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Refer back to Example, Two cars with six-cylinder engines and three with four-cylinder engines are to be driven over a \(300\)-mile course. Let \({X_1}, . . . {X_5}\)denote the resulting fuel efficiencies (mpg). Consider the linear combination

\(Y = \left( {{X_1} + {X_2}} \right)/2 - \left( {{X_3} + {X_4} + {X_5}} \right)/3\)

which is a measure of the difference between four-cylinder and six-cylinder vehicles. Compute \(P\left( {0 \le Y} \right)\)and\(P(Y > - 2)\).

Short Answer

Expert verified

\(\begin{array}{l}P(Y \ge 0) = 0.2877\\P( - 1 \le Y \le 1) = 0.3686\end{array}\)

Step by step solution

01

Definition of Standard Deviation

The standard deviation is a statistical measure of how much a set of values varies or disperses. A low standard deviation implies that the values are close to the set's mean (also known as the anticipated value), whereas a large standard deviation shows that the values are spread out over a wider range.

02

Calculation for finding the value of probability.

The given random variable can be written as

\(Y = \frac{1}{2}{X_1} + \frac{1}{2}{X_2} - \frac{1}{3}{X_3} - \frac{1}{3}{X_4} - \frac{1}{3}{X_5}\)

It is normally distributed with mean value of

\(\begin{aligned}E(Y) &= E\left( {\frac{1}{2}{X_1} + \frac{1}{2}{X_2} - \frac{1}{3}{X_3} - \frac{1}{3}{X_4} - \frac{1}{3}{X_5}} \right)\\ &= \frac{1}{2}E\left( {{X_1}} \right) + \frac{1}{2}E\left( {{X_2}} \right) - \frac{1}{3}E\left( {{X_3}} \right) - \frac{1}{3}E\left( {{X_4}} \right) - \frac{1}{3}E\left( {{X_5}} \right)\\ &= - 1\end{aligned}\)

(1): the expected values are given in the mentioned example.

The variance of random variable Y is

\(\begin{aligned}V(Y) &= V\left( {\frac{1}{2}{X_1} + \frac{1}{2}{X_2} - \frac{1}{3}{X_3} - \frac{1}{3}{X_4} - \frac{1}{3}{X_5}} \right)\\ &= \frac{1}{4}V\left( {{X_1}} \right) + \frac{1}{4}V\left( {{X_2}} \right) + \frac{1}{9}V\left( {{X_3}} \right) + \frac{1}{9}V\left( {{X_4}} \right) + \frac{1}{9}V\left( {{X_5}} \right)\\ &= 3.167\end{aligned}\)

(2): the variances are given in the mentioned example.

03

Calculation for finding the value of probability.

The standard deviation of random variable Y is

\({\sigma _Y} = \sqrt {V(X)} = \sqrt {3.167} = 1.7795\)

The probability of event \(\{ Y \ge 0\} \)is

\(\begin{aligned}P(Y \ge 0) &= P\left( {\frac{{Y - E(Y)}}{{{\sigma _Y}}} \ge \frac{{0 - ( - 1)}}{{1.7795}}} \right)\\ &= P(Z \ge 0.56)\\ &= 1 - P(Z < 0.56)\\ &= 0..2877\end{aligned}\)

(3): from the normal probability table in the appendix. The probability can also be computed with software.

04

Calculation for finding the value of probability.

Similarly, the probability of the event \(\{ - 1 \le Y \le 1\} \)is

\(\begin{aligned}P( - 1 \le Y \le 1) &= P\left( {\frac{{ - 1 - ( - 1)}}{{1.7795}} \le Z \le \frac{{1 - ( - 1)}}{{1.7795}}} \right)\\ &= P(0 \le Z \le 1.12)\\ &= 0.3686,\end{aligned}\)

(3): from the normal probability table in the appendix. The probability can also be computed with a software.

