Probability questions from Math Subject GRE

In summary, the probability of H = h flips for any h = 0, 1, 2, ..., 100 is:P(H=h) = \left( _h^{100} \right) \cdot \frac{1}{2^{100}}
  • #1
mrb
101
0
This is from the practice Math Subject GRE 0568, problem 44.

Homework Statement



A fair coin is to be tossed 100 times, with each toss resulting in a head or a tail. If H is total the number of heads and T is the total number of tails, which of the following events has the greatest probability?
(A) [tex]H = 50 [/tex]
(B) [tex]T \ge 60 [/tex]
(C) [tex]51 \le H \le 55 [/tex]
(D) [tex]H \ge 48 [/tex] and [tex]T \ge 48[/tex]
(E) [tex]H \le 5 [/tex] or [tex]H \ge 95 [/tex]

Homework Equations


The Attempt at a Solution



Well, I can set up expressions that give the probabilities. For instance, (A) is obviously

[tex] \binom{100}{50}(1/2)^{100} [/tex]

And (B) just involves summing similar terms for values 60 through 100. But how to compare these? It's not too hard to figure out that A, C, or E can't be right, but how do I compare B vs. D? Another Math Subject GRE practice test I have has a similar problem.

Thanks.
 
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  • #2
Since the number of flips is sufficiently great, these probabilities can be approximated with a normal distribution. Don't forget to use continuity corrections.

I hope this is helpful.

--Elucidus
 
  • #3
Thanks for your reply. Unfortunately I don't actually know any probability beyond the basics of counting. I am aware of the normal distribution and the fact that it can be used to approximate these kinds of probabilities, but I don't know how to go about doing that. Is this something that can be done in the 2-3 minutes I can afford to spend on a GRE question?
 
  • #4
I don't think the math to switch to the normal is hard (quite possibly easier than the counting), but you'd need a table to be able to get an answer, wouldn't you? Do you know if you get a table for the normal distribution during the test?
 
  • #5
No table. You just get a pencil and scratch paper.
 
  • #6
Well that certainly puts a cramp in things.

Relating all options to H we get:

(a) H = 50
(b) [itex]H \leq 39[/itex]
(c) [itex]51 \leq H \leq 55[/itex]
(d) [itex]48 \leq H \leq 52[/tex]
(e) [itex]H \leq 5 \text{ or } H \geq 95[/itex]

Since this is a (fair) coin flipping problem, the probability for H = h flips for any h = 0, 1, 2, ..., 100 is:

[tex]P(H=h) = \left( _h^{100} \right) \cdot \frac{1}{2^{100}}[/tex]

Since the second factor is always 1/2100, then this really boils down to comparison of binomial coefficients.

Part (a) is obviously the easiest. Part (b) seems the worst to evaluate while the rest are sums of 5 or so terms.

Keep in mind

[tex]\sum_{h=0}^{49} \left( _h^{100} \right) = \sum_{h=51}^{100} \left( _h^{100} \right) = \frac{1}{2} \cdot [1 - \left( _{50}^{100} \right) ][/tex]

[itex]P(H \leq 39) = P(H \leq 49) - P(40 \leq H \leq 49)[/itex]

I may be having a rather opaque day, but this seems a really mucky exercise. The only other thing I can think of is that the standard deviation for H is 5, and one can exploit the approximate probability for falling in between multiples of [itex]\sigma[/itex], but even that faces some wrinkles (like option d).

--Elucidus
 

1. What is the difference between permutation and combination?

Permutation and combination are both methods used to calculate the number of possible outcomes in a given scenario. However, permutation is used when order matters (e.g. arranging a sequence of letters), while combination is used when order does not matter (e.g. choosing a committee from a group of people).

2. How do I calculate the probability of an event?

The probability of an event is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This is represented by the formula P(A) = # of favorable outcomes / # of total outcomes.

3. What is the difference between independent and dependent events?

Independent events are events where the outcome of one event does not affect the outcome of another event. Dependent events, on the other hand, are events where the outcome of one event affects the outcome of another event.

4. What is the difference between mutually exclusive and inclusive events?

Mutually exclusive events are events that cannot occur at the same time. Inclusive events, on the other hand, are events that can occur at the same time. For example, rolling an even number and rolling a number greater than 4 are mutually exclusive events, while rolling a number less than 5 and rolling a prime number are inclusive events.

5. How do I use the binomial distribution to solve probability problems?

The binomial distribution is a probability distribution that is used to calculate the probability of a certain number of successes in a given number of trials. It is represented by the formula 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.

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