You roll a die and then roll the die again. How many possible outcomes are there

Mathematics

2 answers:

siniylev [52]3 years ago

7 0

Answer:

Step-by-step explanation:

If one event has p possible outcomes, and another event has m possible outcomes, then there are a total of p • m possible outcomes for the two events. Rolling two six-sided dice: Each die has 6 equally likely outcomes, so the sample space is 6 • 6 or 36 equally likely outcomes.

nignag [31]3 years ago

4 0

Answer:

There will be 36 possible outcomes and 36 sample spaces

You might be interested in

Find the rate of change for the situation. A Chef cooks 9 lbs of chicken for 36 people and 17 lbs of chicken 68 people

VMariaS [17]

The answer is: for every 4 people its 1 lbs

4 0

3 years ago

Read 2 more answers

Help me plz...

cestrela7 [59]

The 3 inside angles of a triangle need to add up to 180 degrees.

You have two angles of 104 and 36.

A = 180 - 104 - 36 - 40

The answer is 40

4 0

3 years ago

Read 2 more answers

Ollie won 2 games for every 5 games that Noelle won. If Noelle won 15 more

Dmitrij [34]

Answer:

Step-by-step explanation:

2:5, 4:10, 6:15

5 0

3 years ago

Suppose the weights of Farmer Carl's potatoes are normally distributed with a mean of 8.0 ounces and a standard deviation of 1.1

svet-max [94.6K]

Answer:

a) 0.9959 = 99.59% probability that the mean weight is less than 9.3 ounces

b) 0.0129 = 1.29% probability that the mean weight is more than 9.0 ounces

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 $s = \frac{\sigma}{\sqrt{n}}$ .