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Most popular questions from this chapter

The National Health Statistics Reports dated Oct. \({\rm{22, 2008}}\), stated that for a sample size of \({\rm{277 18 - }}\)year-old American males, the sample mean waist circumference was \({\rm{86}}{\rm{.3cm}}\). A somewhat complicated method was used to estimate various population percentiles, resulting in the following values:

a. Is it plausible that the waist size distribution is at least approximately normal? Explain your reasoning. If your answer is no, conjecture the shape of the population distribution.

b. Suppose that the population mean waist size is \({\rm{85cm}}\)and that the population standard deviation is \({\rm{15cm}}\). How likely is it that a random sample of \({\rm{277}}\) individuals will result in a sample mean waist size of at least \({\rm{86}}{\rm{.3cm}}\)?

c. Referring back to (b), suppose now that the population mean waist size in \({\rm{82cm}}\).Now what is the (approximate) probability that the sample mean will be at least \({\rm{86}}{\rm{.3cm}}\)? In light of this calculation, do you think that \({\rm{82cm}}\)is a reasonable value for \({\rm{\mu }}\)?

Let\({{\rm{X}}_{\rm{1}}}{\rm{,}}{{\rm{X}}_{\rm{2}}}{\rm{,}}{{\rm{X}}_{\rm{3}}}{\rm{,}}{{\rm{X}}_{\rm{4}}}{\rm{,}}{{\rm{X}}_{\rm{5}}}\), and \({{\rm{X}}_{\rm{6}}}\) denote the numbers of blue, brown, green, orange, red, and yellow M\&M candies, respectively, in a sample of size\({\rm{n}}\). Then these \({{\rm{X}}_{\rm{i}}}\) 's have a multinomial distribution. According to the M\&M Web site, the color proportions are\({{\rm{p}}_{\rm{1}}}{\rm{ = }}{\rm{.24,}}{{\rm{p}}_{\rm{2}}}{\rm{ = }}{\rm{.13}}\), \({{\rm{p}}_{\rm{3}}}{\rm{ = }}{\rm{.16,}}{{\rm{p}}_{\rm{4}}}{\rm{ = }}{\rm{.20,}}{{\rm{p}}_{\rm{5}}}{\rm{ = }}{\rm{.13}}\), and\({{\rm{p}}_{\rm{6}}}{\rm{ = }}{\rm{.14}}\).

a. If\({\rm{n = 12}}\), what is the probability that there are exactly two M\&Ms of each color?

b. For\({\rm{n = 20}}\), what is the probability that there are at most five orange candies? (Hint: Think of an orange candy as a success and any other color as a failure.)

c. In a sample of\({\rm{20M \backslash Ms}}\), what is the probability that the number of candies that are blue, green, or orange is at least \({\rm{10}}\) ?

The number of parking tickets issued in a certain city on any given weekday has a Poisson distribution with parameter \({\rm{ \mu = 50}}\).

a. Calculate the approximate probability that between \({\rm{35 and 70 }}\)tickets are given out on a particular day.

b. Calculate the approximate probability that the total number of tickets given out during a \({\rm{5 - }}\)day week is between \({\rm{225 and 275}}\)

c. Use software to obtain the exact probabilities in (a) and (b) and compare to their approximations.

Two components of a minicomputer have the following joint pdf for their useful lifetimes \({\rm{X}}\)and \({\rm{Y}}\)

a. What is the probability that the lifetime \({\rm{X}}\) of the first component exceeds \({\rm{3}}\)?

b. What are the marginal pdf’s of \({\rm{X}}\)and \({\rm{Y}}\)? Are the two lifetimes independent? Explain.

c. What is the probability that the lifetime of at least one component exceeds\({\rm{3}}\)?

A service station has both self-service and full-service islands. On each island, there is a single regular unleaded pump with two hoses. Let \({\rm{X}}\)denote the number of hoses being used on the self-service island at a particular time, and let\({\rm{Y}}\)denote the number of hoses on the full-service island in use at that time. The joint \({\rm{pmf}}\) of \({\rm{X}}\)and \({\rm{Y}}\) appears in the accompanying tabulation.

a. What is\({\rm{P(X = 1 and Y = 1)}}\)?

b. Compute P(X£1}and{Y£1)

c. Give a word description of the event , and compute the probability of this event.

d. Compute the marginal \({\rm{pmf}}\) of \({\rm{X}}\)and of \({\rm{Y}}\). Using \({{\rm{p}}_{\rm{X}}}{\rm{(x)}}\)what is P(X£1)?

e. Are \({\rm{X}}\)and\({\rm{Y}}\)independent \({\rm{rv's}}\)? Explain

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