.all Verizon 5:54 PM Done BEYOND THE NUMBERS 1.31 | LEARNING OUTCOMES 8 TO0 Read
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.all Verizon 5:54 PM Done BEYOND THE NUMBERS 1.31 | LEARNING OUTCOMES 8 TO0 Read All About It: Correlation and Causation Causation RELECT Read the following content material or watch the video that is available online There are questions at the end of this reading to answer and turn in. Introduction The point of this chapter may be one you are already quite familiar with. Said simply, two ariables could be highly associated, even exhibit a substantial correlation coefficient, if that calculation applied, but no causal link exist between them. We simply need to be aware that his has real implications for the inferences we might make. While a strong correlation might suggest causation, it is important that we learn to assess the credibility of any implied causation before making an inference about causation. Beer and Sex Let's look at an example. In April, 2000 the Centers for Disease Control did a study at the state level, recording for each state the Beer Tax (horizontal axis; scale suppressed) and the Gonorrhea Rate for that state (vertical axis, scale suppressed). These two variables are plotted in the scatterplot seen on the right. Amount of Beer Tax The two variables are clearly negatively associated, with states that have higher beer taxes also having lower gonorrhea rates. Since there are no scales on the axes we can't easily assess the strength of the association, but the existence of an association is quite clear There's nothing tricky about that part. The tricky part is what we do with that information. Let's see what the CDC did. Take another look at the scatterplot of "Gonorrhea Rates" versus "Beer Tax." Again, since we don't have access to the original data, we can't put the appropriate scales on the horizontal and vertical axes, but it is clear that for every unit moved to the right on the Beer Tax axis, there is a reduction in the Gonorrhea Rates. Using elementary methods of a statistical procedure known as "regression" the CDC was able to ascertain that for every twenty-cent move to the right on the Beer Tax axis, there would be a drop of 8.9% on the Gonorrhea Rates axis. (continued) 97 BEYOND THE NUMBERS 1.3Explanation / Answer
1) The CDC suggeted a cause relation between the gonorreha rate and the tax
They clearly state that by increasing the tax rate will cause a decrease in gonorreha rate by 8.9%
2) The position that Digital Bits Spektic took was that Fat people generally will turn to Diet soda to try and reduce weight
But the media will report that drinking Diet soda makes you fat
3) The work done at the UT Health Science suggests that artificial sweetner will actually trigger appetite and will stop the brain cells to let the person know that he/she is full
This implies that diet soda which has artificial sweetner will actually make the person obese by overeating
4) Sale of products like bread and butter together will have high correlation but just because the sale of butter goes up does not cause the sale of bread to go up
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