We introduced this chapter with the most recognizable experiment of chance, the

Question

We introduced this chapter with the most recognizable experiment of chance, the coin toss. The coin has two random outcomes, heads and tails But, this book is not about coin tossing per se. Provide two examples of business scenarios in which there are two distinct but uncertain outcomes. Randomness is everywhere. In our personal lives, we observe randomness with varying outdoor temperatures, our blood pressure readings, our commuting times, to school or work, and the scores of our favorite sports team. Businesses exist in a world of randomness in the forms of varying dimensions on manufactured parts, customers' waiting times, demand for products or services, prices of a company's stock, injuries in the workplace, and customers' abilities to pay off a loan. Probability theory is the branch of mathematics that describes random behavior, its advanced study entails high-level mathematics. However, as will discover, many of the key ideas are basic. Managers who assimilate these key ideas ate better able to cope me better decision makers. Of course, we never observe a probability exactly. We could always continue tossing the coin, for example. Mathematical probability is an idealization based on imagining what would happen in an indefinitely long series of trials. The best way to understand randomness is to observe random behavior-not only the long-run regularity hut the unpredictable results of short runs. You can do this with physical devices such as coins and dice, but computer simulations of random behav ior allow faster exploration. As you explore randomness, remember independent You must have a long series of independent trials. That is, the outcome of one trial must not influence the outcome of any other. Imagine a crooked gambling house where the operator of a roulette w heel can stop it where she chooses she can prevent the proportion of "red" from settling dow n to a fixed number. These trials are not independent. The idea of probability is empirical. Computer simulations start w ith given probabilities and imitate random behavior, but we can estimate a real-world probability only by actually observing many trials. Nonetheless, computer simulations are very useful because we need long runs of trials In situations such as coin tossing, the proportion of an outcome often requires several hundred trials to settle down to the probability of that outcome. Exploration of probability with physical devices is ty pically too time consuming. Short runs give only rough estimates of a probability.

Explanation / Answer

Situation 1: Suppose you have some money and you want to invest it in the share market(particularly in some section). In the next day you will get profit if your money is in the profitable part of the market or you face loss.So this is uncertain event; either you will make profit or loss.

Situation 2: Suppose you are the project supervisor of your company. A new client gave your group a new project involving his financial activities. He told that if you will be able to complete the job in 5 days he will give extra 150 dollar with the net 500 dollar, otherwise he will give only 400 dollar if the work will end in greater than 5 days. The project completion time is also uncertain as it depends on the involvement of every member in the group with other factors also. This process is also uncertain like the previous one.

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