What is Mean and Variance of Binomial Distribution?

The binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent experiments, each of which yields success with probability $p$.

How to find Mean and Variance of Binomial Distribution

The binomial distribution is a discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.

Mean of Binomial Distribution

The mean of a binomial distribution is given by the formula:

$$E(X) = np$$

where:

• $E(X)$ is the mean of the binomial distribution
• $n$ is the number of trials
• $p$ is the probability of success on each trial

Variance of Binomial Distribution

The variance of a binomial distribution is given by the formula:

$$V(X) = np(1-p)$$

where:

• $V(X)$ is the variance of the binomial distribution
• $n$ is the number of trials
• $p$ is the probability of success on each trial

Example

Suppose we have a binomial distribution with $n = 10$ and $p = 0.5$. Then the mean of the distribution is:

$$E(X) = 10 * 0.5 = 5$$

And the variance of the distribution is:

$$V(X) = 10 * 0.5 * (1-0.5) = 2.5$$

Applications of Binomial Distribution

The binomial distribution is used in a variety of applications, including:

• Quality control
• Reliability engineering
• Medical research
• Social science research

The binomial distribution is a powerful tool for modeling the number of successes in a sequence of independent yes/no experiments. The mean and variance of a binomial distribution can be used to understand the expected value and spread of the distribution.

Derivation of Mean and Variance of Binomial Distribution

The binomial distribution is a discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.

Mean of the Binomial Distribution

The mean of the binomial distribution is given by:

$$E(X) = np$$

where:

• $E(X)$ is the mean of the binomial distribution
• $n$ is the number of experiments
• $p$ is the probability of success on each experiment

Derivation:

The mean of a random variable $X$ is defined as the expected value of $X$, which is given by:

$$E(X) = \sum_{x=0}^n xP(X = x)$$

where $P(X = x)$ is the probability of $X$ taking on the value $x$.

For the binomial distribution, the probability of $X = x$ is given by:

$$P(X = x) = \binom{n}{x}p^x(1-p)^{n-x}$$

where $\binom{n}{x}$ is the binomial coefficient, which is the number of ways to choose $x$ objects from a set of $n$ objects.

Substituting this into the equation for the mean, we get:

$$E(X) = \sum_{x=0}^n x \binom{n}{x}p^x(1-p)^{n-x}$$

We can simplify this expression by using the following identity:

$$\sum_{x=0}^n x \binom{n}{x} = n$$

This identity can be proven using induction.

Substituting this identity into the equation for the mean, we get:

$$E(X) = np\sum_{x=0}^n \binom{n}{x}p^{x-1}(1-p)^{n-x}$$

The sum in this expression is equal to 1, so we can simplify it to:

$$E(X) = np$$

Therefore, the mean of the binomial distribution is $np$.

Variance of the Binomial Distribution

The variance of the binomial distribution is given by:

$$V(X) = np(1-p)$$

where:

• $V(X)$ is the variance of the binomial distribution
• $n$ is the number of experiments
• $p$ is the probability of success on each experiment

Derivation:

The variance of a random variable $X$ is defined as the expected value of the squared deviation of $X$ from its mean, which is given by:

$$V(X) = E[(X - E(X))^2]$$

where $E(X)$ is the mean of $X$.

For the binomial distribution, the mean is given by $np$. Substituting this into the equation for the variance, we get:

$$V(X) = E[(X - np)^2]$$

Expanding the square, we get:

$$V(X) = E[X^2 - 2npX + n^2p^2]$$

We can simplify this expression by using the following identities:

$$E(X^2) = \sum_{x=0}^n x^2P(X = x)$$

$$E(XY) = \sum_{x=0}^n x yP(X = x, Y = y)$$

where $P(X = x, Y = y)$ is the joint probability of $X$ taking on the value $x$ and $Y$ taking on the value $y$.

Using these identities, we can simplify the equation for the variance to:

$$V(X) = np(1-p)$$

Therefore, the variance of the binomial distribution is $np(1-p)$.

Mean and Variance of Negative Binomial Distribution

The negative binomial distribution is a discrete probability distribution that describes the number of successes until a specified number of failures. It is a generalization of the geometric distribution, which describes the number of successes until the first failure.

Mean of Negative Binomial Distribution

The mean of the negative binomial distribution is given by:

$$E(X) = \frac{r \theta}{1-\theta}$$

where:

• $X$ is the random variable counting the number of successes until the $r^{th}$ failure
• $r$ is the number of failures
• $\theta$ is the probability of success on each trial

Variance of Negative Binomial Distribution

The variance of the negative binomial distribution is given by:

$$V(X) = \frac{r \theta}{(1-\theta)^2}$$

Properties of the Mean and Variance of the Negative Binomial Distribution

• The mean of the negative binomial distribution is always greater than or equal to the mean of the geometric distribution.
• The variance of the negative binomial distribution is always greater than or equal to the variance of the geometric distribution.
• The mean and variance of the negative binomial distribution are both increasing functions of $r$.
• The mean and variance of the negative binomial distribution are both decreasing functions of $\theta$.

Applications of the Negative Binomial Distribution

The negative binomial distribution is used in a variety of applications, including:

• Quality control: The negative binomial distribution can be used to model the number of defects in a manufactured product.
• Insurance: The negative binomial distribution can be used to model the number of claims filed by an insurance policyholder.
• Reliability engineering: The negative binomial distribution can be used to model the number of failures of a component before it is replaced.

Special Case: The Mean and Variance of Binomial Distribution are same if

In a binomial distribution, the mean and variance are typically different. However, there is one special case where the mean and variance are equal. This occurs when the probability of success, $p$, is equal to 0.5.

