According to a random sample taken at 12 A.M., body temperatures of healthy adults have a bell-shaped distribution with a mean of 98.28degreesF and a standard deviation of 0.63degreesF. Using Chebyshev's theorem, what do we know about the percentage of healthy adults with body temperatures that are within 2 standard deviations of the mean? What are the minimum and maximum possible body temperatures that are within 2 standard deviations of the mean? At least nothing% of healthy adults have body temperatures within 2 standard deviations of 98.28degreesF.

Answers

Answer 1

Answer:

Answer:

At least 75% of healthy adults have body temperatures within 2 standard deviations of 98.28degreesF.

The minimum possible body temperature that is within 2 standard deviation of the mean is 97.02F and the maximum possible body temperature that is within 2 standard deviations of the mean is 99.54F.

Step-by-step explanation:

Chebyshev's theorem states that, for a normally distributed(bell-shaped )variable:

75% of the measures are within 2 standard deviations of the mean

89% of the measures are within 3 standard deviations of the mean.

Using Chebyshev's theorem, what do we know about the percentage of healthy adults with body temperatures that are within 2 standard deviations of the mean?

At least 75% of healthy adults have body temperatures within 2 standard deviations of 98.28degreesF.

Range:

Mean: 98.28

Standard deviation: 0.63

Minimum = 98.28 - 2*0.63 = 97.02F

Maximum = 98.28 + 2*0.63 = 99.54F

The minimum possible body temperature that is within 2 standard deviation of the mean is 97.02F and the maximum possible body temperature that is within 2 standard deviations of the mean is 99.54F.

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Give the numerical value of the parameter p in the following binomial distribution scenarioA softball pitcher has a 0.721 probability of throwing a strike for each pitch and a 0.279 probability of throwing a ball. If the softball pitcher throws 19 pitches, we want to know the probability that more than 15 of them are strikes.

Answers

Answer:

$P(X>15)= P(X=16)+P(X=17)+P(X=18)+P(X=19)$

$P(X=16)=(19C16)(0.721)^(16) (1-0.721)^(19-16)=0.112$

$P(X=17)=(19C17)(0.721)^(17) (1-0.721)^(19-17)=0.051$

$P(X=18)=(19C18)(0.721)^(18) (1-0.721)^(19-18)=0.015$

$P(X=19)=(19C19)(0.721)^(19) (1-0.721)^(19-19)=0.002$

And replacing we got:

$P(X>15)= P(X=16)+P(X=17)+P(X=18)+P(X=19) =0.112+0.051+0.015+0.002= 0.1801$

Step-by-step explanation:

Previous concepts

A Bernoulli trial is "a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted". And this experiment is a particular case of the binomial experiment.

The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".

The probability mass function for the Binomial distribution is given as:

$P(X)=(nCx)(p)^x (1-p)^(n-x)$

Where (nCx) means combinatory and it's given by this formula:

$nCx=(n!)/((n-x)! x!)$

Solution to the problem

For this case our random variable is given by:

$X \sim Binom(n = 19, p = 0.721)$

For this case we want this probability:

$P(X>15)= P(X=16)+P(X=17)+P(X=18)+P(X=19)$

$P(X=16)=(19C16)(0.721)^(16) (1-0.721)^(19-16)=0.112$

$P(X=17)=(19C17)(0.721)^(17) (1-0.721)^(19-17)=0.051$

$P(X=18)=(19C18)(0.721)^(18) (1-0.721)^(19-18)=0.015$

$P(X=19)=(19C19)(0.721)^(19) (1-0.721)^(19-19)=0.002$

And replacing we got:

$P(X>15)= P(X=16)+P(X=17)+P(X=18)+P(X=19) =0.112+0.051+0.015+0.002= 0.1801$

$P(X> 2)=1-P(X\leq 2)=1-[0.0211+0.0995+0.211]=0.668$

Final answer:

In this binomial distribution scenario, the parameter 'p', representing the probability of success on each trial, is the probability of the pitcher throwing a strike, which is 0.721.

Explanation:

In the binomial distribution scenario you described, the softball pitcher throwing a pitch is the independent trial with two possible outcomes: throwing a strike (success) or a ball (failure). The parameter p represents the probability of success on each independent trial. From the question, we can see that the probability, or p, of the pitcher throwing a strike (success) is 0.721. Therefore, p = 0.721.

Please note that the binomial distribution model can be used when all trials are independent, the outcome of a trial is success or failure, and the probability of success remains the same for each trial. It doesn't appear that we need the number 'n' of independent trials or the random variable 'X' representing the number of successes (strikes in this case) for your question, as we were only asked for the value of 'p'.

Learn more about Binomial Distribution here:

brainly.com/question/33656163

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Answers

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Answers

Answer:

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