Math Problem Statement

Determine f(0) Determine f(1) write the whole question again and then solve it

If the random variable X has CDF F(x) = e^x / (1 + e^x) for −∞ < x < ∞, determine the PDF f(x), and specifically find f(0) and f(1).

Solution

Problem Restatement:

Given that the cumulative distribution function (CDF) of a random variable XX is:

F(x)=ex1+exfor<x<,F(x) = \frac{e^x}{1 + e^x} \quad \text{for} \quad -\infty < x < \infty,

determine the probability density function (PDF) f(x)f(x), and then specifically find:

  1. f(0)f(0)
  2. f(1)f(1)

Solution:

To find the PDF f(x)f(x), we use the fact that the PDF is the derivative of the CDF. That is:

f(x)=ddxF(x)f(x) = \frac{d}{dx} F(x)

The given CDF is:

F(x)=ex1+exF(x) = \frac{e^x}{1 + e^x}

We need to differentiate this with respect to xx. Applying the quotient rule:

f(x)=ddx(ex1+ex)f(x) = \frac{d}{dx} \left( \frac{e^x}{1 + e^x} \right)

The quotient rule is given by:

ddx(uv)=vdudxudvdxv2\frac{d}{dx} \left( \frac{u}{v} \right) = \frac{v \frac{du}{dx} - u \frac{dv}{dx}}{v^2}

For this case:

  • u=exu = e^x and v=1+exv = 1 + e^x
  • dudx=ex\frac{du}{dx} = e^x
  • dvdx=ex\frac{dv}{dx} = e^x

Using the quotient rule:

f(x)=(1+ex)(ex)ex(ex)(1+ex)2f(x) = \frac{(1 + e^x)(e^x) - e^x(e^x)}{(1 + e^x)^2}

Simplifying the numerator:

f(x)=ex+e2xe2x(1+ex)2f(x) = \frac{e^x + e^{2x} - e^{2x}}{(1 + e^x)^2} f(x)=ex(1+ex)2f(x) = \frac{e^x}{(1 + e^x)^2}

Thus, the PDF is:

f(x)=ex(1+ex)2f(x) = \frac{e^x}{(1 + e^x)^2}

Part 1: Find f(0)f(0)

Substitute x=0x = 0 into the PDF:

f(0)=e0(1+e0)2=1(1+1)2=14f(0) = \frac{e^0}{(1 + e^0)^2} = \frac{1}{(1 + 1)^2} = \frac{1}{4}

So, f(0)=14f(0) = \frac{1}{4}.

Part 2: Find f(1)f(1)

Substitute x=1x = 1 into the PDF:

f(1)=e1(1+e1)2=e(1+e)2f(1) = \frac{e^1}{(1 + e^1)^2} = \frac{e}{(1 + e)^2}

So, f(1)=e(1+e)2f(1) = \frac{e}{(1 + e)^2}.


Let me know if you need further clarification or detailed steps! Here are some related questions to explore:

  1. How do we interpret the PDF in terms of probabilities?
  2. What is the CDF's behavior as xx \to -\infty and xx \to \infty?
  3. How would you find the mode of the distribution given this PDF?
  4. Can the PDF be normalized, and why is this important?
  5. What would the PDF look like if we transformed XX using a function, e.g., Y=2X+1Y = 2X + 1?

Tip: Understanding the relationship between the CDF and PDF is crucial. The PDF describes the rate of change of the CDF, which gives insights into how probabilities are distributed.

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Math Problem Analysis

Mathematical Concepts

Calculus
Probability Theory
Cumulative Distribution Functions
Probability Density Functions

Formulas

f(x) = dF(x)/dx
Quotient Rule: d(u/v)/dx = (v * du/dx - u * dv/dx) / v^2

Theorems

Fundamental theorem of calculus
Relationship between CDF and PDF

Suitable Grade Level

College Level (Advanced Probability/Statistics)