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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 skewness is a measure of asymmetry and as it is positive at 4.29, it is greater than zero which reveals that the tail extends to the right indicating the distribution to be mor
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CONSTRUCTION OF AN OR MODEL
The approach to data analysis which emphasizes the use of informal graphical procedures not based on former assumptions about structure of the data or on the formal models for the
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