Financial Modeling Tutorials
- Financial Modeling Basics
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- Future Value Formula
- Present Value Factor
- Perpetuity Formula
- Present Value vs Future Value
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- Present Value of an Annuity
- Doubling Time Formula
- Annuity Formula
- Annuity vs Perpetuity
- Annuity vs Lump Sum
- Internal Rate of Return (IRR)
- NPV vs XNPV
- NPV vs IRR
- NPV Formula
- PV vs NPV
- IRR vs ROI
- Break Even Point
- Payback Period & Discounted Payback Period
- Payback period Formula
- Discounted Payback Period Formula
- Profitability Index
- Cash Burn Rate
- Simple Interest
- Simple Interest vs Compound Interest
- Simple Interest Formula
- CAGR Formula (Compounded Annual Growth Rate)
- Effective Interest Rate
- Loan Amortization Schedule
- Mortgage Formula
- Loan Principal Amount
- Interest Rate Formula
- Rate of Return Formula
- Effective Annual Rate
- Effective Annual Rate Formula (EAR)
- Daily Compound Interest
- Monthly Compound Interest Formula
- Discount Rate vs Interest Rate
- Rule of 72
- Geometric Mean Return
- Real Rate of Return Formula
- Continuous compounding Formula
- Weighted average Formula
- Average Formula
- Average Rate of Return Formula
- Mean Formula
- Weighted Mean Formula
- Harmonic Mean Formula
- Median Formula in Statistics
- Range Formula
- Expected Value Formula
- Exponential Growth Formula
- Margin of Error Formula
- Decrease Percentage Formula
- Percent Error Formula
- Holding Period Return Formula
- Cost Benefit Analysis
- Cost Volume Profit Analysis
- Opportunity Cost Formula
- Mortgage APR vs Interest Rate
- Regression Formula
- Correlation Coefficient Formula
- Covariance Formula
- Coefficient of Variation Formula
- Sample Standard Deviation Formula
- Relative Standard Deviation Formula
- Volatility Formula
- Binomial Distribution Formula
- Quartile Formula
- P Value Formula
- Skewness Formula
- Regression vs ANOVA
Binomial Distribution Formula (Table of Contents)
What is the Binomial Distribution Formula?
The binomial distribution formula provides us with the frequency distribution of the possible number of successful outcomes in a given number of trials where each of these given trials has the same probability of success. Each trial in a binomial experiment can result in just two possible outcomes. Hence, the name is ‘binomial’. One of these outcomes is known as success and the other as a failure. For instance, people who are sick may respond to a treatment or not. Similarly, when we toss a coin, we can have only two types of outcomes: heads or tails. The binomial distribution is a discrete distribution used in statistics, which is different from a continuous distribution.
The probability of obtaining x successes in n independent trials of a binomial experiment is given by the following formula of binomial distribution:
where p is the probability of success
In the above equation of binomial distribution, _{n}C_{x }is used, which is nothing but combinations formula. The formula to calculate combinations is given as _{n}C_{x }= n! / x! (n-x)! where n represents the number of items (independent trials) and x represents the number of items being chosen at a time (successes).
In case n=1 in a binomial distribution equation, the distribution is known as Bernoulli distribution. The mean of a binomial distribution is np. The variance of the binomial distribution is np(1-p).
Explanation of the Binomial Distribution Formula
The formula for the calculation of binomial distribution can be derived by using the following four simple steps:
Step 1: Calculate the combination between the number of trials and the number of successes. The formula for _{n}C_{x }is where n! = n*(n-1)*(n-2) . . . *2*1. For a number n, the factorial of n can be written as, n! = n*(n-1)!
For instance, 5! is 5*4*3*2*1
Step 2: Calculate the probability of success raised to the power of the number of successes that is p^{x}.
Step 3: Calculate the probability of failure raised to the power of the difference between the number of successes and the number of trials. The probability of failure is 1-p. Thus, this refers to obtaining (1-p)^{ n-x}
Step 4: Find out the product of the results obtained in Step 1, Step 2 and Step 3.
Examples of Binomial Distribution Formula
Let’s see some simple to advanced examples of binomial distribution equation to understand it better.
Example #1
The number of trials (n) is 10. The probability of success (p) is 0.5. Do the calculation of binomial distribution to calculate the probability of getting exactly 6 successes.
Solution:
Use the following data for the calculation of binomial distribution.
