The probability that the transistor will last between 12 and 24 weeks is 0.424
X= lifetime of the transistor in weeks E(X)= 24 weeks
O,= 12 weeks
The anticipated value, variance, and distribution of the random variable X were all provided to us. Finding the parameters alpha and beta is necessary before we can discover the solutions to the difficulties.
X~gamma(
)
E(X)= 
= 
=6 weeks
V(x)= 
=24/6= 4
Now we can find the solutions:
The excel formula used to create Figure one is as follows:
=gammadist(X, , , False)
P(
)
P(
)
P(
)
P= 0.424
Therefore, probability that the transistor will last between 12 and 24 weeks is 0.424
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Answer:
(p + q)² - ∛(h·3k) or (p + q)² - ∛(h·3k)
Step-by-step explanation:
Cube root of x: ∛x
Product of h and 3k: h·3k
Sum of p and q: p + q
*****************************
From (p + q)² subtract ∛(h·3k) This becomes, symbolically:
=> (p + q)² - ∛(h·3k)
Using sampling concepts, the population and the sample are given as follows:
d. population: 6,000 batches of 100 cards sample: every 100th batch.
<h3>What is sampling?</h3>
It is a common statistics practice, when we want to study something from a population, we find a sample of this population, which is a <u>group containing elements of a population</u>. A sample has to be representative of the population, that is, it has to involve all segments of the population.
For example:
I want to estimate the proportion of New York state residents who are Buffalo Bills fans. So I ask, lets say, 1000 randomly selected New York state residents whether they are Buffalo Bills fans, and then:
- The population is: All New York State residents.
- The sample is the 1000 randomly selected New York state residents.
Hence, in the situation described the population is the 6,000 batches of 100 cards, while the sample is every 100th batch, hence option d is correct.
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Alexander has to miles to go because if one mile is a third you can simply maltiply by three, then subtract the mile he already went. you maltiply by three because it was a third of the way there
