Lesson 3: Discrete Random Variable (Mean and Variance)
Discrete Random Variable
Definition: A discrete random variable is one that can take on a countable number of distinct values.
Mean and Variance
Mean (Expected Value): Represents the average outcome of a probability distribution.
Variance: Measures the spread of the random variable's values around the mean.
Steps in Computing the Mean of the Probability Distribution
Construct the Probability Distribution
Identify the random variable X and list corresponding probabilities P(X).
Multiply Values by Probabilities
For each value of X, calculate the product of X and its corresponding P(X).
Add the Results
Sum all the products obtained to get the mean.
Example No. 1: Number of Spots from Rolling a Die
Outcome Analysis:
Number of Spots X: 1, 2, 3, 4, 5, 6
Probability P(X): 1/6 for each value (since a die has 6 faces)
Calculations:
X.P(X) for each X:
1 *(1/6) = 1/6
2 *(1/6) = 2/6
3 *(1/6) = 3/6
4 *(1/6) = 4/6
5 *(1/6) = 5/6
6 *(1/6) = 6/6
Sum: ΣX.P(X) = (1/6 + 2/6 + 3/6 + 4/6 + 5/6 + 6/6) = 3.5
Mean Calculation Formula for Probability Distribution
Formula: ( E(X) = \sum (X_i \cdot P(X_i)) )
Where ( X_i ) are the values of X and ( P(X_i) ) are the corresponding probabilities.
Steps in Computing the Variance of the Probability Distribution
Calculate the Mean: Use the mean formula from above.
Substitute in the Variance Formula:
Formula: ( \sigma^2 = \sum (X_i - \mu)^2 \cdot P(X_i) )
Standard Deviation:
Calculate the square root of the variance: ( \sigma = \sqrt{\sigma^2} )
Example No. 2: Number of Cars Sold
Data on Cars Sold per Day:
Number of Cars Sold: 0, 1, 2, 3, 4
Corresponding Probability P(X): ( \frac{1}{10}, \frac{2}{10}, \frac{3}{10}, \frac{2}{10}, \frac{2}{10} )
Task: Compute variance and standard deviation using provided steps.
Exercise: Admissions Inquiries
Data Provided:
Number of Inquiries: 22, 23, 24, 25, 26, 27
Corresponding Probability P(X): 0.08, 0.19, 0.36, 0.25, 0.07, 0.05
Task: Find the variance and standard deviation.