Explain the following statement -- Correlation does not imply causality. Why is this important for internal validity?
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Q: None
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- Olivia sees that in her data there is a relationship between regular exercise and a range of health outcomes, with those doing exercise having better outcomes. Olivia can claim which of the following statements A. there is evidence of a correlation between regular exercise and health outcomes in her group of UTS academics B. there is evidence of a causal relationship between regular exercise and health outcomes in the population of UTS academics C. there is evidence of a causal relationship between regular exercise and health outcomes in her group of UTS academics D. there is evidence of a correlation between regular exercise health outcomes in the population of UTS academics O She can claim only statement A O She can claim only statement C She can claim all statements O She can claim statements A and C O She can claim statements A and D She can claim statements B and CC. Give an example for each of the following: a. Two variables in which you have enough reasons to explain their cause-and-effect relationship. State the independent (x) and dependent variable (y). Provide also description of the cause-and-effect relationship. b. Two variables from which a considerable possible nature of linear correlation may exist. Explain briefly the possible nature of the relationship. State both variables and give a short description of their relationship.The data provides information on life expectancy and the number of televisions per thousand people in a sample of 22 countries, as reported by the world almanac and book of facts 2006 A. Describe the Direction and strength of the correlation between the two variables B. Because of the association between the variables, someone might mistakenly conclude that simply sending televisions to the countries with the lower life expectancies would cause their inhabitants to live longer. Comment on this argument and the reason televisions are likely associated with longer life expectancies C. In general, if two variables are strongly associated, does it follow there must be a cause and effect relationship between them? Explain
- Answer true or false to the following statement and provide a reason for your answer: If there is a very strong positive correlation between two variables, a causal relationship exists between the two variables.In a dataset, X is the independent variable, and Y is the dependent variable. Which of the following statement is correct? None of these Regression between X and Y determines the nature of the relationship between the variables. Regression between X and Y determines whether there exists any relationship between the variables. Correlation between X and Y determines the nature of the relationship between the variables.If the linear association between two numerical variables is studied and the correlation coefficient is 0.32, it can be concluded that:a. The relationship between the variables is inverse and weak. b. The percentage of variability observed in the data that is explained by the error is 89.76%. c. The percentage of observed variability in the data that is explained by the model is 32%. d. None is correct Please explain clearly, thank you
- why are correlations useful in econometrics?A student noticed that when she gets less than 8 hours of sleep at night, she scores lower on her test grades. Can one conclude that this positive correlation shows a causal relationship? OYes, because it is well known that less sleep lowers test scores OYes, because it is not a negative correlation O No, because all students get less than 8 hours of sleep at night No, because it is possible that the student gets lower test grades because of another factor, such as not studyingThe question is…are pre and post shows a dependent test or an independent test? Explain why.
- If 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.What is a reason why correlation does not imply causation? O Because correlations are not based on data. O Because you do not know if X or Y occurred first. O There could be a third variable that explains both X and Y. O Both b and cAfter 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 correlation
