1. Chapter 8 Binomial and Geometric Distributions
Binomial vs. Geometric
Chapter 8
Binomial and Geometric Distributions

2. Binomial vs. Geometric The Binomial Setting The Geometric Setting
Each observation falls into
one of two categories.
1. Each observation falls into
one of two categories.
The probability of success
is the same for each
observation.
2. The probability of success
is the same for each
observation.
The observations are all
independent.
The observations are all
independent.
There is a fixed number n
of observations.
4. The variable of interest is the
number of trials required to
obtain the 1st success.

3. For each of the following situations, determine if the situation is binomial, geometric, or neither.
You observe the gender of the next 20 children born at a local hospital; X is the number of girls among them.
Bobby draws cards from a deck until he gets an ace. He then reshuffles, and starts over and repeats this process until he “wins” 10 times. The count X is the total number of cards he counted until he finished.
Joe buys a “Texas 2-Step” lottery ticket every week until he wins. He has a probability of of winning in any given week.

4. Binomial vs. Geometric The Binomial Setting The Geometric Setting
Each observation falls into
one of two categories.
1. Each observation falls into
one of two categories.
The probability of success
is the same for each
observation.
2. The probability of success
is the same for each
observation.
The observations are all
independent.
The observations are all
independent.
There is a fixed number n
of observations.
4. The variable of interest is the
number of trials required to
obtain the 1st success.

5. Combinations
Formula:
Practice:

6. Developing the Binomial Formula
P(O) = .25
P(Oc) = .75
Outcomes
Probability
Rewritten
OcOcOc
OOcOc
OcOOc
OcOcO
OOOc
OOcO
OcOO
OOO

7. Developing the Binomial Formula
n = # of observations
p = probablity of success
k = given value of variable
Rewritten

8. The Mean and Standard Deviation of a Binomial Random Variable
If a count X has the binomial distribution with number of observations n and probability of a success p…
Mean:
Standard Deviation:

9. Probability Distribution Function (pdf)
Given a discrete random variable X, the pdf assigns a probability to each value of X.

10. Cumulative distribution Function (cdf)
Given a random variable X, cdf calculates the sum of the probabilities for 0, 1, 2, …, X.
It calculates the probability of obtaining at most X successes in “n” trials.

11. Calculator Functions:
pdf: means X = ?
2nd Distr. -> 0 (binompdf)
binompdf(n, p, X)
cdf: means X ≤ ?
2nd Distr. -> A (binomcdf)
binomcdf(n, p, X)

12. Ex 1. The count X of children with type O blood among 5 children whose parents carry genes for both O and A blood types is B(5, .25).
Find P(X = 2).
Find P(X < 4)

13. Define the variable. Complete the table.
Ex 2. Angel is a basketball player who makes 75% of her free throws over the course of a season. At last night’s game, she missed 5 of 7 free throws. Assume that each free throw is independent of one another…studies on long sequences of free throws have found no evidence that they are dependent.
Define the variable.
Complete the table. X
P(X)
F(X)

14. What is the probability that Angel makes at most 6 free throws in a row?

15. What is the probability that Angel makes more than 3 free throw shots?

16. Find the mean and Standard Deviation of the following random variable.
According to a recent Census Bureau report, 12.7% of Americans live below the poverty level. Suppose you plan to sample at random 100 Americans and count the number of people who live below the poverty level.

17. Binomial vs. Geometric The Binomial Setting The Geometric Setting
Each observation falls into
one of two categories.
1. Each observation falls into
one of two categories.
The probability of success
is the same for each
observation.
2. The probability of success
is the same for each
observation.
The observations are all
independent.
The observations are all
independent.
There is a fixed number n
of observations.
4. The variable of interest is the
number of trials required to
obtain the 1st success.

18. Developing the Geometric Formula
X = wanted outcome O
Probability of a Success: P(O) =
Probability of a Failure: P(Oc) = X
Probability

19. The Mean and Standard Deviation of a Geometric Random Variable
If X is a geometric random variable with probability of success p on each trial, the expected value of the random variable (the expected number of trials to get the first success) is

20. Calculator Functions:
pdf: means X = ?
2nd Vars (Distr.) -> D (geometpdf)
geometpdf(p, X)
cdf: means X ≤ ?
2nd Vars (Distr.) -> E (geometcdf)
geometcdf(p, X)

21. Ex 2. Creating a Geometric Probability Distribution table for X = number rolls of a die until a 4 occurs:
The probability of rolling a 4 on die =
A geometric probability does not have a fixed ending value “n”, therefore it is considered an infinite sequence. Meaning the “x” goes on infinitely. X 1 2 3 4 5 …
P(X)

22. Ex 2. What is the probability that it takes more than 6 rolls to observe a 4?

23. Ex 3. What is the probability that it takes at least 3 rolls to observe a 4?

24. Find the mean and Standard Deviation of the following random variable.
A potential buyer will sample videotapes from a large lot of new videotapes. If she finds at least one defective one, she’ll reject the entire lot. If ten percent of the lot is defective, what is the probability that she’ll find a defective tape by the 4th videotape?