ST361: Ch5.4 + Ch1.3Random Variable and Its Probability Distribution
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Topics: Random Variable (§5.4)
Probability Distribution of a discrete random variable (§5.4, §1.3)
Mean and Variance of a discrete random variable (§5.4)
Probability Distribution of a continuous variable (§5.4, §1.3)
Mean and Variance of a continuous random variable (§5.4)
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Random Variable (r.v.)
- A random variableis a variable whose value is a ______of an experiment.
- We can think that an r.v. is any rule that associates a ______with each ______in an experiment.
Ex. Consider an experiment of tossing 2 coins. One way to define a r.v. is
- For numerical variables, most of the time the values themselves can be used as a r.v.
Ex. Define a r.v. to for the exam score of a student as
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Discrete r.v.
The possible values of the r.v. are isolated points along the number line.
Ex.x = # of Heads of tossing 2 coinsx, =0,1,2
Ex.x = # of telephone lines in a company that is in use, x=0,1,2, 3,……
(c.f. Continuous r.v.: The possible values forms an interval along the real line)
ProbabilityDistribution of a Discrete r.v.
- The probability distribution of a r.v., denoted as ______,describes
(a)______and
(b)______
Ex.X = result of tossing a fair dice. The probability distribution of x is
2. In general, the probability that x gets a value c, P(x=c),is defined as the sum of all corresponding outcomes in S (i.e., the sample space) that are assigned to the valuex.
Ex.X = # of heads in tossing two fair coins. Then the probability distribution of X is
3. There are 3 ways to display a probability distribution for a discrete r.v.:
Ex. Toss a (unfair) coin 3 times, and let x= # of heads. Then the probability distribution of x is given as below:
(1)Density plot
(2) Table
x / 0 / 1 / 2 / 3P(x) / 0.1 / 0.4 / 0.3 / 0.2
(3) Formula
From the probability distribution, we can calculate
P(x = 3 ) =
P(x2 ) =
P( x2 ) =
P(x0 ) =
- For any probability distribution P(x), (recall the axiom of probabilities…)
(1)
(2)
Ex.(1) Find the value of c so that the following function is a probability distribution of a r.v. x:
(2) For this probability distribution, find P(x2)
FYI: Mean and Varianceof a discrete r.v.with probability distribution
The mean
(The mean of a r.v. is also called as expected value.)
The variances
The standard deviation =
Ex. A contractor is required by a county planning department to submit from 1 to 5 different forms, depending on the nature of the project. Let x = # of forms required of the next contractor, and for x=1,2,3,4,5.
(a)What is the value of k?
(b)What is the probability that at most 3 forms are required?
(c)What is the expected number (i.e., mean) of forms required?
(d)What is the SD of the number of forms required? (This calculation won’t be included in the exams)
Continuous Random Variable (r.v.)
A r.v.is continuous if its possible values forms an interval along the real line
Ex.x = exam score,
Ex.x = your height,
Probability Distribution of a continuous r.v.
- Every continuous r.v. has a ______, denoted as ______such that for any 2 numbersa and b (a<b),
P( a x b ) = ______under the density curve of f(x) between a and b
Ex. P( -1 X 1 ) =
Ex. P( X 1 ) =
Comment: For continuous r.v. x,
(1) Probability is the area encompassed by the density curve, the two vertical bars and the x-axis. ______
______
(2) Because area under the curve represents probabilities, the total area under the density curve should be equal to ______
(3) Unlike the discrete r.v., the Y-axis is not probability
( The height is determined so that ______
______
Ex.
(4) P( X = a ) = ______(Think what is the size of the corresponding area?)
(5) For a continuous r.v.,
(6) Ways to presenting the density function of a continuous r.v.:by a density plot or formula
(see next page)
Ex. Consider a r.v. X= test score. The probability distribution of X is given below.
1)Density plot
2)Density function
P( X=70) =
P( 60 < X < 80 ) =
P(X > 70 ) =
P( X 70 ) =
Ex.
Summary: A density function of a r.v. has to satisfied the following properties:
(a)
THINK: do we need ?
(b) The total area under the curve is 1, i.e.,
Why?
FYI: Mean and Variance of a continuous r.v. with density function
The mean
(The mean of a r.v. is also called as expected value.)
The variances
The standard deviation
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