Discrete Random Variables

Instructor Katherine Cliff

Warm up question:

What is the expected value of a discrete random variable, and how do you calculate it?







Definition 1: Variance of a Discrete Random Variables













Example 1

For a raffle, a school sells 1,000 tickets for $2 each. There will be one prize for $1000. There will be three $100 prizes. There will be five $20 prizes. Compute the expected value and standard deviation for the value of a raffle ticket.

You work on numbers 2-4

2.

The probability distribution for the number of students in Statistics classes offered at a small college is given, but one value is missing. Fill in the missing value, then answer the questions that follow. Give your answer rounded to 4 decimal places when necessary.

X P(X)
23 0.18
24 0.22
25 0.25
26
27 0.2

mean


2.

The probability distribution for the number of students in Statistics classes offered at a small college is given, but one value is missing. Fill in the missing value, then answer the questions that follow. Give your answer rounded to 4 decimal places when necessary.

X P(X)
23 0.18
24 0.22
25 0.25
26
27 0.2

standard deviation


4.

X is a discrete random variable with probability mass function

\[P(X) = cx^2 \textrm{ for } x = \frac13, \frac23, 1, \frac43\]

A. Find the value of \(c\)

4.

X is a discrete random variable with probability mass function

\[P(X) = cx^2 \textrm{ for } x = \frac13, \frac23, 1, \frac43\]

B. Find the expected value of \(X\).

5.

Suppose that the probabilities are 0.2, 0.1, 0.5, and 0.2, respectively, that 0, 1, 2, or 3 snow storms will strike Colorado Springs in any given year. Find the mean and variance of the random variable X representing the number of snow storms in Colorado Springs in a given year.

Example 2

Let X be a discrete random variable with the following distribution:

\(x_i\) 0 1 2 3
\(P(X=x_i)\) 0.1 0.4 0.3 0.2

Find \(E(X), E(2X-1), \text{Var}(2X-1)\), and \(E(X-2)^2\).

Example 2

Let X be a discrete random variable with the following distribution:

\(x_i\) 0 1 2 3
\(P(X=x_i)\) 0.1 0.4 0.3 0.2

Find \(E(X), E(2X-1), \text{Var}(2X-1)\), and \(E(X-2)^2\).

Example 2

Let X be a discrete random variable with the following distribution:

\(x_i\) 0 1 2 3
\(P(X=x_i)\) 0.1 0.4 0.3 0.2

Find \(E(X), E(2X-1), \text{Var}(2X-1)\), and \(E(X-2)^2\).

6.

Let X be a random variable with an expected value of E(X) = 28 and a variance Var(X) = 2. Find the expected value and variance of the following linear combinations:

A. E(X+4)

B. E(3X)

C. E(8X+2)

6.

Let X be a random variable with an expected value of E(X) = 28 and a variance Var(X) = 2. Find the expected value and variance of the following linear combinations:

D. Var(X+4)

E. Var(3X)

F. Var(8X+2)

Definition 2: Cumulative density function











Example 3

The cumulative distribution of a random variable X is given. Use it to find each of the following probabilities.

\(x_i\) 1 2 3 4
\(P(X \leq x_i)\) 0.3 0.5 0.8 1

\[P(X \leq 2) = \]




\[P(X \geq 2) = \]




Example 3

The cumulative distribution of a random variable X is given. Use it to find each of the following probabilities.

\(x_i\) 1 2 3 4
\(P(X \leq x_i)\) 0.3 0.5 0.8 1

\[P(X < 2) = \]




\[P(X > 2) = \]




7.

Use the cumulative probability distribution table to find the specified probabilities.

\(x_i\) \(P(X \leq x_i)\)
0 0.12
1 0.27
2 0.43
3 0.83
4 1

\[P(X < 2)\]



\[P(X \leq 2)\]

7.

Use the cumulative probability distribution table to find the specified probabilities.

\(x_i\) \(P(X \leq x_i)\)
0 0.12
1 0.27
2 0.43
3 0.83
4 1

\[P(X>1)\]



\[P(X \geq 1)\]

7.

Use the cumulative probability distribution table to find the specified probabilities.

\(x_i\) \(P(X \leq x_i)\)
0 0.12
1 0.27
2 0.43
3 0.83
4 1

\[P(1 \leq X < 3)\]

8.

Let W be a random variable giving the number of heads that come up in four tosses of a coin.

A. Find the probability distribution of the random variable W. (Hint: Write out the sample space).

8.

Let W be a random variable giving the number of heads that come up in four tosses of a coin.

B. Find the expected value of the distribution.

C. What is the meaning of the expected value in this context?

8.

Let W be a random variable giving the number of heads that come up in four tosses of a coin.

A. Find the variance of the distribution.