How Is the Probability of the First Head on Odd Flips Calculated

In summary, The formula for determining the probability that the first head appears on an odd number of flips is obtained by taking the sum of a geometric series. This is derived from the fact that the sequence of events requires an even number of tails followed by a head. The formula is represented by P[X] = (1/2) / (1 - 1/4), and the summation is equivalent to P(n=2k) = (1/2)(2k+1) for k=0 to infinity.
  • #1
PMH_1
2
0
Can anyone explain to me how this problem is solved

Determine the probability that the first head appears on an odd number of flips i.e. X contains {1,3,5..}.

P[X] = summation starting at x = 1 to infinity (1/2)^(2x-1) = 1/2 / (1 - 1/4) = 2/3

Basically my question is, how is the formula for P[X] obtained:
1. 1/2 / (1 - 1/4) : where does this come from?
2. summation starting at x = 1 to infinity (1/2)^(2x-1) and this as well
 
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  • #2
1. comes from 2. (sum of geometric series).

2. is derived from the fact that you need a sequence of an even number of tails followed by a head. Let n be number of tails P(n=0)=1/2, P(n=2)=1/8, P(n=4)=1/32. In general, P(n=2k)=(1/2)(2k+1). Sum for k=0, infinity (since events are mutually exclusive and exhaustive). (your x=k+1).
 
  • #3
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The formula for P[X] is obtained using the geometric series formula, which states that the sum of an infinite geometric series with a common ratio r is equal to a / (1 - r), where a is the first term in the series and r is the common ratio. In this case, the first term is (1/2)^(2x-1) and the common ratio is (1/2)^2 = 1/4.

To understand where the formula comes from, let's break down the problem step by step. First, we need to understand what the event X represents. X contains all the possible outcomes where the first head appears on an odd number of flips. In other words, X contains all the possible outcomes where the first head appears on the first, third, fifth, and so on flips.

Now, let's consider each individual flip. The probability of getting a head on any given flip is 1/2, since we have a fair coin. This means that the probability of getting a head on the first flip is 1/2, the probability of getting a head on the third flip is also 1/2, and so on. Therefore, the probability of getting a head on the first, third, fifth, and so on flips is (1/2)^2, (1/2)^4, (1/2)^6, and so on. We can see that this forms a geometric series with a first term of (1/2)^2 and a common ratio of (1/2)^2.

Now, we can use the geometric series formula to calculate the sum of this infinite series. The first term is (1/2)^2 and the common ratio is (1/2)^2, so we have a = (1/2)^2 and r = (1/2)^2. Plugging these values into the formula, we get:

P[X] = (1/2)^2 / (1 - (1/2)^2) = (1/4) / (1 - 1/4) = (1/4) / (3/4) = 1/3

However, this is not the probability we are looking for. We want the probability of getting a head on an odd number of flips, which includes the first, third, fifth, and so on flips. So, we need to sum up the probabilities of all these
 

FAQ: How Is the Probability of the First Head on Odd Flips Calculated

What is Discrete Probability?

Discrete Probability is a branch of mathematics that deals with the likelihood of events or outcomes that can only take on a finite or countable number of values. This differs from Continuous Probability, which deals with events or outcomes that can take on an infinite number of values.

What are some examples of Discrete Probability?

Some common examples of Discrete Probability include flipping a coin, rolling a dice, and drawing cards from a deck. In each of these cases, there are a finite number of possible outcomes, making it a discrete event.

How is Discrete Probability used in real life?

Discrete Probability is used in many real-life situations, such as predicting the outcome of a sports game, analyzing the results of a survey, or determining the chances of winning a game of chance. It is also commonly used in fields such as finance, engineering, and computer science.

What is the difference between Discrete and Continuous Probability Distributions?

The main difference between Discrete and Continuous Probability Distributions is that Discrete Distributions deal with events or outcomes that can only take on a finite or countable number of values, while Continuous Distributions deal with events or outcomes that can take on an infinite number of values. This means that Discrete Distributions can be represented by a probability mass function, while Continuous Distributions are represented by a probability density function.

How can I calculate probabilities in Discrete Probability?

To calculate probabilities in Discrete Probability, you can use various formulas and techniques, such as the basic counting principles, the Binomial Distribution, the Geometric Distribution, or the Poisson Distribution. You can also use tables or computer software to assist with calculations. It is important to understand the specific problem and choose the appropriate method for calculation.

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