Amber earned $14.50 babysitting on Saturday. On Sunday, she spent $6.95 when she purchased a movie ticket. She needed to borrow
2 answers:
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Answer:
57.8125% or approx. 57.8%
Step-by-step explanation:
There is a 1/4, or 25%, or 0.25 chance that an egg has salmonella.
Thus, there is a 75%, or 0.75 chance that an egg DOESN'T contain salmonella.
Let's find the probability that all 3 of Larry's eggs are free from salmonella. Larry would have to hit that 75% chance 3 times in a row. The chance of that happening is:
0.75 * 0.75 * 0.75 = = 0.421875
From this, we can deduce that if there is a 0.421875 (42.1875%) chance that all eggs are safe to eat, there must be a...
1 - 0.421875 = 0.578125
...0.578125 (57.8125%) chance that 1 or more of Larry's eggs do have salmonella.
Answer: approx. 57.8% or 57.8125%
Answer:
0.8926 = 89.26% probability that the mean weight of the sample of horses would differ from the population mean by less than 15lbs if 31 horses are sampled at random from the stable
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 normally distributed samples are 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.
In this problem, we have that:
What is the probability that the mean weight of the sample of horses would differ from the population mean by less than 15lbs if 31 horses are sampled at random from the stable?
pvalue of Z when X = 975 + 15 = 990 subtracted by the pvalue of Z when X = 975 - 15 = 960. So
X = 990
By the Central Limit Theorem
has a pvalue of 0.9463
X = 960
has a pvalue of 0.0537
0.9463 - 0.0537 = 0.8926
0.8926 = 89.26% probability that the mean weight of the sample of horses would differ from the population mean by less than 15lbs if 31 horses are sampled at random from the stable