Answer:
a) Poisson distribution
use a Poisson distribution model when events happen at a constant rate over time or space.
Step-by-step explanation:
<u> Poisson distribution</u>
- Counts based on events in disjoint intervals of time or space produce a Poisson random variable.
- A Poisson random variable has one parameter, its mean λ
- The Poisson model uses a Poisson random variable to describe counts in data.
use a Poisson distribution model when events happen at a constant rate over time or space.
<u>Hyper geometric probability distribution</u>:-
The Hyper geometric probability distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws without replacement, from a finite population of size that contains exactly objects with that feature where in each draw is either a success or failure.
This is more than geometric function so it is called the <u>Hyper geometric probability distribution </u>
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<u>Binomial distribution</u>
- The number of successes in 'n' Bernoulli trials produces a <u>Binomial distribution </u>. The parameters are size 'n' success 'p' and failure 'q'
- The binomial model uses a binomial random variable to describe counts of success observed for a real phenomenon.
Finally use a Binomial distribution when you recognize distinct Bernoulli trials.
<u>Normal distribution</u>:-
- <u>normal distribution is a continuous distribution in which the variate can take all values within a range.</u>
- Examples of continuous distribution are the heights of persons ,the speed of a vehicle., and so on
- Associate normal models with bell shaped distribution of data and the empirical rule.
- connect <u>Normal distribution</u> to sums of like sized effects with central limit theorem
- use histograms and normal quantile plots to judge whether the data match the assumptions of a normal model.
<u>Conclusion</u>:-
Given data use a Poisson distribution model when events happen at a constant rate over time or space.
Answer:
1. There are a total of 19 marbles in the bag. The probability of picking a green marble out of them is 4/19 since there are only 4 green ones. The probability of picking a brown marble after replacing what has been initially picked is 1/19. The final probability is the product of the two probabilities and that is 4/361.
2. ?
3. 60%
Step-by-step explanation:
<span>(y=mx+b) or (ax+by=c) hope this helped
</span>
Answer:
x = 2 (doesn’t work in the equation)
3(2) -3 = 3 - 6(2)
X = 2/3 (works in the equation
3 (2/3) - 3 = 3 - 6(2/3)
2-3 = 3 - 4
- 1 = -1
Step-by-step explanation:
|3x| - 3 = 3 - 6x
|3x| = 3 + 3 - 6x
|3x| = 6 - 6x
|3x| = -6 + 6x
3x = - 6 + 6x or 3x = 6 - 6x
6 = 6x - 3x
6 = 3x
x = 2
3x = 6 - 6x
3x + 6x = 6
9x = 6
x = 6/9
x = 2/3
Answer:
- 6
Step-by-step explanation:
The average rate of change of f(x) in the closed interval [ a, b ] is
![\frac{f(b)-f(a)}{b-a}](https://tex.z-dn.net/?f=%5Cfrac%7Bf%28b%29-f%28a%29%7D%7Bb-a%7D)
here [ a, b ] = [ - 7, - 1 ]
f(b) = f(- 1) = (- 1)² + 2(- 1) - 8 = 1 - 2 - 8 = - 9
f(a) = f(- 7) = (- 7)² + 2(- 7) - 8 = 49 - 14 - 8 = 27
Hence
average rate of change =
=
= - 6