Concept explainers
You may have heard it said before that “
Do some research and find some interesting, or even funny, examples of spurious correlation. Share, cite your source, and discuss. Why is this an example of spurious correlation? How do you know?
Trending nowThis is a popular solution!
Step by stepSolved in 2 steps
- just the multiple choice part thank youarrow_forwardThe following statement contains an error. Choose the statement that best explains the error. "The correlation between shoe size and height is 0.87 inches" A. Correlation requires that both of the variables be categorical B. When stating the correlation coefficient, one must state whether it is a positive or negative relationship C. This statement does not tell us whether or not shoe size is correlated with height D. When reporting correlation, one does not report units because correlation has no units E. There is no error in this statementarrow_forwardWould A be the answer?arrow_forward
- True or false .If there is no linear correlation between enrollment and the number of burglaries , then those two variables are not related in anyway. Explain why ?arrow_forwardWhat is another possible explanation for this correlational relationship? Since this is just a correlation, we can’t be sure if age causes changes in your field goal percentage. So, what is one possible other explanation that you can come up with for the correlation that you calculated (hint: think about possible "third variable" explanations for this correlation---what other variables or factors might be causing changes in both x and y that could explain this correlation?).arrow_forwardIf when measuring two quantitative variables on an individual, we find that increases in the first variable x correspond to decreases in the second variable y, we say that A. there is a positive association between the two variables. b. there is a negative association between the two variables. c. there is no correlation between the two variables. d. there is a cause-and-effect relationship between the two variables.arrow_forward
- GRC often rely heavily on raising money for an "annual fund" to support operations. Alumni are typically solicited for donations to the annual fund. Studies suggest that the graduate's annual income is a good predictor of the amount of money he or she would be willing to donate, and there is a reasonably strong, positive, linear relationship between these variables. In the studies described: O a. annual income is the predictor variable O b. the correlation between annual income and the size of the donation is negative. O c. the size of the donation to the annual fund is the predictor variable O d. All of the above O e. None of the abovearrow_forwardWhat is another possible explanation for this correlational relationship? Since this is just a correlation, we can’t be sure if age causes changes in your field goal percentage. So, what is one possible other explanation that you can come up with for the correlation that you calculated (hint: think about possible "third variable" explanations for this correlation---what other variables or factors might be causing changes in both x and y that could explain this correlation?).arrow_forwardThe results from a research study indicate that adolescents who watch more violent content on television also tend to engage in more violent behavior than their peers. The correlation between amount of TV violent content and amount of violent behavior is an example of Question 25 options: a positive correlation a negative correlation a correlation near zero a correlation near onearrow_forward
- If we find that there is a linear correlation between the concentration of carbon dioxide in our atmosphere and the global temperature, does that indicate that changes in the concentration of carbon dioxide cause changes in the global temperature? Choose the correct answer below. A. No. The presence of a linear correlation between two variables does not imply that one of the variables is the cause of the other variable. B. Yes. The presence of a linear correlation between two variables implies that one of the variables is the cause of the other variable.arrow_forwardAfter gathering data about the number of starfish and measuring the pollution in areas of the ocean you find a negative linear correlation between pollution levels and number of starfish. What can you conclude based on this information? a. There is a confounding variable that is affecting both pollution and starfish. b. As pollution rises the number of starfish falls c. That pollution is causing starfish to die, leading to the negative correlation d. That pollution is supporting starfish, leading to the negative correlationarrow_forward
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
- Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman