Indicate whether each phrase is more descriptive of financial accounting or managerial accounting.

(a) May be subjective

Financial accounting

Managerial accounting

(b) Often used to obtain financing

Financial accounting

Managerial accounting

(c) Typically prepared quarterly or annually

Financial accounting

Managerial accounting

(d) May measure time or customer satisfaction

Financial accounting

Managerial accounting

(e) Future oriented

Financial accounting

Managerial accounting

(f) Has a greater emphasis on cost-benefit analysis

Financial accounting

Managerial accounting

(g) Keeps records of assets and liabilities

Financial accounting

Managerial accounting

(h) Highly aggregated statements

Financial accounting

Managerial accounting

(i) Must conform to external standards

Financial accounting

Managerial accounting

(j) Special-purpose reports

Financial accounting

Managerial accounting

(k) Decision-making tool

Financial accounting

Managerial accounting

(l) Income statement, balance sheet, and statement of cash flows

Financial accounting

Managerial accounting

(1) Let f : [n] r [n] be a permutation. Axed point of f is an element x E [n] such that f(x) = x. Now consider random permutations of [n] and let X be the random variable which represents the number of xed points of a given permutation. {a} What is the probability that X = 0? {b} What is the probability thatX = n 2? (c) What is the probability that X = n 1? {d} What is the expectation of X? (Hint: As usual, express X as the sum of wellchosen indicator variables and use the linearity of expectation.) (e) Let tk be the number of permutations of [n] that have exactly I: fixed points. Prove that 23:\" ktk = n!. (Hint: Use parts {aHd) of the problem and consider two different expressions for E 00.) 5. Let X1, ..., Xn be a random sample from Uniform(0, 0) with an unknown endpoint 0 > 0. We want to estimate the parameter 0. (a) Find the method of moments estimator (MME) of 0. (b) Find the MLE 0 of 0. (c) (R) Set the sample size as 25, do a simulation in R to compare these two esti- mators in terms of their bias and variance. Include a side-by-side boxplot that compares their sampling distributions