Scatterplot: It is a graphical (pictorial)
interpretation of the linear correlation between two continuous (interval /
ratio) variables, predictive and outcome, plotted in X and Y axis respectively.
Why Scatterplot?
1.
Because describing a
relationship through a number is not enough, we need to look at the
relationship in a scatterplot or how points very (bunched-up) around regression
line in a scatterplot.
2.
Graph (scatterplot)
also helps in detecting the homoscedasticity (variability of points around the
regression line) in a relationship which is rectified through transformation.
3.
Graph (scatterplot)
tells about the outliers in the data,
which are the threats to the interpretation
and needs to be omitted.
4.
Scatterplot determines the situations where
the correlation is curvilinear. Simply reporting r = 0 might be misleading because a relation
still exists which might not be a linear one.
Two Characteristics
of a scatter-plot:
1.
The slope of the scatter-plot, and
2.
The degree to which the points in
the scatter-plot cluster around an imaginary line representing the slope.
These notes are written by S C Joshi during EPSY 635 Course, Fall 2015, Texas A&M University. Acknowledgements to Dr. Bob Hall, Professor, EPSY, Texas A&M University for his assistance in understanding these terms during the course
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