Binomial and geometric distributions

In summary, the conversation discusses finding expected values and probabilities for two different distributions, Binomial and Geometric. The correct answers for the given exercises were provided and the conversation also includes discussions on how to compute the answers and where possible errors were made.
  • #1
sara_87
763
0
i was doing some exercises nut I'm not sure if my answers are correct
1) X~B(5,0.25) i have to find:
a) E(x^2) and my answer was 2.5, is this correct?
b) P(x(>or=to)4) and my answer was 0.0889, is this correct?

2) X~Geom(1/3) i have to find:
a) E(x) my answer is 1/3
b) E(x^2)
c) var(x)
d) P(X=4) my answer is 0.988
e) P(X>2) my answer is... -(1/18)
i don't know how to compute b) and c)
and i think d) is wrong
and i know e) is wrong.
can someone help please.
Thank you very much
 
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  • #2
Would "yes" or "no" answers help you? Show what you did to get those answers and we can point out exactly where, if at all, you went wrong.
 
  • #3
my working out is:
1) a) E(x^2)= var(x)+E(x)^2 = np(1-p+np)= 5(1/4)(1-1/4-5/4)=2.5
b) P(x(>or = to)4)= 1-P(x=3)-P(x=2)-P(x=1)-P(x=0)
using the formula P(x=m)=(nCm)P^m(1-p)^(n-m)
=1-(15/1024)-(135/512)-(405/1024)-(243/1024)=91/1024=0.0889

2) a)E(x)=1/p=1/(1/3)=3
b) and c) i have no idea
d) using the formula P(x=m)=P(1-P)^(m-1)
P(x=4)=1/3(1-1/3)^3=8/81=0.988
e) P(x>2)=1-P(x(<or=to)2)=1-P(x=2)-P(x=1)-P(x=0)
=1-(1/3)(2/3)-(1/3)(2/3)^0-(1/3)(2/3)^-1=-1/18
thank u very much
 
  • #4
1a is correct except you meant +5/4) not -5/4).

For 1b can you write out the nCm terms?

2a is correct.

For 2b use the same formula as in 1a.

2c: Var(Geometric(p)) = (1-p)/p^2; see http://en.wikipedia.org/wiki/Geometric_distribution

2d: 1/3(1-1/3)^3 = 8/81 < 0.1 so it cannot be > 0.9 (i.e., you have made a fraction computation error).

2e: if the exponent is k-1 then k = 1, 2, ... OTOH, if the exponent is k, then k = 0, 1, 2, ... In either case, exponent term > 0 so it cannot be -1. See http://en.wikipedia.org/wiki/Geometric_distribution
 
  • #5
for 1b:
my working including the ncm terms:
P(x(>or = to)4)= 1-P(x=3)-P(x=2)-P(x=1)-P(x=0)
using the formula P(x=m)=(nCm)P^m(1-p)^(n-m)
=1-[(5C3)(1/4)^3(1-1/4)^2]-[(5C2)(1/4)^2(1-1/4)^3]-[(5C1)(1/4)(1-1/4)^4]-[(5C0)(1/4)^0(1/1/4)^5]
=1-(15/1024)-(135/512)-(405/1024)-(243/1024)
=91/1024=0.0889

for 2e:
i looked at the link but i still haven't figured out what i had done wrong with my working out.

thank u very much for everything else.
 
  • #6
sara_87 said:
for 2e:
i looked at the link but i still haven't figured out what i had done wrong with my working out.
You have the exponential term -1, which shouldn't be there under either definition of the geometric distribution (referenced on the page I linked to).

My guess is you should have 1 - (1/3)(2/3)^2 - (1/3)(2/3)^1 - (1/3)(2/3)^0, but you should verify that.

For 1b, you have:
1 - Binomial[5, 3] (1/4)^3 (1 - 1/4)^2 - Binomial[5, 2] (1/4)^2 (1 - 1/4)^3 - Binomial[5, 1] (1/4) (1 - 1/4)^4 - Binomial[5, 0] (1/4)^0 (1/1/4)^5, where Binomial[n,m] stands for nCm.

Binomial[5, 3] = Binomial[5, 2] = 10
Binomial[5, 1] = 5
Binomial[5, 0] = 1

so the expression is:
1 - 10 (1/4)^3 (1 - 1/4)^2 - 10 (1/4)^2 (1 - 1/4)^3 - 5 (1/4) (1 - 1/4)^4 - 1 (1/4)^0 (1 - 1/4)^5
=1 - 10 (1/4)^3 (3/4)^2 - 10 (1/4)^2 (3/4)^3 - 5 (1/4) (3/4)^4 - 1 (1/4)^0 (3/4)^5
= 1 - 10 (9/1024) - 10 (27/1024) - 5 (81/1024) - 1 (243/1024)
= 1- (90 + 270 + 405 + 243)/1024
= 1 - 1008/1024
= 1 - 504/512
= 8/512
= 1/64
= 0.015625
 
Last edited:
  • #7
oh okay i see so the formula i used will only work if k=1,2,3... (as u said before)
so if i used the formula P(x=m)=P(1-P)^(m-1)
and did 1-P(x=2)-P(x=1)
=1-(1/3)(2/3)-(1/3)(2/3)^0
=4/9
would that work?
 
  • #8
Shouldn't x=3 be part of your formula?
 
  • #9
no it should only be for x=2 and x=1 as these are the ones strictly less than three
 
  • #10
Your OP stated "P(x(>or = to)4)," doesn't that exclude x=3?
 

What is a binomial distribution?

A binomial distribution is a probability distribution that shows the likelihood of obtaining a certain number of successes in a fixed number of independent trials with two possible outcomes (usually labeled as "success" and "failure"). It is characterized by two parameters: the number of trials (n) and the probability of success (p).

What is the formula for calculating binomial probability?

The formula for calculating binomial probability is P(x) = (nCx) * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial. (nCx) is the combination formula, which calculates the number of ways to choose x objects from a set of n objects, and is equal to n! / (x!(n-x)!).

How is a geometric distribution different from a binomial distribution?

A geometric distribution also shows the likelihood of obtaining a certain number of successes in a fixed number of independent trials, but it only has two possible outcomes: success or failure. In contrast, a binomial distribution can have more than two possible outcomes. Additionally, the probability of success in a geometric distribution remains constant for all trials, while the probability of success in a binomial distribution may vary.

What is the relationship between binomial and geometric distributions?

A binomial distribution can be thought of as a series of independent geometric distributions, where each trial is considered a "success" or "failure". This makes the binomial distribution a sum of multiple geometric distributions. Mathematically, the probability of getting exactly x successes in a binomial distribution can be calculated by multiplying the probability of success in a single trial (p) by the probability of getting x successes in a geometric distribution with x trials.

What are some real-life examples of binomial and geometric distributions?

Binomial distributions can be seen in many scenarios, such as flipping a coin, rolling a dice, or conducting surveys with yes/no questions. Geometric distributions can be observed in situations where there are repeated independent trials until a certain outcome is achieved, such as the number of attempts to make a successful basketball shot or the number of failures before a light bulb burns out.

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