Answer:
is proved for the sum of pth, qth and rth terms of an arithmetic progression are a, b,and c respectively.
Step-by-step explanation:
Given that the sum of pth, qth and rth terms of an arithmetic progression are a, b and c respectively.
First term of given arithmetic progression is A
and common difference is D
ie.,
and common difference=D
The nth term can be written as

pth term of given arithmetic progression is a

qth term of given arithmetic progression is b
and
rth term of given arithmetic progression is c

We have to prove that

Now to prove LHS=RHS
Now take LHS




![=\frac{[Aq+pqD-Dq-Ar-prD+rD]\times qr+[Ar+rqD-Dr-Ap-pqD+pD]\times pr+[Ap+prD-Dp-Aq-qrD+qD]\times pq}{pqr}](https://tex.z-dn.net/?f=%3D%5Cfrac%7B%5BAq%2BpqD-Dq-Ar-prD%2BrD%5D%5Ctimes%20qr%2B%5BAr%2BrqD-Dr-Ap-pqD%2BpD%5D%5Ctimes%20pr%2B%5BAp%2BprD-Dp-Aq-qrD%2BqD%5D%5Ctimes%20pq%7D%7Bpqr%7D)




ie., 
Therefore
ie.,
Hence proved
He raised $736.
One way is to multiply 640 x .15 = 96
Add 96 + 640 = 736
Answer:
6/5
Step-by-step explanation:
2/5*3=2/5*3/1=6/5
Answer:
For this case we can use the probability mass function and we got:

Step-by-step explanation:
Previous concepts
A Bernoulli trial is "a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted". And this experiment is a particular case of the binomial experiment.
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
The probability mass function for the Binomial distribution is given as:
Where (nCx) means combinatory and it's given by this formula:
Solution to the problem
Let X the random variable of interest, on this case we now that:
For this case we can use the probability mass function and we got:
