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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.
Multi co linearity is the term used in the regression analysis to indicate situations where the explanatory variables are related by a linear function, making the inference of the
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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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The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if |t | > t = 1.96
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