Mark drew a regular octagon with side lengths of 10 inches. He divided the octagon into two identical trapezoids and a rectangle

If f(x) is an even function and (6, 8) is one the points on the graph of f(x), which reason explains why (–6, 8) must also be a

Svet_ta [12734]

If $f(x)$ is categorized as an even function, then $f(x)=f(-x)$ . In this circumstance, $f(6)=8=f(-6)$ .

This indicates an even function has symmetry about the $y-axis$ . Thus, the results of a negative x-value and a positive x-value are identical.

The appropriate choice is the first alternative.

As the graph is even, the outputs for a negative x-value and a positive x-value are congruent.

4 0

2 months ago

Let X represent the amount of gasoline (gallons) purchased by a randomly selected customer at a gas station. Suppose that the me

babunello [11817]

Answer:

a) There is an 18.94% chance that the sample mean of the amount purchased will be at least 12 gallons.

b) There is an 81.06% chance that the total gasoline purchased will not exceed 600 gallons.

c) The estimated value for the 95th percentile of the total consumption by 50 randomly chosen customers is 621.5 gallons.

Step-by-step explanation:

The solution to this query involves applying the normal probability distribution and the central limit theorem.

Normal probability distribution

Issues involving normally distributed samples can be addressed using the z-score formula.

In a dataset characterized by mean $\mu$ and standard deviation $\sigma$ , the z-score for a value X is expressed as:

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

The z-score indicates how many standard deviations a particular value is from the mean. After calculating the z-score, we reference the z-score table to find its corresponding p-value, which represents the probability that a measure is less than X, essentially giving us X's percentile. By subtracting the p-value from 1, we find the chance that the measure exceeds X.

Central Limit Theorem

The Central Limit Theorem posits that for a normally distributed variable X, with mean $\mu$ and standard deviation $\sigma$ , the distribution of sample means with size n approximates a normal distribution characterized by mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .

Even when dealing with a skewed variable, the Central Limit Theorem remains applicable as long as n is no less than 30.

For sums, this theorem can likewise be employed, accompanied by mean $\mu$ and standard deviation $s = \sqrt{n}*\sigma$ .

In this scenario, we are given that:

$\mu = 11.5, \sigma = 4$

a. For a group of 50 randomly selected customers, what is the estimated probability that the sample mean amount purchased is at least 12 gallons?

Here we have $n = 50, s = \frac{4}{\sqrt{50}} = 0.5657$

This probability is derived from 1 minus the p-value of Z corresponding to X = 12.

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

According to the Central Limit Theorem

$Z = \frac{X - \mu}{s}$

$Z = \frac{12 - 11.5}{0.5657}$

$Z = 0.88$

$Z = 0.88$ yields a p-value of 0.8106.

1 - 0.8106 = 0.1894

Therefore, there is an 18.94% chance that the sample mean amount purchased is at least 12 gallons.

b. For a group of 50 randomly selected customers, what is the estimated probability that the total amount of gasoline purchased does not exceed 600 gallons?

Regarding sums, we have $mu = 50*11.5 = 575, s = \sqrt{50}*4 = 28.28$