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# Res 342 Week 4 DQ 2

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Week 4 DQ 2: Does correlation equal causation? Does the strength of correlation
depend on the direction? What is the meaning of a zero correlation? What is the
difference between an independent and a dependent variable? Does a regression
model imply causation? Explain your answers.
Correlation may be described as the degree of association between two variables
(Asuero, Sayago and Gonzalez, 2006). We can say that the study of interdependence leads to
the investigation of correlations. The correlation charts, known as scatter diagrams is one of the
seven basic tools for statistical quality control. Correlation does not equal to causation, I just
checks if there is a linear relationship between two variables. As stated in chapter 12, a visual
display is a good first step in analysis but analysts or researchers would like to quantify the
strength of the association between the two variables as well (p. 490). The strength of
correlation does not depend on either direction (+ or -) and they can have the same degree of
association and more importantly, there is no flat rule for a “high” correlation because sample
size must be taken into consideration. The illustration of different type of correlations is on page
491 includes strong positive correlation, weak positive correlation, strong negative correlation,
and weak negative correlation. The meaning of a zero correlation refers to a situation in which
no linear correlation between two variables. The two variables do not co-vary in a clear pattern.
For example, one variable can go up while the other one can go down or may be up, we cannot
tell. The difference between an independent and a dependent variable is that its values does
not depend on the movement of another variable. On the other hand, the value of dependent
variable depends on the values of driving factors (i.e. independent variables, p. 500). A
regression model does not imply causation; it measures the degree of association between a
dependent variable and an independent variable in the presence of other independent
variables.
References
Asuero, A., Sayago, A., & Gonzalez, A. (2006). The correlation coefficient. A chemistry, 36(1),
41-59.
Doane, D. P. & Seward, L. E. (2007). Applied statistics in business and economics. Boston. MA.
McGraw-Hill/Irwin

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