\fNPar Tests [DataSet]] C: \\Users\\gmb g\\Documents\\WFC. sav One-Sample Kolmogorov-Smirnov Test WFC N 77 Normal Parameters .b Mean 2.8365 Std Deviation 69369 Most Extreme Differences Absolute 138 Positive 138 Negative .086 Kolmogorov-Smirnov Z 1.208 Asymp Sig. (2-tailed) 108 a. Test distribution is Normal b. Calculated from data. Sn search WWe conducted 2 independent randomised experiments on mice separately to determine whether exposure to excess UV {ultraviolet} light would cause cancer among young and old mice. The following data were tabulated. {\"Healthy" means no cancer. and "Normal\" means no excess {a} What are the explanatory variable and response variable here? {1:} Calculate the relative risk of \"Cancer\" for excms to normal UV exposure in young mice. (c} Calculate the relative risk of \"Genes? for excess to normal UV exposure in old mice. {d} Calculate the relative risk of \"Cancer" for to normal UV exposure in all mice combined (no need to adjust for confounding variable}. {e} Do you think \"Simpson's Paradox" is at play here? {i} If your answer to part (d) is yes, give a plausible reason why this is so. Ifyonr answer is no, explain why not. {g} Based on the relative risk concept, do you believe the 2 random samples above suggest there is a practically signicant relationship between \"Excess" UV exposure and \"(L'anoel separately for young and old mice? Briefly explain. Now suppose the population regression function can be written as: Ay = , + BAr + As , where EA = 0, but E A& | Ax #0. Furthermore, suppose the econometrician has an instrumental variable, Az , such that: cov(As, Az )=0 and cov(Ax, Az) # 0. (AZ, - AZ AV, Demonstrate that the instrumental variables slope estimator. B, = f=1 , is biased. (AZ, - Az Ax