In a class of 300 students ,20% are girls how many boys are there?
2 answers:
You might be interested in
Answer:
The probability that the mean from that sample will be between 183 and 186 is 0.0994 = 9.94%.
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 z-score 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 p-value, 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.
A population has a mean of 180 and a standard deviation of 24.
This means that
A sample of 100 observations will be taken.
This means that
The probability that the mean from that sample will be between 183 and 186 is:
This is the pvalue of Z when X = 186 subtracted by the pvalue of Z when X = 183. So
X = 186
By the Central Limit Theorem
has a pvalue of 0.9938
X = 183
has a pvalue of 0.8944
0.9938 - 0.8944 = 0.0994
The probability that the mean from that sample will be between 183 and 186 is 0.0994 = 9.94%.
Answer:
33.36% probability that X is less than 2.
Step-by-step explanation:
The distribution is not normal, however, using the central limit theorem, it is going to be approximately normal. So
Central Limit Theorem:
The Central Limit Theorem estabilishes that, for a random variable X, with mean and standard deviation
, a large sample size can be approximated to a normal distribution with mean
and standard deviation
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.
In this problem, we have that:
(a) Suppose we let X be the mean number of accidents per week at the intersection during 9 randomly chosen weeks. What is the probability that X is less than 2?
This is the pvalue of Z when X = 2. So
By the Central Limit Theorem
has a pvalue of 0.3336.
33.36% probability that X is less than 2.