Do the following plots show 1. Constant variability 2. Nearly normal residuals 3. Independent observations for SLR (conditions for linear regression)
Do the following plots show
1. Constant variability
2. Nearly normal residuals
3. Independent observations for SLR
(conditions for linear regression)
Introduction -
Constant of variation -
Constant means to stay same , while variation refers to change . But as you will soon see , they make sense together in the world of math. In math , variation shows how one variable change sin relation to another variable. This relationship is usually expressed as a ratio . when we say that the variation is constant , we are saying that ratio remains the same . So when you see the term constant of variation just remember that it means the relationship between the variables does not change .
Nearly normal residuals - Nearly normal residuals condition : A histogram of the residuals looks roughly unimodal and symmetric .Equal variance Assumption . The variability in y is the same everywhere . By this we mean that all the Normal models of errors (at the different values of x) have the same standard deviation .
Independence of errors- There is not a relationship between the residuals and the Y variable in other words Y is independent of errors .Check this assumption by examining scatterplot of residuals versus fits , the correlation should be approximately 0 . In other words there should not look like there is a relationship .
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