Can you do a number line with no solution set.

The price of gasoline rose from $2.40 to $2.76 in one month. By what percent did the gas price rise?

IrinaK [193]

Answer: 15%

Reason: I wrote it down hopes it helps!!

3 0

2 years ago

ABC and AED are straight lines. BE and CD are parallel. AC = 12.3cm AB = 8.2cm BE = 3.8cm a) Work out length CD. AD = 9.15cm

elena-14-01-66 [18.8K]

Answer:

15.33cm

Step-by-step explanation:

when you try to draw out this figure, satisfying the fact that Ad and Ac are both straight lines. you find the ultimately it forms a right-angled triangle with sides AC and AD and hypotenuse CD.

Now, you may use Pythagoras theorem to solve:

AC^2 + AD^2 = CD^2

12.3^2 + 9.15^2 = CD^2

CD^2 = 235.0125

CD = square root of 235.0125 = 15.33cm

Hope this helps

6 0

3 years ago

Suppose that the walking step lengths of adult males are normally distributed with a mean of 2.5 feet and a standard deviation o

Mumz [18]

Answer:

0% probability that the mean of the sample taken is less than 2.2 feet.

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 $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .