F(6) if f(x)=4x what's the answer?a.24b.10c.16or is it d.2
1 answer:
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Answer:
Category Relative Frequency
male survivors 17.3%
males who died 63.7 %
Female survivors 15.1 %
Females who died 3.8%
Step-by-step explanation:
- The relative frequency is the proportion of the number of times the event occurs to the total number of trials.
Given : Total people on ship = 2274
Number of male survivors = 394
⇒ Relative Frequency of male survivors =
In percent ,
Number of males who died =1449
⇒ Relative Frequency of male who died =
In percent ,
Number of female survivors =344
⇒ Relative Frequency of female survivors =
In percent ,
Number of females who died = 87
⇒ Relative Frequency of females who died =
In percent ,
Hence, the complete table would be :
Category Relative Frequency
male survivors 17.3%
males who died 63.7 %
Female survivors 15.1 %
Females who died 3.8%
Answer:
15.87% probability that a random sample of 16 people will exceed the weight limit
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.
For sums, the theorem can be applied, with mean and standard deviation
In this problem, we have that:
If a random sample of 16 persons from the campus is to be taken, what is the chance that a random sample of 16 people will exceed the weight limit
This is 1 subtracted by the pvalue of Z when X = 2508. So
By the Central Limit Theorem
has a pvalue of 0.8413
1 - 0.8413 = 0.1587
15.87% probability that a random sample of 16 people will exceed the weight limit