Write each expression using the fewest number of bases possible

Gnesinka [82]

First take the amount of drinks and popcorn needed then multiply the amount by the price then see if she has enough money when you finish the multiplying.

5 0

3 years ago

If there are 23 students in your class, and your teacher is pulling random names from a hat, what is the probability that your n

krok68 [10]

If the teacher isn't putting the names back in the hat then it would be 1/21 if she is then 1/23

3 0

3 years ago

Read 2 more answers

Can someone please help me asap!!

deff fn [24]

The answer will be 24 23/100

4 0

2 years ago

Read 2 more answers

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