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Insurance. The idea of insurance is that we all face risks that are unlikely but carry high cost. Think of a fire or flood destroying your apartment. Insurance spreads the risk: we all pay a small amount, and the insurance policy pays a large amount to those few of us whose apartments are damaged. An insurance company looks at the records for millions of apartment owners and sees that the mean loss from apartment damage in a year is \(\mu=\$ 150\) per person. (Most of us have no loss, but a few lose most of their possessions. The \(\$ 150\) is the average loss.) The company plans to sell renters' insurance for \(\$ 150\) plus enough to cover its costs and profit. Explain clearly why it would be unwise to sell only 10 policies. Then explain why selling thousands of such policies is a safe business.

Short Answer

Expert verified
Selling only 10 policies carries high financial risk due to insufficient risk spread. Selling thousands allows the company to use the law of large numbers, ensuring predictability and profitability.

Step by step solution

01

Understanding the Risk Spread

Insurance spreads the risk among a large group of people. The idea is that while some people will experience high losses, most will have little to no loss. By selling insurance, the company collects small premiums from everyone and compensates those who incur large losses.
02

Analyzing Small Number of Policies

If the company sells only 10 policies, the risk is not well spread. Suppose even one or two clients suffer a massive loss, the company will need to pay out a large amount, which can far exceed the total premiums collected ( 10 imes $150 = $1500). This creates a high risk for the company due to insufficient funds to cover potential losses.
03

Analyzing Large Number of Policies

When the company sells thousands of policies, like 1000, it collects a substantial premium amount ( 1000 imes $150 = $150,000). With a large number of policies, the law of large numbers ensures that the actual average loss will be close to the expected average loss ( $150 per person). This provides stability, as the total premiums cover expenses and allow for profit.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Law of Large Numbers
The Law of Large Numbers is a principle that plays a crucial role in the insurance industry. It states that as the number of exposure units (e.g., people, houses) increases, the actual loss experience will more closely approximate the expected loss experience. This mathematical concept is essential for insurance companies as it reduces uncertainty. The key idea is that with a larger number of policies, the company can predict losses more accurately. A larger pool ofinsured individuals stabilizes the average loss, which becomes a predictable expense for the insurance company. Therefore, selling thousands of policies is safer because it helps to mitigate the variation in the loss outcomes. Instead of being swayed by a few outliers who experience high losses, the company relies on a stable average outcome.
Risk Spread
Risk spreading in insurance refers to the distribution of risk across a large number of policyholders. By collecting premiums from many individuals, the insurance company is able to pool risks, thereby reducing the financial impact of any single incident. When risks are spread over a large base, the financial strain caused by claims from individuals suffering losses is distributed among all policyholders. This allows the company to compensate for high losses suffered by a few with the premiums collected from many. In practical terms, this means the risk of any significant financial burden is shared collectively. This principle is what makes insurance viable and attractive to both consumers and insurers.
Expected Loss Calculation
Expected loss calculation is a key component in setting insurance premiums. It involves figuring out how much, on average, a policyholder is likely to lose due to an insured event.In the exercise, we see that the average loss per person is calculated to be \( \mu = $150 \) annually. To determine this, insurance companies analyze large datasets of previous claims to identify patterns. Using the expected loss, insurers set their premiums such that the sum collected covers anticipated losses. This ensures that when distributing compensation to those who suffer unfortunate events, the insurer remains financially stable while also making a profit.
Insurance Premiums
Insurance premiums are the amounts paid by policyholders to the insurer, typically on an annual basis. These premiums are not arbitrary; they are determined by several factors, including the expected loss and the insurer's need to cover its operating costs while making a profit.In our scenario, the basic premium is calculated as \( \$150 \), mirroring the average loss per insured unit. However, insurers must charge more than this base amount to cover administrative costs and contribute to profit margins. When calculating premiums, they consider not only the statistical likelihood of loss but also other factors like the expenses involved in claims processing and policy administration. This ensures the company can continue operations efficiently.

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Most popular questions from this chapter

The Bureau of Labor Statistics announces that last month it interviewed all members of the labor force in a sample of 60,000 households; \(3.5 \%\) of the people interviewed were unemployed. The boldface number is a a. sampling distribution. b. statistic. c. parameter.

Sampling Students. To estimate the mean score \(\mu\) of those who took the Medical College Admission Test on your campus, you will obtain the scores of an SRS of students. From published information, you know that the scores are approximately Normal, with standard deviation about \(10.6\). How large an SRS must you take to reduce the standard deviation of the sample mean score to 1 ?

Florida Voters. Florida has played a key role in recent presidential elections. Voter registration records in September 2019 show that \(37 \%\) of Florida voters are registered as Democrats and \(\mathbf{3 5} \%\) as Republicans. (Most of the others did not choose a party.) To test a random digit dialing device that you plan to use to poll voters for the 2020 presidential elections, you use it to call 250 randomly chosen residential telephones in Florida. Of the registered voters contacted, \(35 \%\) are registered Democrats. Is each of the boldface numbers a parameter or a statistic?

Sampling Distribution Versus Population Distribution. The 2018 American Time Use Survey contains data on how many minutes of sleep per night each of 9600 survey participants estimated they get. \(-\) The times follow the Normal distribution with mean \(529.2\) minutes and standard deviation \(135.6\) minutes. An SRS of 100 of the participants has a mean time of \(x=514.4\) minutes. A second SRS of size 100 has mean \(x=539.3\) minutes. After many SRSs, the many values of the sample mean \(x\) follow the Normal distribution with mean \(529.9\) minutes and standard deviation \(13.56\) minutes. a. What is the population? What values does the population distribution describe? What is this distribution? b. What values does the sampling distribution of \(x\) describe? What is the sampling distribution?

Playing the Numbers: A Gambler Gets Chance Outcomes. The law of large numbers tells us what happens in the long run. Like many games of chance, the numbers racket described in the previous exercise has outcomes that vary considerably-one three-digit number wins \(\$ 600\) and all others win nothing - that gamblers never reach "the long run." Even after many bets, their average winnings may not be close to the mean. For the numbers racket, the mean payout for single bets is \(\$ 0.60\) (60 cents), and the standard deviation of payouts is about \(\$ 18.96\). If Joe plays 350 days a year for 40 years, he makes 14,000 bets. a. What are the mean and standard deviation of the average payout \(x\) that Joe receives from his 14,000 bets? b. The central limit theorem says that his average payout is approximately Normal with the mean and standard deviation you found in part (a). What is the approximate probability that Joe's average payout per bet is between \(\$ 0.50\) and \(\$ 0.70\) ? You see that Joe's average may not be very close to the mean \(\$ 0.60\) even after 14,000 bets.

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