At a fabric store, the probability that a customer buys thread is 0.15. The probability that a customer buys a needle given that
1 answer:
Answer:
The correct statement is:
C. Buying a needle and buying a thread are dependent events.
Explanation:
The first statement is incorrect because the given events are dependent on each other because the probability that a customer buys a needle given that the customer buys thread is 0.25.
The second statement is incorrect because we cannot say all those who buy a thread also buy a needle.
The fourth statement is incorrect because the probability that a customer buys a needle and thread is
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Answer:
p = - 5
Step-by-step explanation:
The vertex of the function is (0.5, 30.25 )
Since the roots are (6, 0) and (p, 0) then the axis of symmetry is vertical
with equation x = 0.5
The axis of symmetry passes through the midpoint of the roots, thus
= 0.5 ( multiply both sides by 2 )
6 + p = 1 ( subtract 6 from both sides )
p = - 5
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 and standard deviation
, the zscore of a measure X is given by:
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 and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
Mean of 8.0 ounces and a standard deviation of 1.1 ounces.
This means that
(a) If 5 potatoes are randomly selected, find the probability that the mean weight is less than 9.3 ounces?
means that
This probability is the pvalue of Z when X = 9.3. So
By the Central Limit Theorem
has a pvalue of 0.9959
0.9959 = 99.59% probability that the mean weight is less than 9.3 ounces
(b) If 6 potatoes are randomly selected, find the probability that the mean weight is more than 9.0 ounces?
means that
This probability is 1 subtracted by the pvalue of Z when X = 9. So
has a pvalue of 0.9871
1 - 0.9871 = 0.0129
0.0129 = 1.29% probability that the mean weight is more than 9.0 ounces