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Negative Binomial Distribution
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Statistics Tutorial
Negative Binomial Distribution
Descriptive Statistics
In this lesson, we cover the negative binomial distribution and the geometric distribution. As we will
► Quantitative measures
see, the geometric distribution is a special case of the negative binomial distribution.
► Charts and graphs
► Tabular displays
Probability
Negative Binomial Experiment
A negative binomial experiment is a statistical experiment that has the following properties:
► Probability basics
The experiment consists of x repeated trials.
► Probability laws
Each trial can result in just two possible outcomes. We call one of these outcomes a success
► Random variables
and the other, a failure.
► Sampling theory
The probability of success, denoted by P, is the same on every trial.
Distributions
The trials are independent; that is, the outcome on one trial does not affect the outcome on other
► Distribution basics
trials.
▼ Discrete
The experiment continues until r successes are observed, where r is specified in advance.
Binomial distribution
Negative binomial
Hypergeometric
Multinomial
Poisson
► Continuous
Estimation
Consider the following statistical experiment. You flip a coin repeatedly and count the number of times
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Approved for AP Statistics and
Calculus
Statistics in Plain English, Third
Edition
Timothy C. Urdan
the coin lands on heads. You continue flipping the coin until it has landed 5 times on heads. This is a
negative binomial experiment because:
The experiment consists of repeated trials. We flip a coin repeatedly until it has landed 5 times
on heads.
Each trial can result in just two possible outcomes ­ heads or tails.
► Estimation theory
The probability of success is constant ­ 0.5 on every trial.
► Proportions
The trials are independent; that is, getting heads on one trial does not affect whether we get
► Mean scores
heads on other trials.
Hypothesis Testing
The experiment continues until a fixed number of successes have occurred; in this case, 5
► Foundations of testing
heads.
► Mean scores
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AP Statistics: NEW 3rd Edition
(Advanced Placement (AP)
Test Preparation)
Robin Levine­Wissing, David
Thiel
Notation
► Proportions
► Power
The following notation is helpful, when we talk about negative binomial probability.
► Chi­square tests
x: The number of trials required to produce r successes in a negative binomial experiment.
Survey Sampling
r: The number of successes in the negative binomial experiment.
► Sampling methods
P: The probability of success on an individual trial.
► Simple random samples
Q: The probability of failure on an individual trial. (This is equal to 1 ­ P.)
► Stratified samples
► Cluster samples
► Sample planning
More Applied Statistics
b*(x; r, P): Negative binomial probability ­ the probability that an x­trial negative binomial
Principles of Statistics (Dover
Books on Mathematics)
M.G. Bulmer
experiment results in the rth success on the xth trial, when the probability of success on an
individual trial is P.
nC r: The number of combinations of n things, taken r at a time.
► Linear regression
► Regression tests
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Negative Binomial Distribution
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► Experiments
Appendices
■ Notation
■ Statistics Formulas
Negative Binomial Distribution
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A negative binomial random variable is the number X of repeated trials to produce r successes in a
negative binomial experiment. The probability distribution of a negative binomial random variable is
called a negative binomial distribution. The negative binomial distribution is also known as the
Pascal distribution.
Excel 2007 Data Analysis For
Dummies
Stephen L. Nelson
Suppose we flip a coin repeatedly and count the number of heads (successes). If we continue flipping
the coin until it has landed 2 times on heads, we are conducting a negative binomial experiment. The
negative binomial random variable is the number of coin flips required to achieve 2 heads. In this
example, the number of coin flips is a random variable that can take on any integer value between 2
and plus infinity. The negative binomial probability distribution for this example is presented below.
Number of coin flips
Probability
2
0.25
3
0.25
4
0.1875
5
0.125
6
0.078125
7 or more
0.109375
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Statistics Hacks: Tips & Tools
for Measuring the World and
Beating the Odds
Bruce Frey
Negative Binomial Probability
The negative binomial probability refers to the probability that a negative binomial experiment results
in r ­ 1 successes after trial x ­ 1 and r successes after trial x. For example, in the above table, we see
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that the negative binomial probability of getting the second head on the sixth flip of the coin is
0.078125.
Given x, r, and P, we can compute the negative binomial probability based on the following formula:
Statistics (Cliffs Quick Review)
David H. Voelker, Peter Z.
