Regression and Correlation are the most used and most abused tools in research. People are quick to jump to conclusions that if a relationship exists between two variables, then one must cause the other.
There are many reasons why two variables can be related without causality. For example, there is a strong inverse relationship between the sale of ice cream and the number of reported cases of the flu -- does ice cream cure the flu? Of course not, but more ice cream is consumed in the summer, when the flu is minimized, thus there is a seasonal relationship, not a causal one. There is also a relationship between the seat belt lights turning on in an airplane and the existence of turbulence in flight, but the lights do not cause the turbulence.
Give a plausible explanation for the following correlations:
a. There is a strong relationship between the amount of money people spend and the amount people save (in other words, people who spend more tend to save more). Does this mean that you can improve your life savings by spending more money? Explain how this correlation is true.
b. There is a strong relationship between the number of cops on our streets and the number of reported crimes. Does this mean that cops commit crimes or that criminals are more bold when cops are on the streets? Explain how this correlation is true.
c. How does this apply or could apply to your workplace or home?
d. How does this apply or could apply to your daily use, business use, or personal use?© BrainMass Inc. brainmass.com March 4, 2021, 10:55 pm ad1c9bdddf
(a) At first glance, it appears strange that there should be a strong degree of correlation between amount of
money people save and that people spend. One possible explanation for this could be that, to do both
(spending money and saving money) you must have a lot of money in the first place. If you have too less
money, you will concentrate on saving (for the uncertain future) and if you have just enough you may spend
and not have ...
Complete and Correct Answers are provided with necessary explanation.