What is the main difference between High-Low Method and Regression Analysis?

A) They are the same

B) Regression Analysis is used in aggressive periods, but High-Low Method is used always

C) High-Low Method is based on 2 periods, but Regression analysis consider all data so Regression analysis is more trustable

D) High-Low Method is based on high costs, but Regression analysis considers all costs

E) Regression Analysis is based on 2 periods, but High-Low Method considers all data

244 SOME SPECIAL DISTRIBUTIONS It is easy to show (Problem 2) that cov(X. Y) = a/x, so that var(2) = 2[1 + (a/x)] If Z is normal, its MGF must be (12) Next we compute the MGF of Z directly from the joint PDF (10). We have - + +12F() - 112F()- 1If()f()dxdy = +a "[2F() - 11/()dx Now "(2F() - 1/()dx = -2 "[1 - F(x)/(x)dx + 'n exp -s(x2 + (u + x)? - zx] expl-87 /2 + (v - 1)2/4] expl-[x + (# - 0)/217) -dx du du (13) where Z, is an A(0. 1) RV. It follows that (14) 1+a(1-28/21- #21)"2. Contour of bivariate Gaussian. Sketch the contour defined by f(x, y) = 0.06, where f(x, y) is the bivariate normal density with (a) Ax = 1, My = 2,02 = 03 = 1 and pry = 0. (b) Hz = 0, My = 0, 03 = of = 1 and Pry = 0.2. (c) #x = 0, My = 0,02 = 07 = 1 and Pxy = 0.8. (d) Ax = 0, My = 0,02 = 4, 0% = 1 and pry = 0.8. Here, o2 := VarX, o2 := VarY, and the correlation coefficient is defined as Pry := E(X -HI) ( Y-Hy) What is the distribution of X - Y, when (X, Y) has each of the bivariate distributions given above?2. Contour of bivariate Gaussian. Sketch the contour defined by f(x, y) = 0.06, where f(x, y) is the bivariate normal density with (a) Ax = 1, My = 2,02 = 03 = 1 and pry = 0. (b) Hz = 0, My = 0, 03 = of = 1 and Pry = 0.2. (c) #x = 0, My = 0,02 = 07 = 1 and Pxy = 0.8. (d) Ax = 0, My = 0,02 = 4, 0% = 1 and pry = 0.8. Here, o2 := VarX, o2 := VarY, and the correlation coefficient is defined as Pry := E(X -HI) ( Y-Hy) What is the distribution of X - Y, when (X, Y) has each of the bivariate distributions given above?18. A researcher want to examine the impact of multiple risk factors (e.g. gender, race, age, blood pressure and etc.) on the development of CVD and to assess potential confounding and/or effect modification, which of the following would be the most appropriate method? [HINT: the outcome variable is CVD diagnosis (measured as CVDo CVD)] A) Simple linear regression analysis B) Simple logistic regression analysis C) Multiple linear regression analysis D) Multiple logistic regression analysis rue/False lation