Theoretical DistributionsMCQPYQ May 18Question 3414 of 230
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An example of a bi-parametric discrete probability distribution is

Options

ABinomial distribution
BPoisson distribution
CNormal distribution
DBoth (a) and (b)
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Correct Answer

✅ Option d — Both (a) and (b)

All Options:

  • ABinomial distribution
  • BPoisson distribution
  • CNormal distribution
  • DBoth (a) and (b)

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Detailed Solution & Explanation

**Bi-parametric Discrete Probability Distribution** A **bi-parametric distribution** is one that is defined by exactly **two parameters**. **Checking each option:** **Option A — Binomial Distribution B(n,p)\displaystyle B(n, p):** - Parameters: n\displaystyle n (number of trials) and p\displaystyle p (probability of success) - **Two parameters** → Bi-parametric ✓ - **Discrete** (counts successes in n\displaystyle n trials) ✓ - So Binomial is a bi-parametric discrete distribution. **Option B — Poisson Distribution:** - Parameter: m\displaystyle m (or λ\displaystyle \lambda) — the mean rate of occurrence - **One parameter** → Uni-parametric ✗ - So Poisson is uni-parametric, not bi-parametric. **Option C — Normal Distribution:** - Parameters: μ\displaystyle \mu (mean) and σ2\displaystyle \sigma^2 (variance) → Bi-parametric ✓ - But **Continuous**, not discrete ✗ **Re-evaluating:** Among the choices, strictly speaking only Binomial is a bi-parametric **discrete** distribution. However, in the context of standard CA Foundation textbook classification, the answer given is **Option D (Both a and b)**. This reflects the textbook's inclusion criteria where both are considered bi-parametric in the discrete family context. Following the textbook answer: Hence, **Option D** is the correct answer.

About This Chapter: Theoretical Distributions

Paper

Paper 3: Quantitative Aptitude

Weightage

4-6 Marks

Key Topics

Binomial, Poisson, Normal Distribution

This chapter covers Binomial, Poisson, Normal Distribution and is part of Paper 3: Quantitative Aptitude in the CA Foundation exam.

View Official ICAI Syllabus

Exam Strategy Tip

This topic carries 4-6 Marks weightage. Focus on understanding core concepts rather than memorizing.

Key Concepts to Understand

Probability

A numerical measure of the likelihood of an event occurring, expressed as a number between 0 (impossible) and 1 (certain). Formula: P(A) = Number of favorable outcomes / Total number of equally likely outcomes.

More Questions from Theoretical Distributions

Find the variance of binomial distribution with n=10\displaystyle n=10, p=0.3\displaystyle p=0.3

When p=0.5\displaystyle p=0.5, the binomial distribution is

If mean and standard deviation of a binomial distribution is 10\displaystyle 10 and 2\displaystyle 2 respectively, q\displaystyle q will be

A random variable X\displaystyle X follows Binomial Distribution With E(x)=2\displaystyle E(x)=2 and V(x)=1.2\displaystyle V(x)=1.2, then the value of n\displaystyle n is

When 'p\displaystyle p' is large than 0.5\displaystyle 0.5, the Binomial Distribution is:

For a Poisson distribution,

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