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CIE AS/A Level Maths-6.3 Continuous random variables- Study Notes- New Syllabus - 2026-2027

CIE AS/A Level Maths-6.3 Continuous random variables- Study Notes- New Syllabus

Ace AS/A Level Maths Exam with CIE AS/A Level Maths-6.3 Continuous random variables- Study Notes

Key Concepts:

  • Continuous Random Variables and Probability Density Functions (PDFs)
  • Using a Probability Density Function (PDF) to Solve Problems

AS & A Level Maths Study Notes– All Topics

Continuous Random Variables and Probability Density Functions (PDFs)

Continuous Random Variables and Probability Density Functions (PDFs)

Continuous random variable:

  • A random variable \(X\) that can take any value within a given interval (finite or infinite) is called continuous. 
  • Unlike discrete variables, the probability of \(X\) taking any exact value is zero: \(P(X = x) = 0\).

Probability density function (PDF):

  • A function \(f(x)\) such that the probability that \(X\) lies in an interval \([a, b]\) is given by:

\( P(a \le X \le b) =\displaystyle \int_a^b f(x) \, dx \)

Properties of a PDF \(f(x)\):

  • \(f(x) \ge 0\) for all \(x\)

  • The total area under the curve is 1: \( \displaystyle\int_{-\infty}^{\infty} f(x) \, dx = 1 \)
  • Probabilities are obtained by integrating over intervals, not by evaluating \(f(x)\) at a point.

Expectation (mean) of \(X\):

\( \mathrm{E}(X) = \displaystyle\int_{-\infty}^{\infty} x f(x) \, dx \)

Variance of \(X\):

\( \mathrm{Var}(X) = \displaystyle\int_{-\infty}^{\infty} (x – \mathrm{E}(X))^2 f(x) \, dx \)

Example:

Let \(X\) be uniformly distributed on \([0, 5]\), so \(f(x) = \dfrac{1}{5}\) for \(0 \le x \le 5\). Find \(P(1 \le X \le 3)\), \(\mathrm{E}(X)\), and \(\mathrm{Var}(X)\).

▶️ Answer/Explanation

Probability: \( P(1 \le X \le 3) = \int_1^3 \dfrac{1}{5} \, dx = \dfrac{3-1}{5} = \dfrac{2}{5} \)

Expectation: \( \mathrm{E}(X) = \int_0^5 x \cdot \dfrac{1}{5} \, dx = \dfrac{1}{5} \cdot \dfrac{5^2}{2} = 2.5 \)

Variance: \( \mathrm{Var}(X) = \int_0^5 (x – 2.5)^2 \cdot \dfrac{1}{5} \, dx = \dfrac{25}{12} \approx 2.0833 \)

Final Answer: \(\boxed{P = \frac{2}{5}, \; \mathrm{E}(X) = 2.5, \; \mathrm{Var}(X) \approx 2.083}\)

Example :

Let \(X\) have PDF \( f(x) = \frac{1}{2} e^{-x/2} \), \(x \ge 0\). Find \(P(X > 3)\), \(\mathrm{E}(X)\), and \(\mathrm{Var}(X)\).

▶️ Answer/Explanation

Probability: \( P(X > 3) = \int_3^{\infty} \frac{1}{2} e^{-x/2} \, dx = e^{-3/2} \approx 0.2231 \)

Expectation: \( \mathrm{E}(X) = \int_0^{\infty} x \cdot \frac{1}{2} e^{-x/2} \, dx = 2 \)

Variance: \( \mathrm{Var}(X) = \int_0^{\infty} (x-2)^2 \cdot \frac{1}{2} e^{-x/2} \, dx = 4 \)

Final Answer: \(\boxed{P \approx 0.2231, \; \mathrm{E}(X) = 2, \; \mathrm{Var}(X) = 4}\)

Example:

Continuous Random Variable with PDF \( f(x) = \dfrac{3}{x^4}, x \ge 1 \) Verify and find all the parameters 

▶️Answer/Explanation

Step 1: Verify PDF

Check that \( \int_1^\infty f(x) \, dx = 1 \)

\( \int_1^\infty \dfrac{3}{x^4} \, dx = 3 \int_1^\infty x^{-4} dx = 3 \left[ \dfrac{x^{-3}}{-3} \right]_1^\infty = 1 \)

Step 2: Find probability \(P(X > 2)\)

\( P(X > 2) = \int_2^\infty f(x) \, dx = \int_2^\infty \dfrac{3}{x^4} dx = 3 \left[ \dfrac{x^{-3}}{-3} \right]_2^\infty = 2^{-3} = \dfrac{1}{8} \)

Step 3: Find expectation \( \mathrm{E}(X) \)

\( \mathrm{E}(X) = \int_1^\infty x f(x) \, dx = \int_1^\infty x \cdot \dfrac{3}{x^4} dx = 3 \int_1^\infty x^{-3} dx = \dfrac{3}{2} \)

Step 4: Find variance \( \mathrm{Var}(X) \)

\( \mathrm{E}(X^2) = \int_1^\infty x^2 f(x) \, dx = \int_1^\infty 3 x^{-2} dx = 3 \)

\( \mathrm{Var}(X) = \mathrm{E}(X^2) – (\mathrm{E}(X))^2 = 3 – \left(\dfrac{3}{2}\right)^2 = \dfrac{3}{4} \)

Example:

Let \(X \sim \text{Uniform}(0, 1)\) with PDF \(f(x) = 1\) for \(0 \le x \le 1\). Let \(Y = 3X + 2\). Find \(\mathrm{E}(Y)\) and \(\mathrm{Var}(Y)\).

