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Linearity - Reasons for Screening Data
Many of the technics of standard statistical analysis are based on the assumption that the relationship, if any, between variables is linear. Measures of linear relationship such as the Pearson r, cannot detect any nonlinear relationship between variables.
In analyses that are somehow related to predicted values of variables, the analysis of linearity is primarily conducted by evaluating the residual plots. More specifically, this is done by looking at the standardized residual plots with residuals for each observation appearing on the horizontal axis and their standardized values along the vertical axis.
A second more crude method of assessing linearity is accomplished by inspecting the bivariate scatterplots. If the variables being analyzed are, both normally distributed and linearly related, then the resulting scatterplot would be of elliptical shape.
The problematic and enigmatic theory of an inference introduced by the Fisher, which extracts a probability distribution for the parameter on the basis of the data without having f
Likert scales is often used in the studies of attitudes in which the raw scores are based on the graded alternative responses to each of a series of queries. For instance, the sub
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The transformation of the Pearson's product moment correlation coefficient, r, can be given by The statistic z has the normal distribution with mean here ρ is the pop
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Codominance : The relationship between genotype at the locus and a phenotype to which it in?uences. If an individuals with heterozygote (such as, AB) genotype is phenotypically dif
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