Continuous Random Variables & Probability Distributions
The Normal Distribution
Chapter 4: Continuous Random Variables
and Probability Distributions
Walid Sharabati
Purdue University
February 14, 2014
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 1 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Chapter Overview
Continuous random variables
Probability density function (pdf)
Definition and interpretation
Cumulative distribution function (cdf)
Definition and interpretation
Relationship between cdf and pdf
Expectation, variance and percentile for continuous rv
Some continuous distributions
Uniform and exponential
The Normal distribution
Using normal table
Approximateing the Bionomial distribution
Professor Sharabati (Purdue University)
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Continuous Random Variables
(Slide 2 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Continuous rv and the Probability Density Function
Continuous random variables
Definitions
Examples
Probability density functions (pdf)
Definitions
Interpretations
Examples
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 3 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Continuous rv
Definition
A random variable X is said to be continuous if its set of possible
value includes an entire interval of numbers on the real line.
Example Make depth measurements at a randomly selected
location in a specific lake. Let X = the depth at this location.
X can be any value between 0 and maximum depth M .
Example A chemical compound is randomly selected and let
X = the pH value. X can be any value between 0 and 14.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 4 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Probability Density Function (PDF)
Definition
Let X be a continuous rv. Then a probability distribution or
probability density function (pdf) of X is a function f (x) such
that for any two numbers a and b with a ≤ b,
Z
P (a ≤ X ≤ b) =
b
f (x)dx.
a
The graph of f is the density curve.
i.e., the probability that X falls in [a, b] is the area under the
function f (x) above this interval.
f (x) must satisfies the following:
1
2
f (x) ≥ 0 for all x.
R∞
−∞ f (x)dx = 1
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 5 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Probability Density Function (PDF)
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 6 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Interpretations of f (x)
The density function f (x) gives us an idea about the distribution
of probability density instead of probability itself.
1
For any c, P (X = c) = 0, i.e., the probability that X takes
any specific value is 0.
2
We can only look at the probability that X falls on a specific
interval. This is given by the integration of f (x).
3
For any two numbers a and b with a < b,
P (a ≤ X ≤ b) = P (a < X ≤ b) = P (a ≤ X < b)
Rb
= P (a < X < b) = a f (x)dx.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 7 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Pdf Example - Uniform
Bus comes every 30 minutes, let X = waiting time till a bus
comes. The pdf of X is:
f (x) =
1
, 0 ≤ x ≤ 30.
30
What is the probability that waiting time is longer than 5
minutes? What is the probability that the waiting time is
between 5 and 10 minutes?
In general Given b > a, X with pdf: f (x) =
said to have uniform distribution.
Professor Sharabati (Purdue University)
Spring 2014
1
b−a , a
Continuous Random Variables
≤ x ≤ b is
(Slide 8 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Pdf Example - Exponential
Let X = the life span of some bacteria (in hours). X is a
continuous rv, the pdf is give as:
f (x) = 2e−2x , x ≥ 0
What is the probability that the bacteria lives over 2 hours?
What is the probability that the bacteria dies within an hour?
In general given λ > 0, X with pdf f (x) = λe−λx , x ≥ 0 is an
rv with exponential distribution.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 9 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Cumulative Distribution Function, Expectation, Variance and
Percentile
Cumulative distribution function (cdf) for continuous rv
Definition and interpretation
Relationship between pdf and cdf
Examples
Expectation and variance of continuous rv
Definition
Examples
Percentile
Definition and interpretation
Examples
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 10 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Cumulative Distribution Function (CDF)
Definition
The cumulative distribution function F (x) for a continuous rv
X is defined for every number x by:
Z x
F (x) = P (X ≤ x) =
f (y)dy
−∞
i.e., F (x) is the area under f (x) to the left of x.
We have:
1
0 ≤ F (x) ≤ 1
2
F (x) is non-decreasing.
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Spring 2014
Continuous Random Variables
(Slide 11 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
F (x) and f (x)
From the definition of cdf, we can easily derive:
Rx
P (X ≤ x) = F (x) = −∞ f (y)dy
f (x) = F 0 (x), for which the derivative F 0 (x) exists.
Rb
For a < b, P (a < X < b) = a f (x)dx = F (b) − F (a)
R∞
P (X > a) = a f (x)dx = 1 − F (a)
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Spring 2014
Continuous Random Variables
(Slide 12 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Finding F (x) and use F (x) to Compute Probabilities
Uniform cdf: Find the cdf F (x) for the uniform distribution:
1
10 2 ≤ x ≤ 12
f (x) =
0 otherwise
What is P (x < 6)? What is P (x > 3)?
Hint
In general, for uniform
f (x) =
1
b−a
0
a≤x≤b
otherwise
The cdf is given by:
F (x) =

 0,
x−a
b−a

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Spring 2014
x<a
a≤x<b
x≥b
Continuous Random Variables
(Slide 13 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Finding F (x) and use F (x) to Compute Probabilities
Uniform cdf: Find the cdf F (x) for the uniform distribution:
1
10 2 ≤ x ≤ 12
f (x) =
0 otherwise
What is P (x < 6)? What is P (x > 3)?