▶️ Answer/Explanation

\(\mathrm{E}(Y) = \mathrm{E}(3X + 2) = 3 \mathrm{E}(X) + 2 = 3 \cdot 0.5 + 2 = 3.5\)

\(\mathrm{Var}(Y) = \mathrm{Var}(3X + 2) = 3^2 \mathrm{Var}(X) = 9 \cdot \dfrac{1}{12} = 0.75\)

Final Answer: \(\boxed{\mathrm{E}(Y) = 3.5, \; \mathrm{Var}(Y) = 0.75}\)

Using a Probability Density Function (PDF) to Solve Problems

Using a Probability Density Function (PDF) to Solve Problems

A continuous random variable \(X\) has a probability density function \(f(x)\) if the probability that \(X\) lies in an interval \([a, b]\) is given by:

\( P(a \le X \le b) =\displaystyle \int_a^b f(x) \, dx \)

Properties of PDF:

      • \(f(x) \ge 0\) for all \(x\)
      • \(\displaystyle \int_{-\infty}^{\infty} f(x) \, dx = 1 \)

Expectation (mean) and variance:

Expectation: \( \mathrm{E}(X) = \displaystyle\int_{-\infty}^{\infty} x f(x) \, dx \)

Variance: \( \mathrm{Var}(X) = \displaystyle\int_{-\infty}^{\infty} (x – \mathrm{E}(X))^2 f(x) \, dx \)

Median and percentiles:

The median \(m\) is the value for which half the area under the PDF lies to the left: \( \displaystyle\int_{-\infty}^{m} f(x) \, dx = 0.5 \)

Percentiles are found similarly: the \(p\)th percentile \(x_p\) satisfies \( \displaystyle\int_{-\infty}^{x_p} f(x) \, dx = p/100 \)

Example:

Let \(X\) have PDF \( f(x) = \dfrac{3}{x^4} \) for \( x \ge 1 \). Find \(P(X > 2)\), \(\mathrm{E}(X)\), and \(\mathrm{Var}(X)\).

▶️ Answer/Explanation

Probability: \( P(X > 2) = \int_2^{\infty} \dfrac{3}{x^4} dx = 3 \left[ \dfrac{x^{-3}}{-3} \right]_2^\infty = 2^{-3} = \dfrac{1}{8} \)

Expectation: \( \mathrm{E}(X) = \int_1^\infty x \cdot \dfrac{3}{x^4} dx = \dfrac{3}{2} \)

Variance: \( \mathrm{Var}(X) = \int_1^\infty x^2 \cdot \dfrac{3}{x^4} dx – (\mathrm{E}(X))^2 = 3 – \left(\dfrac{3}{2}\right)^2 = \dfrac{3}{4} \)

Final Answer: \(\boxed{P(X>2) = \frac{1}{8}, \; \mathrm{E}(X) = \frac{3}{2}, \; \mathrm{Var}(X) = \frac{3}{4}}\)

Example:

Find the median \(m\) of \(X\) with PDF \( f(x) = \dfrac{3}{x^4} \), \(x \ge 1\).

▶️ Answer/Explanation

The median satisfies \( \int_1^m \dfrac{3}{x^4} dx = 0.5 \)

Compute the integral: \( 3 \left[ \dfrac{x^{-3}}{-3} \right]_1^m = 0.5 \Rightarrow -(m^{-3} – 1) = 0.5 \Rightarrow 1 – m^{-3} = 0.5 \Rightarrow m^{-3} = 0.5 \)

So \( m = 0.5^{-1/3} = 2^{1/3} \approx 1.26 \)

Final Answer: \(\boxed{m \approx 1.26}\)

Example:

Find the 75th percentile \(x_{0.75}\) of \(X\) with the same PDF.

▶️ Answer/Explanation

75th percentile satisfies \( \int_1^{x_{0.75}} \dfrac{3}{x^4} dx = 0.75 \)

Compute: \( 1 – x_{0.75}^{-3} = 0.75 \Rightarrow x_{0.75}^{-3} = 0.25 \Rightarrow x_{0.75} = 0.25^{-1/3} = 4^{1/3} \approx 1.587 \)

Final Answer: \(\boxed{x_{0.75} \approx 1.587}\)

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