.Answer:
Step-by-step explanation:
Answer:
12r/t
Step-by-step explanation:
Answer:
17.
Step-by-step explanation:
6^2 - 19
= 36 - 19
= 17.
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>
<u></u>
<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:
Option B. 0.71
Step-by-step explanation:
There are 200 males and 300 female employees in the company.
The percentage of its employees are given who stayed for at least 1, 2 and 3 years.
1 year 2 years 3 years
Male 0.67 0.45 0.20
Female 0.73 0.64 0.39
Number of male employees who stayed at least one year = 0.67 × 200
= 134
Number of female employees who stayed at least 1 year = 0.73 × 300
= 219
Total employees who stayed at least 1 year = 134 + 219 = 353
Total employees in the company = 200 (males) + 300 (females)
= 500 employees
Probability that an employee stayed for at least one year = 
= 0.706 ≈ 0.71
Option B 0.71 is the answer.