Answer questions gplzz

Can someone please correct me if im wrong ? Thanks

kakasveta [241]

the answer is D I'm pretty sure!!

6 0

3 years ago

for y=x^2-10 do the following . A) Sketch a graph of the equation. B) Identify the vertex. C)Compare the graph of y=f(x) to the

Andreas93 [3]

A ) A graph of the equation is in the attachment.
B ) The vertex ( 0, -10 )
C ) The graph of y = x² is translated for 10 units down.

Download docx

8 0

3 years ago

The school theater department made $2,142 on ticket sales for the three nights of their play. The department sold the same numbe

anyanavicka [17]

Answer:

102 tickets

Step-by-step explanation:

Let's say the total number of tickets sold is n.

7n = 2142

n = 2142 ÷ 7

n = 306

They sold 306 tickets in total.

We want to know how much they sold per night. We know they sold the same number of tickets each night for three nights.

306 ÷ 3 = 102

Therefore, they sold 102 tickets each night.

I hope this helps :)

6 0

3 years ago

Ricardo and Tammy practice putting golf balls. Ricardo makes 47% of his putts and Tammy makes 51% of her putts. Suppose that Ric

yaroslaw [1]

Answer:

0.3821 = 38.21% probability that Ricardo makes a higher proportion of putts than Tammy.

Step-by-step explanation:

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

Normal probability distribution

When the distribution is normal, we use 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 $s = \sqrt{\frac{p(1-p)}{n}}$

Subtraction of normal variables:

When we subtract normal variables, the mean of the subtraction will be the subtraction of the means, while the standard deviation will be the square root of the sum of the variances.

Ricardo makes 47% of his putts, and attempts 25 putts.

By the Central Limit Theorem, we have that:

$\mu_R = 0.47, s_R = \sqrt{\frac{0.47*0.53}{25}} = 0.0998$