Posts

Poisson Distribution

Poisson Distribution: Definition, Moments, MGF and Solved Examples Definition The Poisson Distribution is a discrete probability distribution used to model the number of times an event occurs in a fixed interval of time, space, area, or volume, when the events occur independently and at a constant average rate. A random variable \(X\) is said to follow a Poisson distribution with parameter \(\lambda > 0\) if \[ \boxed{ P(X=x)=\frac{e^{-\lambda}\lambda^x}{x!}, \qquad x=0,1,2,\ldots } \] We write \[ X\sim\operatorname{Poisson}(\lambda). \] Here, \(\lambda\) represents the average number of occurrences in the specified interval. Main Conditions Events occur independently. The average rate of occurrence is constant. The number of occurrences in disjoint intervals is independent. In a sufficiently small interval, the probability of more than one occurrence is negligible. Moments of the Poisson Distribution Let \[ X\sim\operatorname{Poisson}(\...

Binomial Distribution

Binomial Distribution: Definition, Moments, MGF and Solved Examples A Binomial Distribution is a discrete probability distribution that gives the probability of obtaining exactly \(x\) successes in \(n\) independent Bernoulli trials, where the probability of success remains constant. If \(X \sim \operatorname{Bin}(n,p)\), then the probability mass function is \[ P(X=x)=\binom{n}{x}p^xq^{\,n-x}, \qquad x=0,1,2,\ldots,n, \] where \(n\) = number of trials, \(p\) = probability of success, \(q=1-p\) = probability of failure, \(X\) = number of successes. Conditions for Binomial Distribution The number of trials \(n\) is fixed. Each trial has only two possible outcomes: success or failure. The probability of success \(p\) remains constant. The trials are independent. Moments of Binomial Distribution Let \[ X\sim \operatorname{Bin}(n,p), \qquad q=1-p. \] First Moment: Mean \[ \boxed{E(X)=np} \] Second Raw Moment Since \[ E[X(X-1)]=n(n...

Chapter 5: Probability Distribution Function

Discrete and Continuous Probability Functions 1. Discrete Probability Function Let \(X\) be a discrete random variable. A function \[ p(x)=P(X=x) \] is called the probability mass function (PMF) or discrete probability function of \(X\). For a function \(p(x)\) to be a valid discrete probability function, it must satisfy the following conditions: \[ \boxed{p(x)\geq 0} \] and \[ \boxed{\sum_x p(x)=1}. \] Thus, the probability of an event such as \(a\leq X\leq b\) is obtained by adding the corresponding probabilities: \[ P(a\leq X\leq b) = \sum_{x=a}^{b}p(x). \] 5 Solved Examples on Discrete Probability Functions Example 1: Determine the Constant in a PMF A discrete random variable \(X\) has probability function \[ P(X=x)=kx,\qquad x=1,2,3,4. \] Find: The value of \(k\) \(P(X\leq 2)\) The mean \(E(X)\) Show Solution Since the total probability must be equal to 1, \[ \sum_{x=1}^{4} P(X=x)=1. \] Therefore, \[ k...

Random Variable

Random Variable: Definition, Types and 10 Solved Examples Definition of Random Variable A random variable is a real-valued function defined on the sample space of a random experiment. \( X:S\rightarrow \mathbb{R} \) It assigns a numerical value to every possible outcome of a random experiment. Example Suppose two coins are tossed. The sample space is \( S=\{HH,HT,TH,TT\}. \) Let \(X\) denote the number of heads obtained. Then \(X(HH)=2,\quad X(HT)=1,\quad X(TH)=1,\quad X(TT)=0.\) Therefore, \(X\in\{0,1,2\}.\) Types of Random Variables 1. Discrete Random Variable A random variable is called discrete if it takes a finite or countably infinite number of values. Its probability mass function (PMF) is \(p_X(x)=P(X=x)\) and it satisfies \(p_X(x)\geq 0,\qquad \sum_x p_X(x)=1.\) 2. Continuous Random ...

Questions on Function and Inverse Function

Functions and Inverse Functions – 10 Practice Questions Question 1. Determine whether the relation R = {(1,2), (2,4), (3,6), (4,8)} defines a function. Show Solution A relation is a function if every input has exactly one output. Here the inputs are 1, 2, 3, and 4, and each input has only one corresponding output. Therefore, R is a function. Question 2. Given f(x) = 3x - 5 find f(2), f(-1), and f(0). Show Solution For x = 2: f(2) = 3(2) - 5 = 6 - 5 = 1 For x = -1: f(-1) = 3(-1) - 5 = -3 - 5 = -8 For x = 0: f(0) = 3(0) - 5 = -5 Hence, f(2) = 1, f(-1) = -8, and f(0) = -5. Question 3. If f(x) = x 2 + 2x - 3 find f(a + 1). Show Solution Replace x by a + 1: f(a + 1) = (a + 1) 2 + 2(a + 1) - 3 Expanding, = a 2 + 2a + 1 + 2a + 2 - 3 = a 2 + 4a Therefore, f(a + 1) = a 2 + 4a. Question 4. Find the domain of f(x) = (2x + 1)/(x - 4) Show Solution The denominator cannot be zero. x - 4 ≠ 0 Therefore, x ≠ 4 Hence...

Animated Graph of Function

📘 Mathematics-I Interactive notes on mathematical functions, their domains, ranges, properties and animated graphs. Functions and Their Graphs A function describes a relationship between an input x and an output f(x) . Different functions produce characteristic curves. Click any function below to expand the section. The graph will then be drawn automatically. 1. Exponential Functions eˣ Exponential Function ⌄ f(x) = e x Domain ℝ Co-domain ℝ Range (0, ∞) The value of e x is always positive. The x-axis is a horizontal asymptote. ▶ Replay Graph 2ˣ Exponential Function ⌄ f(x) = 2 x Domain ℝ Co-domain ℝ Range (0, ∞) The graph passes through (0,1) and increases rapidly as x increases. ▶ Replay Graph 2. Trigonometric Functions sin Sine Function ⌄ f(x) = sin x Domain ℝ Co-domain ℝ Range ...