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Homoscedasticity - Reasons for Screening Data
Homoscedasticity is the assumption that the variability in scores for a continuous variable is roughly the same at all values of another continuous variable.
1. In the bivariate case, this is referred to as homogeneity of variances. Usually the Leven's test is the tool to assess the homogeneity of variances. This test is used to assess the hypothesis that assumes samples of observations come from populations from the same variances. Therefore rejecting it would imply heterogeneity of variances.
2. In multivariate analysis this is referred to Homoscedasticity. Homoscedasticity is related to the assumption of multivariate normality. Therefore bivariate scatterplots could be used to detect heteroscedasticity. Heteroscedastic relationship could also mean that one of the variables in the group of variables to be analyzed has a relationship with the transformation of the other variable.
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The probability distribution of the various observations is required to obtain the run of two successes in the series of Bernoulli trials with the probability of success equal to a
Principal factor analysis is the method of factor analysis which is basically equivalent to a principal components analysis performed on reduced covariance matrix attained by repl
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Generally the final stage of an exploratory factor analysis in which factors derived initially are transformed to build their interpretation simpler. Generally the target of the pr
Reasons for screening data Garbage in-garbage out Missing data a. Amount of missing data is less crucial than the pattern of it. If randomly
Pascal's triangle is an arrangement of numbers described by Pascal in his Traité du Triangle Arithmétique published in the year 1665 as 'The number in each cell is equal to in the
Non-identified response is a term used to signify censored observations in survival data, which are not independent of the endpoint of the interest. Such observations can happen f
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