Please help with these math comparisons!? explain

If you were to roll two dice, how many different ways could you roll double fours?

muminat

Answer:

There are 6 ways we can roll doubles out of a possible 36 rolls (6 x 6), for a probability of 6/36, or 1/6, on any roll of two fair dice. So you have a 16.7% probability of rolling doubles with 2 fair six-sided dice

Step-by-step explanation:

5 0

3 years ago

Use Substitution to solve the following System of Equations.<br> y = 2x - 4<br> y= 12 +3

castortr0y [4]

Answer:

x = 9.5

this should be the right answer if the same one you had to solve.

Step-by-step explanation:

y = 2x - 4

y = 12 + 3 combine them

2x - 4 = 12 + 3

+4 +4 The 4's cancel each other out

2x = 12 + 7 Add then divide

2x = 19

---- ----

19 19

x = 9.5

4 0

3 years ago

Covert 3/7into a percent

Elza [17]

Answer:

42.86%

Step-by-step explanation:

Our percent fraction is 42.857142857143/100, which means that 37 as a percentage is 42.86%

6 0

2 years ago

Read 2 more answers

For home economics class Sandra had 5 cups of flour. She made 3 batches of cookies that each used 1.5 cups of flour. Find the am

sweet-ann [11.9K]

Okay so what you would do is 1.5 multiply by 3 find out the answer there and then subtract the 5 cups with the answer.

so if it is 1.5×3 that would equal ____ then you would subract 5 - ___ then there is your answer

5 0

3 years ago

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

Alisiya [41]

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.

4 0

3 years ago