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Free_Kalibri [48]
3 years ago
15

A study of college football games shows that the number of holding penalties assessed has a mean of penalties per game and a sta

ndard deviation of penalties per game. What is the probability that, for a sample of college games to be played next week, the mean number of holding penalties will be penalties per game or less
Mathematics
1 answer:
Alisiya [41]3 years ago
4 0

Answer:

The probability that the mean number of holding penalties per game is of X or less is the p-value of Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}, in which \mu is the mean number of penalties per game, \sigma is the standard deviation and n is the number of games that will be sampled.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the z-score of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

We have that:

The mean number of penalties per game is \mu and the standard deviation is \sigma.

Sample of n games:

This means that s = \frac{\sigma}{\sqrt{n}}

What is the probability that, for a sample of college games to be played next week, the mean number of holding penalties will be X penalties per game or less?

The probability that the mean number of holding penalties per game is of X or less is the p-value of Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}, in which \mu is the mean number of penalties per game, \sigma is the standard deviation and n is the number of games that will be sampled.

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Answer/Step-by-step explanation:

6. To know who cycled faster between Sam and Bobby, find the constant of proportionality of both of them. The person with the least value of constant of proportionality is the fastest.

Constant of proportionality for Sam = \frac{distance (y)}{time (x)} = \frac{20}{2} = 10 mph

Constant of proportionality for Bobby:

Use (0, 0) and (2, 18) to find the constant of proportionality, which is:

m = \frac{y_2 - y_1}{x_2 - x_1} = \frac{18 - 0}{2 - 0} = \frac{18}{2} = 9 mph.

Bobby's unit rate/constant of proportionality is smaller than Sam's.

So, Bobby cycled faster.

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Answer:

The probability that the sample mean would differ from the population mean by more than 0.5 millimeters is 0.2420.

Step-by-step explanation:

Let <em>X</em> = the diameter of the steel bolts manufactured by the steel bolts manufacturing company Thompson and Thompson.

The mean diameter of the bolts is:

<em>μ</em> = 149 mm.

The standard deviation of the diameter of bolts is:

<em>σ</em> = 5 mm.

A random sample, of size <em>n</em> = 49, of steel bolts are selected.

The population of the diameter of bolts is not known.

According to the Central Limit Theorem if we have a population with mean <em>μ</em> and standard deviation <em>σ</em> and we take appropriately huge random samples (<em>n</em> ≥ 30) from the population with replacement, then the distribution of the sample mean will be approximately normally distributed.

The mean of the sampling distribution of sample mean is:

\mu_{\bar x}=\mu=149\ mm

The standard deviation of the sampling distribution of sample mean is:

\sigma_{\bar x}=\frac{\sigma}{\sqrt{n}}=\frac{5}{\sqrt{49}}=0.7143

Compute the probability that the sample mean would differ from the population mean by more than 0.5 millimeters as follows:

P(\bar X>\bar x)=P(\frac{\bar X-\mu_{\bar x}}{\sigma _{\bar x}}>\frac{0.50}{0.7143})\\=P(Z>0.70)\\=1-P(Z

*Use a <em>z</em>-table for the probability.

Thus, the probability that the sample mean would differ from the population mean by more than 0.5 millimeters is 0.2420.

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Answer:

<h2>-6</h2>

Step-by-step explanation:

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Substitute:

m=\dfrac{15-(-3)}{-1-2}=\dfrac{15+3}{-3}=\dfrac{18}{-3}=-6

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