Answer:
x=-8
Step-by-step explanation:
1) remove the constant(number without variable) to do this, we must use the opposite operation(in our case, subtraction)
so
4x+1=-31
a) 4x+1-1=-31-1
4x=-32
b) remove the coefficient by doing the opposite operation(division in our case)
x=-8
Answer:
1/2
Step-by-step explanation:
Answer:
1.17
Step-by-step explanation:
(MUCH BETTER!)
From the definition of the sin of an angle, you know:
sin(45°) = AC/AB = 4/AB ⇒ AB = 4/sin(45°) ≈ 5.6569
sin(63°) = AC/AD = 4/AD ⇒ AD = 4/sin(63°) ≈ 4.4893
Then the difference AB-AD is:
AB -AD = 5.6569 -4.4893 = 1.1676
AB -AD ≈ 1.17 units
Sorry this took me so long to answer!
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.
