Let Y denote a random variable with the following cumulative distribution function. if y < 0 Ina In (x + 1)²) if 0 < y < 1, if y > 1. F(y) = a. Find the PDF, ƒ(y). b. Evaluate P(Y = 0.5). c. Evaluate P(Y < 0.8|Y > 0.4). d. Evaluate P(Y < 1).

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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(x+1) is a TYPO, supposed to be (y+1)

**Cumulative Distribution Function (CDF) of a Random Variable Y**

Let \( Y \) denote a random variable with the following cumulative distribution function:
\[
F(y) = 
\begin{cases}
0 & \text{if } y < 0 \\
\frac{1}{\ln (4)} \ln((x + 1)^2) & \text{if } 0 \leq y \leq 1, \\
1 & \text{if } y > 1.
\end{cases}
\]

**Tasks:**

a. **Find the Probability Density Function (PDF), \( f(y) \):**

   The PDF \( f(y) \) can be found by differentiating the CDF \( F(y) \) with respect to \( y \).

b. **Evaluate \( P(Y = 0.5) \):**

   Use the PDF \( f(y) \) to evaluate the probability at \( Y = 0.5 \).

c. **Evaluate \( P(Y < 0.8 \mid Y > 0.4) \):**

   This requires finding the conditional probability, which involves the CDF \( F(y) \) in the specified range.

d. **Evaluate \( P(Y < 1) \):**

   Use the CDF \( F(y) \) to find the probability that \( Y \) is less than 1.

Note: Detailed steps for each task involve applying the fundamental principles of probability and statistics, such as differentiating the CDF to find the PDF, using properties of probability, and appropriate integration where necessary.
Transcribed Image Text:**Cumulative Distribution Function (CDF) of a Random Variable Y** Let \( Y \) denote a random variable with the following cumulative distribution function: \[ F(y) = \begin{cases} 0 & \text{if } y < 0 \\ \frac{1}{\ln (4)} \ln((x + 1)^2) & \text{if } 0 \leq y \leq 1, \\ 1 & \text{if } y > 1. \end{cases} \] **Tasks:** a. **Find the Probability Density Function (PDF), \( f(y) \):** The PDF \( f(y) \) can be found by differentiating the CDF \( F(y) \) with respect to \( y \). b. **Evaluate \( P(Y = 0.5) \):** Use the PDF \( f(y) \) to evaluate the probability at \( Y = 0.5 \). c. **Evaluate \( P(Y < 0.8 \mid Y > 0.4) \):** This requires finding the conditional probability, which involves the CDF \( F(y) \) in the specified range. d. **Evaluate \( P(Y < 1) \):** Use the CDF \( F(y) \) to find the probability that \( Y \) is less than 1. Note: Detailed steps for each task involve applying the fundamental principles of probability and statistics, such as differentiating the CDF to find the PDF, using properties of probability, and appropriate integration where necessary.
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