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Calculation of binomial distribution can be done as follows,
P(x=6) = _{10}C_{6}*(0.5)^{6}(1-0.5)^{10-6}
^{ }= (10!/6!(10-6)!)*0.015625*(0.5)^{4}
^{ }= 210*0.015625*0.0625
Probability of Getting Exactly 6 Successes will be-
P(x=6) = 0.2051
The probability of getting exactly 6 successes is 0.2051
Example #2
A manager of an insurance company goes through the data of insurance policies sold by insurance salesmen working under him. He finds that 80% of the people who purchase motor insurance are men. He wants to find out that if 8 motor insurance owners are randomly selected, what would be the probability that exactly 5 of them are men.
Solution: We first have to find out what are n, p, and x.
Calculation of binomial distribution can be done as follows,
P(x=5) = _{8}C_{5}*(0.8)^{5}(1-0.8)^{8-5}
^{ }= (8! /5! (8-5)! )*0.32768*(0.2)^{3}
^{ }= 56*0.32768*0.008
Probability of Exactly 5 Successes will be-
P(x=5) = 0.14680064
The probability of exactly 5 motor insurance owners being men is 0.14680064.
Example #3
Hospital management is excited about the introduction of a new drug for treating cancer patients as the chance of a person being successfully treated by it is very high. The probability of a patient being successfully treated by the drug is 0.8. The drug is given to 10 patients. Find the probability of 9 or more patients being successfully treated by it.
Solution: We first have to find out what is n, p, and x.
We have to find the probability of 9 or more patients being successfully treated by it. Thus, either 9 or 10 patients are successfully treated by it
x (number that you have to find probability for) = 9 or x = 10
We have to find P(9) and P(10)
Calculation of binomial distribution to find P(x=9) can be done as follows,
P(x=9) = _{10}C_{9}*(0.8)^{9}(1-0.8)^{10-9}
^{ } = (10! /9! (10-9)!)*0.134217728*(0.2)^{1}
^{ }= 10*0.134217728*0.2
Probability of 9 Patients will be-
P(x=9) = 0.2684
Calculation of binomial distribution to find P(x=10) can be done as follows,
P(x=10) = _{10}C_{10}*(0.8)^{10}(1-0.8)^{10-10}
^{ }= (10!/10! (10-10)!)*0.107374182*(0.2)^{0}
^{ }= 1*0.107374182*1
The probability of 10 Patients will be-
P(x=10) = 0.1074
Therefore, P(x=9)+P(x=10) = 0.268 + 0.1074
= 0.3758
Thus, the probability of 9 or more patients being treated by the drug is 0.375809638. ^{ }
Binomial Distribution Formula Calculator
You can use the following calculator.
n | |
p | |
x | |
Binomial Distribution Formula = | |
Binomial Distribution Formula = | _{n}C_{x} * p^{x} * (1 -p)^{n-x} | |
_{0}C_{0} * 0^{0} * (1-0)^{0-0} = | 0 |
Relevance and Use
A binomial distribution formula is used when the following four conditions are satisfied:
- There are only two outcomes
- The probability of each outcome remains constant from trial to trial
- There are a fixed number of trials
- Each trial is independent i.e. mutually exclusive of others
Example of a binomial experiment is tossing of a coin, say thrice. When we flip a coin, only 2 outcomes are possible – heads and tails. The probability of each outcome is 0.5. Since the coin is tossed thrice, the number of trials is fixed that is 3. The probability of each toss is not influenced by other tosses.
Binomial distribution finds its applications in social science statistics. It is used for developing models for dichotomous outcome variables where there are two outcomes. An example of this is whether Republicans or Democrats would win the election.
Binomial Distribution Formula in Excel (with excel template)
Saurabh learned about binomial distribution equation in school. He wants to discuss the concept with his sister and have a bet with her. He thought that he would toss an unbiased coin 10 times. He wants to bet $100 on getting exactly 5 tails in 10 tosses. For the purpose of this bet, he wants to compute the probability of getting exactly 5 tails in 10 tosses.
Solution: We first have to find out what is n, p, and x.
There is an inbuilt formula for binomial distribution is Excel which is
It is BINOM.DIST(number of successes, trials, a probability of success, FALSE).
For this example of the binomial distribution formula would be:
=BINOM.DIST(B2, B3, B4, FALSE) where cell B2 represents the number of successes, cell B3 represents the number of trials and cell B4 represents the probability of success.
Therefore, the calculation of Binomial Distribution will be-
P(x=5) = 0.24609375
The probability of getting exactly 5 tails in 10 tosses is 0.24609375
Recommended Articles
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