Orton, Scott Adams
Negative Binomial Formula. Suppose a negative binomial experiment consists
of x trials and results in r successes. If the probability of success on an individual
trial is P, then the negative binomial probability is:
b*(x; r, P) = x­1Cr­1 * Pr * (1 ­ P)x ­ r
The Mean of the Negative Binomial Distribution
If we define the mean of the negative binomial distribution as the average number of trials required to
produce r successes, then the mean is equal to:
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Practical Statistics Simply
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Explaining Science)
Russell Langley
μ = r / P
where μ is the mean number of trials, r is the number of successes, and P is the probability of a
success on any given trial.
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Alternative Views of the Negative Binomial Distribution
As if statistics weren't challenging enough, the above definition is not the only definition for the
negative binomial distribution. Two common alternative definitions are:
Probability For Dummies
Deborah Rumsey
The negative binomial random variable is R, the number of successes before the binomial
experiment results in k failures. The mean of R is:
μR = kP/Q
The negative binomial random variable is K, the number of failures before the binomial
experiment results in r successes. The mean of K is:
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μK = rQ/P
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Negative Binomial Distribution
The moral: If someone talks about a negative binomial distribution, find out how they are defining
the negative binomial random variable.
On this web site, when we refer to the negative binomial distribution, we are talking about the
What is a p­value anyway? 34
Stories to Help You Actually
Understand Statistics
Andrew J. Vickers
definition presented earlier. That is, we are defining the negative binomial random variable as X,
the total number of trials required for the binomial experiment to produce r successes.
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Geometric Distribution
The geometric distribution is a special case of the negative binomial distribution. It deals with the
number of trials required for a single success. Thus, the geometric distribution is negative binomial
distribution where the number of successes (r) is equal to 1.
Texas Instruments TI­86
Graphing Calculator
An example of a geometric distribution would be tossing a coin until it lands on heads. We might ask:
What is the probability that the first head occurs on the third flip? That probability is referred to as a
geometric probability and is denoted by g(x; P). The formula for geometric probability is given below.
Geometric Probability Formula. Suppose a negative binomial experiment
consists of x trials and results in one success. If the probability of success on an
individual trial is P, then the geometric probability is:
g(x; P) = P * Qx ­ 1
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Approved for AP Statistics and
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Sample Problems
The problems below show how to apply your new­found knowledge of the negative binomial distribution
(see Example 1) and the geometric distribution (see Example 2).
Negative Binomial Calculator
As you may have noticed, the negative binomial formula requires some potentially time­
consuming computations. The Negative Binomial Calculator can do this work for you ­ quickly,
easily, and error­free. Use the Negative Binomial Calculator to compute negative binomial
probabilities and geometric probabilities. The calculator is free. It can be found under the Stat
Tables tab, which appears in the header of every Stat Trek web page.
Negative Binomial Calculator
Example 1 Bob is a high school basketball player. He is a 70% free throw shooter. That means his probability of
making a free throw is 0.70. During the season, what is the probability that Bob makes his third free
throw on his fifth shot?
Solution: This is an example of a negative binomial experiment. The probability of success (P) is 0.70,
the number of trials (x) is 5, and the number of successes (r) is 3.
To solve this problem, we enter these values into the negative binomial formula.
b*(x; r, P) = x­1Cr­1 * Pr * Qx ­ r b*(5; 3, 0.7) = 4C2 * 0.73 * 0.32 b*(5; 3, 0.7) = 6 * 0.343 * 0.09 = 0.18522
Thus, the probability that Bob will make his third successful free throw on his fifth shot is 0.18522.
Example 2 stattrek.com/probability-distributions/negative-binomial.aspx?Tutorial=Stat
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Negative Binomial Distribution
Let's reconsider the above problem from Example 1. This time, we'll ask a slightly different question:
What is the probability that Bob makes his first free throw on his fifth shot?
Solution: This is an example of a geometric distribution, which is a special case of a negative binomial
distribution. Therefore, this problem can be solved using the negative binomial formula or the geometric
formula. We demonstrate each approach below, beginning with the negative binomial formula.
The probability of success (P) is 0.70, the number of trials (x) is 5, and the number of successes (r) is
1. We enter these values into the negative binomial formula.
b*(x; r, P) = x­1Cr­1 * Pr * Qx ­ r b*(5; 1, 0.7) = 4C0 * 0.71 * 0.34 b*(5; 3, 0.7) = 0.00567
Now, we demonstate a solution based on the geometric formula.
g(x; P) = P * Qx ­ 1 g(5; 0.7) = 0.7 * 0.34 = 0.00567
Notice that each approach yields the same answer.
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