Regression and Correlation are two of the most often used and abused tools in research. People...

Regression and Correlation are two of the most often used and abused tools in research. People are quick to jump to conclusions that if a relationship (correlation) exists between two variables, then one must cause (causation) the other. There are many reasons why two variables can be related without causality. Please respond to the following:

Comparing the amount of money people spend and the amount people save, your analysis revealed an R-squared = 0.97. Should you use this for predictive purposes and why?

Comparing the number of cops on our streets and the number of reported crimes, your analysis revealed an R-squared = 0.40. Should you use this for predictive purposes and why?

How could this apply in your profession?

Expert Solution

1. Here in the first case R-squared value is 0.97 on the regression between money spend and money save.

which implies that the amount of money saved and the amount of money spend is highly related.

But we can not use this for predicted purpose, because the sum of the amount of money spend and save is

equal to the total money income by the people. i.e there is a relationship between the money saved ,money spend and

the persons income. So, the amount of money and the money spend are highly related.

but it may happen that a persons income may less but he spend lots of money while save less, where as a persons

income is high and he spend the same amount of money but also save high amount of money. But regression line will

predict that both of them save same amount of money.

2. Though in the 2nd case the no of cops is not related to the number of crimes reported since the R-squared value is

vary low 0.40. But if the no of crops is increase then the no. of crime reported will be high. i.e they are directly related.

Here the R-squared is low so the model is going to predicted vary badly. this is why we can not use this model to predict.

orchestra answered 2 years ago

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