Hint
In general, for uniform
f (x) =
1
b−a
0
a≤x≤b
otherwise
The cdf is given by:
F (x) =

 0,
x−a
b−a

Professor Sharabati (Purdue University)
1
Spring 2014
x<a
a≤x<b
x≥b
Continuous Random Variables
(Slide 14 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Example Continued
Exponential cdf: Find the cdf F (x) for the exponential
distribution f (x) = λe−λx , λ > 0, x ≥ 0. What is P (X > a)?
What is P (a < X < b)?
Hint
The general form of an exponential cdf is:
0
x<0
F (x) =
1 − e−λx x ≥ 0
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 15 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Example Continued
Exponential cdf: Find the cdf F (x) for the exponential
distribution f (x) = λe−λx , λ > 0, x ≥ 0. What is P (X > a)?
What is P (a < X < b)?
Hint
The general form of an exponential cdf is:
0
x<0
F (x) =
1 − e−λx x ≥ 0
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 16 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Expectation of Continuous rv
Definition (Expectation)
The expectation or mean value of a continuous rv X with pdf
f (x) is defined as:
Z ∞
E(X) = µX =
x · f (x)dx
−∞
Expectation for continuous rv is an integration instead of a
summation, it is a measure of the center of the distribution.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 17 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Properties of Expectation for Continuous rv
1
2
3
E(aX + b) = aE(x) + b
E(a1 X1 + a2 X2 + ... + an Xn ) =
a1 E(X1 ) + a2 E(X2 ) + ... + an E(Xn )
Expectation of function of X: if h(X) is any function of
X, expectation of h(X) is:
Z ∞
E[h(X)] = µh(X) =
h(x) · f (x)dx
−∞
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 18 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Examples of Expectations
Uniform expectation Find the expectation of the uniform rv
with pdf:
1
b−a a ≤ x ≤ b
f (x) =
0
otherwise
Answer:
E(X) =
a+b
2
Exponential expectation Find the expectation of the
exponential rv with parameter λ.
Answer:
E(X) =
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Spring 2014
1
λ
Continuous Random Variables
(Slide 19 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Examples Continued...
Find E(X 2 ) for uniform distribution with parameters a, b.
Answer:
E(X 2 ) =
a2 + ab + b2
3
Find E(X 2 ) for exponential distribution with parameter λ,i.e.,
f (x) = λe−λx , λ > 0, x ≥ 0.
Answer:
E(X 2 ) =
Professor Sharabati (Purdue University)
Spring 2014
2
λ2
Continuous Random Variables
(Slide 20 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Variance of Continuous rv
Definition (Variance)
The variance of a continuous rv X with pdf f (x) and expectation
E(X) is:
Z ∞
(x − E(X))2 · f (x)dx = E[(X − E(X))2 ]
V ar(x) =
−∞
Standard deviation of X is:
p
V ar(X)
Variance of continuous rv is an integration instead of a summation,
it is a measure of the spreadness of the distribution.
Properties of Variance:
1 V ar(aX + b) = a2 V ar(X)
2 V ar(X) = E(X 2 ) − (E(X))2 = E(X 2 ) − µ2
X
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 21 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Examples of Variances
Variance of uniform: Find the variance of uniform:
1
b−a a ≤ x ≤ b
f (x) =
0
otherwise
Answer:
V ar(X) =
(b − a)2
12
Variance of exponential: Find the variance of exponential
with parameter λ.
Answer:
V ar(X) =
Professor Sharabati (Purdue University)
Spring 2014
1
λ2
Continuous Random Variables
(Slide 22 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Percentiles of a Continuous Distribution
Definition
Let p be a number between 0 and 1. The (100p)th percentile of
the distribution of a continuous rv X, denoted η(p), is defined by:
Z
η(p)
p = F (η(p)) =
f (y)dy
−∞
η(p) is the value on the measurement axis such that 100p% of the
area under the graph of f (x) lies to the left of η(p) and
100(1 − p)% lies to the right. For example, η(0.8), the 80th
percentile, means that 80% of all population are below η(0.8) and
20% are above.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 23 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Median of a Continuous rv: 50th Percentile
Definition
The median of a continuous distribution (denoted µ̃, is the 50th
percentile. That is:
Z µ̃
0.5 = F (µ̃) =
f (y)dy
−∞
i.e., median divides the pdf into two halves with equal area.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 24 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Continuous Random Variables
Cumulative Distribution Functions and Expected Values
Exercise
Find the 50th percentile of the pdf given below:
3
2
2 (1 − x ) 0 ≤ x ≤ 1
f (x) =
0
otherwise
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 25 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Normal Distribution
Normal pdf
Standard Normal, pdf and cdf
Normal table
zα notation
Non-standard Normal
Examples
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 26 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Normal Distributions
Definition
A continuous rv X is said to have a normal distribution with
parameters µ and σ, where −∞ < µ < ∞ and σ > 0, if the pdf of
X is
(x−µ)2
1
f (x; µ, σ) = √ e− 2σ2 , −∞ < x < ∞.
