Draw the reflection of the image over the y-axis.

A factory uses 15 pounds of steel for every 18 pounds of copper. How much copper will the factory use for 2,700 pounds of steel?

Nostrana [21]

Answer:

3240 pounds of copper would be used

Step-by-step explanation:

$\frac{18}{15}=\frac{x}{2700} \\(2700)*(\frac{18}{15})=x\\3240lbs=x$

3 0

2 years ago

SOMEONE PLEASE HELP!!!!!

blagie [28]

Answer:

on the first triangle it is equal, 1.5 on the left of it is half of 3 on the right triangle, 5 on the left is half of 10 on the right triangle. so the answer is 8 becuase each side on the left is 2 times the side on the right

Step-by-step explanation:

hope this helps

3 0

3 years ago

The population of a city has decreased by 35% since it was last measured. If the current population is 9100 , what was the previ

Alina [70]

The previous population was 14000.

Explanation

The population decreased by 35% since the last measurement. So the current population is less than the previous population.

current population=9100

previous population =x

current population is 35% less than the previous population x

$9100=x*(100-35)/100\\9100=(x*65)/100\\x=(100*9100)/65=14000$

The previous population was 14000.

4 0

2 years ago

How do you solve for what is a? Please answer quickly?<br><br> 4(a-2)=3(a+4)

Lunna [17]

4(a-2)=3(a+4)
4a-8=3a+12
a=20

8 0

3 years ago

Consider the question of whether the home team wins more than half of its games in the National Basketball Association. Suppose

spayn [35]

Answer:

0.0037 = 0.37% probability that the home team would win 65% or more of its games in a simple random sample of 80 games

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 zscore 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 pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes 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 $s = \frac{\sigma}{\sqrt{n}}$ .

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

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean $\mu = p$ and standard deviation $s = \sqrt{\frac{p(1-p)}{n}}$