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There is a curve in there that’s why linearity is not met, and secondly the residuals fan out in a triangular fashion showing that equal variance is not met as well. In the picture above both linearity and equal variance assumptions are violated. Equal variance assumption is also violated, the residuals fan out in a “triangular” fashion. Linearity assumption is violated – there is a curve. It also meets equal variance assumption because we do not see the residuals “dots” fanning out in any triangular fashion. It is linear because we do not see any curve in there. In the above picture both linearity and equal variance assumptions are met. If the residuals do not fan out in a triangular fashion that means that the equal variance assumption is met. y on the vertical axis, and the ZRESID (standardized residuals) on the x axis. We can say that this distribution satisfies the normality assumption.Įquality of variance: We look at the scatter plot which we drew for linearity (see above) – i.e. Even though is slightly skewed, but it is not hugely deviated from being a normal distribution. If the residuals are not skewed, that means that the assumption is satisfied. Normality: we draw a histogram of the residuals, and then examine the normality of the residuals. In cross sectional datasets we do not need to worry about Independence assumption. Longitudinal data set is one where we collect GPA information from the same student over time (think: video). For example we collect IQ and GPA information from the students at any one given time (think: camera snap shot) Cross -sectional datasets are those where we collect data on entities only once. We generally have two types of data: cross sectional and longitudinal. Longitudinal dataset is one where we collect observations from the same entity over time, for instance stock price data – here we collect price info on the same stock i.e. Independence – we worry about this when we have longitudinal dataset. not a curvilinear pattern) that shows that linearity assumption is met. If the scatter plot follows a linear pattern (i.e. Linearity – we draw a scatter plot of residuals and y values. Y values are taken on the vertical y axis, and standardized residuals (SPSS calls them ZRESID) are then plotted on the horizontal x axis.