1.9.18. Find the mean and the variance of the distribution that has the cdf x <0 0

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
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**Problem 1.9.18:** 

Find the mean and the variance of the distribution that has the cumulative distribution function (cdf):

\[
F(x) = 
\begin{cases} 
0 & \text{for } x < 0 \\
\frac{x}{8} & \text{for } 0 \leq x < 2 \\
\frac{x}{2} - \frac{3}{16} & \text{for } 2 \leq x < 4 \\
1 & \text{for } 4 \leq x.
\end{cases}
\]

This piecewise function represents the cumulative distribution function (cdf) of a probability distribution. The cdf is defined in segments over different intervals of x:

- For \(x < 0\), the cdf is 0, indicating no probability mass below 0.
- For \(0 \leq x < 2\), the cdf is \(\frac{x}{8}\).
- For \(2 \leq x < 4\), the cdf is \(\frac{x}{2} - \frac{3}{16}\).
- For \(4 \leq x\), the cdf is 1, indicating that the total probability mass is accumulated by 4.

The problem asks for the mean and variance of this distribution. These are key statistical measures for understanding the central tendency and spread of the probability distribution.
Transcribed Image Text:**Problem 1.9.18:** Find the mean and the variance of the distribution that has the cumulative distribution function (cdf): \[ F(x) = \begin{cases} 0 & \text{for } x < 0 \\ \frac{x}{8} & \text{for } 0 \leq x < 2 \\ \frac{x}{2} - \frac{3}{16} & \text{for } 2 \leq x < 4 \\ 1 & \text{for } 4 \leq x. \end{cases} \] This piecewise function represents the cumulative distribution function (cdf) of a probability distribution. The cdf is defined in segments over different intervals of x: - For \(x < 0\), the cdf is 0, indicating no probability mass below 0. - For \(0 \leq x < 2\), the cdf is \(\frac{x}{8}\). - For \(2 \leq x < 4\), the cdf is \(\frac{x}{2} - \frac{3}{16}\). - For \(4 \leq x\), the cdf is 1, indicating that the total probability mass is accumulated by 4. The problem asks for the mean and variance of this distribution. These are key statistical measures for understanding the central tendency and spread of the probability distribution.
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