σ 2π
E(X) = µ.
V ar(X) = σ 2 and thus std dev= σ.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 27 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Standard Normal Distribution
Definition
The normal distribution with parameter values µ = 0 and σ = 1 is
called a standard normal distribution. The standard normal rv is
denoted by Z. pdf is:
z2
1
f (z) = √ e− 2 , −∞ < z < ∞
2π
The cdf, denoted by Φ(z) (instead of F (z)) is:
Z z
Φ(z) = P (Z ≤ z) =
f (y)dy
−∞
The standard normal density curve is called z curve. z curve is
bell shaped, symmetric wrt y axis.
Φ(z) gives the area under the normal density curve from −∞
to the number z.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 28 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Standard Normal Distribution
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Continuous Random Variables
(Slide 29 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Standard Normal Table
There is no closed form for Φ(z), so standard normal cdf values
have been tabulated using numeric methods.
Let Z be a standard normal rv, find the following using the
standard normal table:
1
P (Z ≤ 0.85)
P (Z ≤ 0.85) = Φ(0.85)
2
P (Z > 1.32)
P (Z > 1.32) = 1 − P (Z < 1.32) = 1 − Φ(1.32)
3
P (−2.1 < Z < 1.78)
P (−2.1 < Z < 1.78) = P (Z < 1.78) − P (Z < −2.1) =
Φ(1.78) − Φ(−2.1)
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 30 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Another Example
Let Z be a standard normal rv, find z when:
1
P (Z < z) = 0.9278
P (Z < z) = Φ(z) = 0.9278, look for 0.9278 in table, and find
z accordingly.
2
P (|Z| < z) = 0.8132
P (−z < Z < z) = P (−z < Z < 0) + P (0 < Z < z)
= 2P (0 < Z < z)
= 2(Φ(z) − Φ(0)) = 2(Φ(z) − 12 )
= 2Φ(z) − 1 = 0.8132, thus Φ(z) = 0.9066, look for 0.9066,
and find z accordingly. in the table
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 31 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
zα N otation
Later when we discuss inferential statistics, we will need values on
the measurement axis that capture small tail areas under the
normal curve, this is denoted zα :
zα denote the value on the measurement axis for which α of the
area under the z curve lies to the right of zα .
1 − α is the area lies to the left of zα under the z curve. i.e.,
zα is the 100(1 − α)th percentile of the standard normal
dist.
z curve is symmetric wrt y axis, so area to the left of −zα is
also α.
z is usually referred to as z critical values.
Example
What is z0.05 ? It is the ?-th percentile?
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 32 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Nonstandard Normal Distributions
Proposition
If X ∼ N (µ, σ 2 ), then
X −µ
σ
has a standard normal distribution, thus
b−µ
b−µ
a−µ
a−µ
≤Z≤
P (a ≤ X ≤ b) = P
=Φ
−Φ
σ
σ
σ
σ
a−µ
b−µ
P (X ≤ a) = Φ
, P (X ≥ b) = 1 − Φ
σ
σ
Z=
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 33 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Empirical Rule
Nonstandard normal curve:
1 Roughly 68% of the values are within σ of the mean.
2 Roughly 95% of the values are within 2σ of the mean.
3 Roughly 99.7% of the values are within 3σ of the mean.
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 34 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Exercise of Nonstandard Normal
Reaction time for an in-traffic response to a brake signal from
standard brake lights can be modelled with a normal with mean
1.25 sec and std dev 0.46 sec. What is the probability that
reaction time is between 1.00 and 1.75 sec?
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 35 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Percentiles of an Arbitrary Normal
The (100p)th percentile of a normal distribution with mean µ and
standard deviation σ can be easily transformed from the percentile
of a standard normal.
(100p)th percentile for N (µ, σ 2 ) = µ + (100p)th percentile for N (0, 1) · σ
Example
What is the 95th percentile of N (µ = 2, σ = 4.5)?
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 36 of 37)
Continuous Random Variables & Probability Distributions
The Normal Distribution
Normal Approximation to Binomial
Let X be a binomial rv based on n trials, each with probability of
success p. Check the binomial pmf (histogram) is not too skewed,
X has
papproximately a normal distribution, with µ = np and
σ = np(1 − p).
!
x + 0.5 − np
P (X ≤ x) = Φ p
np(1 − p)
In practice, the approximation is adequate provided that both
np ≥ 10 and n(1 − p) ≥ 10
Professor Sharabati (Purdue University)
Spring 2014
Continuous Random Variables
(Slide 37 of 37)