Question

PLEASE HELP!!!!!!!...... I HAVE ALSO ATTACHE THE LINK THAT QUESTING ONE IS TALKING ABOUT THANKS

See the Washington Post article link:  https://www.washingtonpost.com/news/food/wp/2017/06/16/those-french-fries-could-kill-you-a-new-study-says-but-dont-panic/?noredirect=on&utm_term=.4b2068acfb53

1. Researchers have collected data that shows a relationship between eating french fries and the risk of premature death. (See the Washington Post article ) Is it safe to assume, on the basis of these data, that eating french fries leads to premature death? Explain the rationale for your answer and include at least two alternative explanations for the reported results.

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2. Describe a scenario where the Pearson correlation would be appropriate to use in calculating the strength of the relationship between two variables. Identify the variables and explain the rationale for your answer.

3. Describe a scenario where the Spearman correlation would be appropriate to use in calculating the strength of the relationship between two variables. Identify the variables and explain the rationale for your answer. What makes this scenario different from the scenario in the previous question?

4. Give an example of how you might use a trend line to make a prediction about an outcome. Identify the variables and explain the rationale for using the trend line to predict the outcome.

5. Describe one limitation of using regression equations to make predictions about outcomes. Include an example to illustrate this.

Answer 1

I am answering Q2 and Q5

(2) Pearson Correlation:

A study may be taken up to see if the final grades of students in a Statistics class are correlated to how often they miss their homework deadlines. Can doing the assignments sincerely and within time have any effect on the final score of a student? This can be verified by calculating a Pearson coefficient for the bivariate data of number of home works not submitted or late submissions vs the final grade. It is expected that a correlation (if present) ought to be negative. That is, less the misses (more diligent a student is with the homework), higher his exam score.

(5) Limitations of regression:

The limitation of linear regression are :

(1) Linear regression is often inappropriately used to model non-linear relationships (due to lack in understanding when linear regression is applicable)

(2) Linear regression is limited to predicting numeric outputs only. It gives no qualitative information.

(3) It is meaningless to do a regression without knowing what to expect, at least roughly.

In making estimates from a regression equation, it is important to remember that the relationship has not changed since the regression equation was computed. It is also important to realize that the relationship shown by the scatter diagram may not be the same if the equation is extended beyond the values used in computing the equation. For example, there may be a close linear relationship between the yield of a crop and the amount of fertilizer used. It would not be logical, however to extend this equation beyond the limits of the experiment, because it is quite likely that too much fertilizer may in fact reduce the yield of the crop.