric) in this section can be an alternative for you.
Saiyidi Mat Roni +1 more
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In this final chapter we demonstrate some of the under-appreciated non-parametric tests. These are the cousins (not siblings) of the parametric ones. For example, if your dataset does not meet the assumptions for the t-test (parametric), then Mann-Whitney test (non-parametric) in this section can be an alternative for you.
Saiyidi Mat Roni +1 more
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Non-Parametric Regression Methods
Computational Management Science, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Non-parametric Statistical Methods
1987Basic statistics and econometrics courses stress methods based on assuming that the data or error term in regression models follow the normal distribution. Indeed, the efficiency of least squares estimates relies on the assumption of normality. In order to lessen the dependence of statistical inference on that assumption statisticians developed methods
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1986
The coefficient rs is calculated as \(1 - \frac{{6\Sigma {d^2}}}{{n({n^2} - 1)}}\) where n is the number of observations in each of two series and d is the difference between the ranks of the corresponding observations in each series.
J. Murdoch, J. A. Barnes
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The coefficient rs is calculated as \(1 - \frac{{6\Sigma {d^2}}}{{n({n^2} - 1)}}\) where n is the number of observations in each of two series and d is the difference between the ranks of the corresponding observations in each series.
J. Murdoch, J. A. Barnes
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2011
The t-tests reviewed in the previous chapter are suitable for studies with normally distributed results. However, if there are outliers, then the t-tests are not sensitive and non-parametric tests have to be applied. We should add that non-parametric are also adequate for testing normally distributed data. And, so, these tests are, actually, universal,
Ton J. Cleophas, Aeilko H. Zwinderman
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The t-tests reviewed in the previous chapter are suitable for studies with normally distributed results. However, if there are outliers, then the t-tests are not sensitive and non-parametric tests have to be applied. We should add that non-parametric are also adequate for testing normally distributed data. And, so, these tests are, actually, universal,
Ton J. Cleophas, Aeilko H. Zwinderman
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2000
Parametric tests require some specific conditions about the distributions of scores in the populations of interest. When these conditions cannot be formally tested, researchers assume that they exist. The interpretation of the results derived from parametric tests relies heavily on these requirements not being seriously violated. When these assumptions
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Parametric tests require some specific conditions about the distributions of scores in the populations of interest. When these conditions cannot be formally tested, researchers assume that they exist. The interpretation of the results derived from parametric tests relies heavily on these requirements not being seriously violated. When these assumptions
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2012
Beginning statistics students are usually introduced to what are called “parametric” statistics methods. Those methods utilize “models” of score distributions such as the normal (Gaussian) distribution, Poisson distribution, binomial distribution, etc.
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Beginning statistics students are usually introduced to what are called “parametric” statistics methods. Those methods utilize “models” of score distributions such as the normal (Gaussian) distribution, Poisson distribution, binomial distribution, etc.
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