Applications of Mathematical Expectation

Applications of Mathematical Expectation

Applications in Decision Making

Application 1: maximizing expected return Under uncertainty, a decision maker usually chooses the alternative with the largest expected return.

Application 2: risk assessment Risk is assessed by computing the expected loss.

Applications in Statistics

Application 3: the sample mean The sample mean is an unbiased estimator of the population expectation.

Application 4: parameter estimation Many statistical methods are based on the properties of expectation.

Cases of Practical Application

Case 1: Investment Decision

Suppose there are two investment alternatives:

  • Alternative A: a gain of 1000 yuan with probability 60%, a loss of 500 yuan with probability 40%
  • Alternative B: a gain of 300 yuan with probability 80%, a loss of 200 yuan with probability 20%

Compute the expected return of each alternative:

Expected return of alternative A: E(A)=0.6×1000+0.4×(−500)=600−200=400E(A) = 0.6 \times 1000 + 0.4 \times (-500) = 600 - 200 = 400

Expected return of alternative B: E(B)=0.8×300+0.2×(−200)=240−40=200E(B) = 0.8 \times 300 + 0.2 \times (-200) = 240 - 40 = 200

Therefore, alternative A has the higher expected return.

Case 2: Quality Control

On a production line, the pass rate of products is 95%. For every 100 products produced, the expected number of qualified products is: E(X)=100×0.95=95E(X) = 100 \times 0.95 = 95

Case 3: Insurance Pricing

Insurance companies compute the expected loss of a certain class of accidents from historical data and set premiums on that basis.

Exercises

Exercise 1

Given that X,YX, Y are independent with E(X)=1,E(Y)=2E(X)=1, E(Y)=2, find E(X+Y)E(X+Y).

mathematical expectationlinearity of expectationcomputation

Approach: Use the linearity of mathematical expectation.

Steps:

  1. E(X+Y)=E(X)+E(Y)E(X+Y) = E(X) + E(Y)
  2. =1+2=3= 1 + 2 = 3
  3. The linearity of expectation holds for any random variables, so independence is not required as a premise in this step.

Answer: E(X+Y)=3E(X + Y) = 3.

Exercise 2

A store sells a certain product. The profit per item is 10 yuan, and the distribution of the number of items sold is:

  • Probability of selling 1 item: 0.3
  • Probability of selling 2 items: 0.5
  • Probability of selling 3 items: 0.2

Find the expected profit.

mathematical expectationapplication problemexpected profit

Approach: First compute the expected number of items sold, then multiply by the profit per item.

Steps:

  1. Let the number of items sold be XX; then: E(X)=1×0.3+2×0.5+3×0.2=0.3+1.0+0.6=1.9E(X) = 1 \times 0.3 + 2 \times 0.5 + 3 \times 0.2 = 0.3 + 1.0 + 0.6 = 1.9

  2. Expected profit = E(X)×10=1.9×10=19E(X) \times 10 = 1.9 \times 10 = 19 yuan

Answer: 19 yuan

Exercise 3

The pass rate of the products of a factory is 90%. Unqualified products need to be reworked, at a rework cost of 50 yuan. The production cost of each item is 100 yuan. Find the expected cost per item.

mathematical expectationapplication problemexpected cost

Approach: Consider the two cases of a qualified product and an unqualified product.

Steps:

  1. Cost of a qualified product: 100 yuan
  2. Cost of an unqualified product: 100 + 50 = 150 yuan
  3. Expected cost: E(C)=0.9×100+0.1×150=90+15=105E(C) = 0.9 \times 100 + 0.1 \times 150 = 90 + 15 = 105 yuan

Answer: 105 yuan

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