Question

Discussion 1: Searching for Causes

This week examines how to use correlation and simple linear regression to test the relationship of two variables. In both of these tests you can use the data points in a scatterplot to draw a line of best fit; the closer to the line the points are the stronger the association between variables. It is important to recognize, however, that even the strongest correlation cannot prove causation.

For this Discussion, review this week’s Learning Resources and consider a true relationship between variables.

By Day 3

Post a brief explanation of when an observed correlation might represent a true relationship between variables and why. Be specific and provide examples.

Answer 1

consider an example.

if you collect India's steel production data and population of 10 consecutive year you will find a very high correlation which means the have a strong linear relationship..but does it indicate that steel production causes population or vice versa..the rationale behind this phenomenon is that both population and steel production is increasing over time..here both the variables doesn't depend on each other but they both are dependent on.time..so a high correlation never reflects a cause and effect relationship it only indicates an extent of linear interdependence..

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