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
15. Under which decision-making condition is the decision maker unaware of all the alternatives and the risks and consequences associated with each alternative? a. Probability b. Risk c. Certainty d. Rationality e. Uncertainty 16. When the manager understands the available options but the probabilities associated with each option are uncertain, the manager is experiencing a. decision making under risk. b. satisficing. c. bounded rationality. d. decision making under certainty. e, decision making under uncertainty, 17. Decisions that are routine and deal with situations in which the factors are familiar and have occurred in the past are called decisions. a. nonprogrammed b. satisficing C. rational d. programmed e. noninnovative 18. A(n) group is the most common form of a decision-making group. a. interacting b. Delphi c. nominal d. unilateral e. task 19. _ is the act of choosing one alternative from many. a. Decision making b. Problem solving c. Risk assessment d. Programmed decision making c. Certainty evaluationQuestion Two (6 Marks): Your organization is considering the use of group decision making. You have read about decision making and are trying to inform your manager about the advantages and disadvantages of group decision making. Give TWO examples to explain when group decision making is recommended over individual decision making and TWO examples when individual decision making is better.8. An important distribution in the multivariate setting is the multivariate normal distribution. Let X be a random vector in RK. That is X1 X2 X = X ... with X1, X2, ..., Xx random variables. If X has a multivariate normal distribution, then its joint pdf is given by 1 (x) = (27)k/2 ( det 2) 1/2 exp -2(x -1) 72-1(x-1) with parameters #, a vector in R", and E, a matrix in R*x*. > is the covariant matrix. Note that det > is the determinant of matrix E. And E(X1 ) E(X 2) H = E(Xk) (a) Consider the bivariate case where k = 2. Let of and o2 be the standard deviations of r.v. X1 and X2 respectively. Let p be their correlation coefficient. Determine the form of E, the covariate matrix, in terms of 61, 02 and p12. (b) If X] ~ N(0, 3), X2 ~ N(0, 4), p = -1/4 and it is known that X = [X1 X2]" has a bivariate normal distribution. Determine the joint pdf of (X1, X2)