Proof

The mean of a binomial distribution is given by:

$$E(X) = np$$

And the variance is given by:

$$V(X) = np(1-p)$$

If $p = 0.5$, then:

$$E(X) = n(0.5) = \frac{n}{2}$$

$$V(X) = n(0.5)(1-0.5) = \frac{n}{4}$$

Therefore, if $p = 0.5$, the mean and variance of a binomial distribution are both equal to $n/4$.

Intuition

This result can be understood intuitively by considering the following scenario. Suppose you are flipping a fair coin $n$ times. The probability of getting heads on any given flip is 0.5. If you flip the coin $n$ times, you would expect to get heads about $n/2$ times. This is because the number of heads is binomially distributed with $n$ trials and $p = 0.5$.

Now, consider the variance of the number of heads. The variance is a measure of how spread out the data is. In this case, the data is spread out evenly around the mean. This is because the probability of getting any number of heads from 0 to $n$ is the same.

Therefore, if $p = 0.5$, the mean and variance of a binomial distribution are both equal to $n/4$. This is because the distribution is symmetric around the mean and the data is spread out evenly around the mean.

Properties of Mean and Variance of Binomial Distribution

The binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent experiments, each of which yields success with probability $p$. The mean and variance of the binomial distribution have several important properties.

The mean and variance of the binomial distribution have several important properties:

• The mean of the binomial distribution is a linear function of $n$. This means that the mean increases in proportion to the number of experiments.
• The variance of the binomial distribution is a quadratic function of $n$. This means that the variance increases more slowly than the mean as the number of experiments increases.
• The mean and variance of the binomial distribution are independent of the probability of success $p$. This means that the shape of the distribution does not change as the probability of success changes.

These properties are useful for understanding the behavior of the binomial distribution and for making inferences about the probability of success in a binomial experiment.

Applications of Mean and Variance of Binomial Distribution

The mean and variance of the binomial distribution have various applications in different fields. Here are a few examples:

1. Quality Control:

• In quality control, the binomial distribution is used to determine the probability of obtaining a certain number of defective items in a sample of a given size. This information can be used to set quality standards and make decisions about accepting or rejecting batches of products.

2. Medical Research:

• In medical research, the binomial distribution is used to analyze the effectiveness of treatments or interventions. For example, a researcher may use the binomial distribution to determine the probability of a patient recovering from a disease after receiving a new treatment.

3. Survey Sampling:

• In survey sampling, the binomial distribution is used to estimate the proportion of a population that has a certain characteristic. For example, a pollster may use the binomial distribution to estimate the proportion of voters who will vote for a particular candidate in an upcoming election.

4. Insurance:

• In insurance, the binomial distribution is used to calculate premiums and assess risks. For example, an insurance company may use the binomial distribution to determine the probability of a car accident occurring in a given year and set premiums accordingly.

5. Financial Analysis:

• In financial analysis, the binomial distribution is used to model the probability of success or failure of a financial investment. For example, an investor may use the binomial distribution to determine the probability of a stock price increasing or decreasing over a certain period.

6. Sports Analytics:

• In sports analytics, the binomial distribution is used to analyze the performance of athletes and teams. For example, a sports analyst may use the binomial distribution to determine the probability of a basketball player making a free throw or a baseball player getting a hit.

7. Social Science Research:

• In social science research, the binomial distribution is used to analyze the behavior and preferences of individuals and groups. For example, a social scientist may use the binomial distribution to determine the probability of a person voting for a particular political party or choosing a particular brand of product.

8. Engineering and Manufacturing:

• In engineering and manufacturing, the binomial distribution is used to analyze the reliability and quality of products and systems. For example, an engineer may use the binomial distribution to determine the probability of a component failing or a product meeting certain specifications.

9. Education:

• In education, the binomial distribution is used to analyze the performance of students on tests and assessments. For example, a teacher may use the binomial distribution to determine the probability of a student answering a question correctly or passing a test.

10. Marketing and Advertising:

• In marketing and advertising, the binomial distribution is used to analyze the effectiveness of marketing campaigns and advertising strategies. For example, a marketer may use the binomial distribution to determine the probability of a customer purchasing a product after seeing an advertisement.

These are just a few examples of the many applications of the mean and variance of the binomial distribution. This versatile distribution is a powerful tool that can be used to analyze a wide variety of problems in different fields.

Mean and Variance of Binomial Distribution FAQs

What is the mean of a binomial distribution?

• The mean of a binomial distribution is the expected value of the random variable, which is the sum of the products of each possible outcome and its probability.
• For a binomial distribution with parameters $n$ and $p$, the mean is given by:

$$\mu = np$$

• Where:
• $n$ is the number of trials.
• $p$ is the probability of success on each trial.

What is the variance of a binomial distribution?

• The variance of a binomial distribution is a measure of how spread out the distribution is.
• For a binomial distribution with parameters $n$ and $p$, the variance is given by:

$$ \sigma^2 = np(1-p)$$

• Where:
• $n$ is the number of trials.
• $p$ is the probability of success on each trial.

What is the relationship between the mean and variance of a binomial distribution?

• The mean and variance of a binomial distribution are related by the following equation:

$$ \sigma^2 = np(1-p) = n\mu(1-\mu)$$

• This equation shows that the variance is proportional to the mean, and that the variance increases as the mean increases.

How do the mean and variance of a binomial distribution change as $n$ and $p$ change?

• The mean of a binomial distribution increases as $n$ increases and as $p$ increases.
• The variance of a binomial distribution increases as $n$ increases and decreases as $p$ increases.

What are some applications of the binomial distribution?

• The binomial distribution is used in a variety of applications, including:
• Quality control
• Reliability engineering
• Medical research
• Social science research
• Business decision-making