Respuesta :
Answer:
[tex]E(x) = 4.500[/tex] --- Expected value
[tex]SD(x) = 2.872[/tex] --- Standard deviation
Step-by-step explanation:
Given
[tex]\begin{array}{ccccccccccc}x & {0} & {1} & {2} & {3} & {4}& {5} & {6} & {7} & {8} & {9} \ \\ P(X=x) & {\frac{1}{10}} & {\frac{1}{10}} & {\frac{1}{10}} & {\frac{1}{10}} & {\frac{1}{10}}& {\frac{1}{10}} & {\frac{1}{10}} & {\frac{1}{10}} & {\frac{1}{10}} & {\frac{1}{10}} \ \end{array}[/tex]
Solving (a): Expected value
This is calculated using:
[tex]E(x) = \sum\limits^{9}_{i=0} x_i * P(X = x_i)[/tex]
Since they all have the same probability, the formula becomes:
[tex]E(x) = \frac{1}{10}\sum\limits^{9}_{i=0} x_i[/tex]
[tex]E(x) = \frac{1}{10}(0+1+2+3+4+5+6+7+8+9)[/tex]
[tex]E(x) = \frac{1}{10}*45[/tex]
[tex]E(x) = \frac{45}{10}[/tex]
[tex]E(x) = 4.500[/tex]
Solving (b): Standard Deviation
First, we calculate the variance using
[tex]Var(x) = E(x^2) - (E(x))^2[/tex]
In (a), we have:
[tex]E(x) = 4.500[/tex]
[tex]E(x^2)[/tex] is calculated as:
[tex]E(x^2) = \sum\limits^{9}_{i=0} x_i^2 * P(X = x_i)[/tex]
Since they all have the same probability, the formula becomes:
[tex]E(x^2) = \frac{1}{10}\sum\limits^{9}_{i=0} x_i^2[/tex]
So, we have:
[tex]E(x^2) = \frac{1}{10}(0^2+1^2+2^2+3^2+4^2+5^2+6^2+7^2+8^2+9^2)[/tex]
Using a calculator
[tex]E(x^2) = \frac{1}{10}(285)[/tex]
[tex]E(x^2) = 28.5[/tex]
So:
[tex]Var(x) = E(x^2) - (E(x))^2[/tex]
[tex]Var(x) = 28.5 - 4.5^2[/tex]
[tex]Var(x) = 28.5 - 20.25[/tex]
[tex]Var(x) = 8.25[/tex]
The standard deviation is then calculated as:
[tex]SD(x) = \sqrt{Var(x)}[/tex]
[tex]SD(x) = \sqrt{8.25}[/tex]
[tex]SD(x) = 2.872[/tex] ---- approximated