Answer: Dimensions of A are of length [L]
Dimensions of B are of 
Dimensions of C are of 
Step-by-step explanation:
The given equation is
Since the dimension on the L.H.S of the equation is [L] , each of the terms on the right hand side should also have dimension of length[L] to be dimensionally valid
Thus
Dimensions of A = [L]
Dimensions of Bt = [L]
![Bt=[L]\\\\](https://tex.z-dn.net/?f=Bt%3D%5BL%5D%5C%5C%5C%5C)
![[B][T]=[L]](https://tex.z-dn.net/?f=%5BB%5D%5BT%5D%3D%5BL%5D)
![\\\\\therefore [B]=LT^{-1}](https://tex.z-dn.net/?f=%5C%5C%5C%5C%5Ctherefore%20%5BB%5D%3DLT%5E%7B-1%7D)
Similarly
Dimensions of ![Ct^{}2 = [L]](https://tex.z-dn.net/?f=Ct%5E%7B%7D2%20%3D%20%5BL%5D)
![Ct^{2}=[L]\\\\[C][T]^{2}=[L]\\\\\therefore [C]=LT^{-2}](https://tex.z-dn.net/?f=Ct%5E%7B2%7D%3D%5BL%5D%5C%5C%5C%5C%5BC%5D%5BT%5D%5E%7B2%7D%3D%5BL%5D%5C%5C%5C%5C%5Ctherefore%20%5BC%5D%3DLT%5E%7B-2%7D)
You will made m the subject.. so m=F/a. I hope it helped :)
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.
300 is 10 times as much as 30.
because if you do 30 x 10 you get 300
Answer:
<em>y = 3( </em>
<em> ) </em>
Step-by-step explanation:
Exponential function has the form f(x) = k 
where b > 0 and b ≠ 1
~~~~~~
3 = k × 
⇒ k = 3
1.5 = 3( 
) ⇒ b = 0.5
Thus, <em>y = 3( </em><em> )</em>
