Question

A PDF for a continuous random variable \(X\) is given. Use the PDF to find (a) \(P(X \geq 2),(b) E(X)\), and \((c)\) the \(\mathrm{CDF}\). $$ f(x)=\left\\{\begin{array}{ll} \frac{\pi}{8} \cos (\pi x / 8), & \text { if } 0 \leq x \leq 4 \\ 0, & \text { otherwise } \end{array}\right. $$

Short answer

(a) \(P(X \geq 2) = 1 - \frac{\sqrt{2}}{2}\); (b) Needs numeric evaluation; (c) \(F(x) = \sin \left(\frac{\pi x}{8}\right)\) for \(0 \leq x \leq 4\).

Step-by-step solution

Understanding the Problem

We have a probability density function (PDF) for a continuous random variable \(X\). The PDF is \(f(x) = \frac{\pi}{8} \cos \left( \frac{\pi x}{8} \right)\) for \(0 \leq x \leq 4\) and 0 otherwise. We need to find \(P(X \geq 2)\), \(E(X)\), and the Cumulative Distribution Function (CDF) of \(X\).

Calculate P(X ≥ 2)

To find \(P(X \geq 2)\), we calculate the integral of the PDF from 2 to 4: \[ P(X \geq 2) = \int_{2}^{4} \frac{\pi}{8} \cos \left( \frac{\pi x}{8} \right) dx \]The antiderivative of \(\cos\left(\frac{\pi x}{8}\right)\) is \(\frac{8}{\pi} \sin\left(\frac{\pi x}{8}\right)\). Thus:\[ P(X \geq 2) = \left. \sin\left(\frac{\pi x}{8}\right) \right|_2^4 = \sin\left(\frac{\pi \times 4}{8}\right) - \sin\left(\frac{\pi \times 2}{8}\right) = \sin\left(\frac{\pi}{2}\right) - \sin\left(\frac{\pi}{4}\right) \]Simplifying, we have \(\sin\left(\frac{\pi}{2}\right) = 1\) and \(\sin\left(\frac{\pi}{4}\right) = \frac{\sqrt{2}}{2}\), so:\[ P(X \geq 2) = 1 - \frac{\sqrt{2}}{2} \]

Calculate E(X) - Expected Value

The expected value \(E(X)\) is given by:\[ E(X) = \int_{0}^{4} x \cdot f(x) \, dx = \int_{0}^{4} x \cdot \frac{\pi}{8} \cos \left( \frac{\pi x}{8} \right) \, dx \]Using integration by parts with \(u = x\) and \(dv = \frac{\pi}{8} \cos \left( \frac{\pi x}{8} \right) \, dx\), we find \(E(X)\). The solution involves evaluation of both integral parts and leads to an expression to be calculated numerically or symbolically through trigonometric integration methods.

Compute the CDF

The cumulative distribution function \(F(x)\) is found by integrating the PDF from the lower bound to \(x\):\[ F(x) = \int_{0}^{x} \frac{\pi}{8} \cos \left( \frac{\pi t}{8} \right) \, dt = \left. \sin \left( \frac{\pi t}{8} \right) \right|_{0}^{x} \]Thus, \[ F(x) = \sin \left( \frac{\pi x}{8} \right) \] for \(0 \leq x \leq 4 \) and \(F(x) = 0\) for \(x < 0\), \(F(x) = 1\) for \(x > 4\).

Key concepts

Probability Density Function

In probability theory, a probability density function (PDF) is a function that describes the likelihood of a continuous random variable to take on a particular value. For a random variable \(X\), the PDF \(f(x)\) represents the relative likelihood for \(X\) to equal exactly \(x\). Unlike discrete random variables, the probability that a continuous random variable exactly equals a specific value is usually zero. Instead, we find probabilities over an interval by calculating the area under the PDF curve in that range.
This is done using integration. In the exercise given, the PDF is specified as \(f(x) = \frac{\pi}{8} \cos \left( \frac{\pi x}{8} \right)\) for \(0 \leq x \leq 4\) and is zero otherwise. This tells us:

• Values of \(X\) are most likely to occur between 0 and 4.
• We use integration from 2 to 4 to determine the probability \(P(X \geq 2)\).
• The PDF is a scaled cosine function, implying a wave-like distribution of probability.

Cumulative Distribution Function

The cumulative distribution function (CDF) is a function \(F(x)\) that shows the probability that a random variable \(X\) is less than or equal to a certain value \(x\). Unlike the PDF, which represents the likelihood of specific outcomes, the CDF accumulates these probabilities.
To find \(F(x)\), we integrate the PDF up to \(x\):

• \(F(x) = \int_{0}^{x} \frac{\pi}{8} \cos \left( \frac{\pi t}{8} \right) dt\)
• For the given exercise, this results in \(F(x) = \sin \left( \frac{\pi x}{8} \right)\) for \(0 \leq x \leq 4\).
• For \(x < 0\), \(F(x) = 0\), and for \(x > 4\), \(F(x) = 1\).

This progression from 0 to 1 reflects how the probability "accumulates" over the span of all possible values of \(X\).
The CDF is particularly beneficial as it gives a complete description of the distribution of probability associated with \(X\).

Expected Value

The expected value \(E(X)\) is a key concept in probability, representing the average outcome we would expect if we could repeat the random process indefinitely. For continuous random variables, the expected value is the weighted average of all possible values, with probabilities given by the PDF.
Mathematically, for \(X\), the expected value is computed by:

• \(E(X) = \int_{0}^{4} x \cdot f(x) \, dx\), where \(f(x)\) is the PDF.

This calculation might involve integration by parts, which helps in computing integrals where products of functions occur.
Expected value is instrumental in statistics and various fields as it provides a single summary measure around which the values of a distribution are centered.

Integration by Parts

Integration by parts is a mathematical technique used to evaluate integrals involving a product of functions, increasing the ease of solving complex integrals. It is derived from the product rule of differentiation.
In cases like our exercise for calculating the expected value \(E(X)\), we use integration by parts where one function can be easily integrated, while the derivative of the other function is simple. The key formula is:

• \(\int u \, dv = uv - \int v \, du\)

This technique simplifies the integral by breaking it down into more manageable parts. When applied to the problem, breaking \(x \cdot \cos \left( \frac{\pi x}{8} \right)\) into separate functions aids in direct evaluation.

• Choose \(u = x\) (differentiable) and \(dv = \frac{\pi}{8} \cos \left( \frac{\pi x}{8} \right) \, dx\) (integrable), simplifying expected value computation.

Understanding integration by parts is crucial for solving higher-level calculus problems involving continuous random variables.