Random Numbers:
Random Variables:
(a) Discrete Random Variable, and (b) Continuous Random Variable. (a) Discrete Random Variable: A random variable which can assume only a finite number of values or a sequence of whole numbers is called a discrete random variable. For example, the number of spots on a die is a discrete random variable, number of persons enrolled for CSS examinations, number of students passed in 1st division in a particular class, number of defective items in a lot, etc. are discrete random variables, which could assume any of the possible values, i.e., 1, 2, 3……. (b) Continuous Random Variable: A random variable which can assume all possible values on a continuous scale in a given interval is called a continuous random variable. For example, height, weight, temperature, distance, life periods, speed, etc. are continuous random variables. Example: A coin is tossed three times. Find the possibility space and define two random variables for this possibility space. Solution: S = {HHH, HHT, HTH, THH, HTT, TTH, THT, TTT} (i) Let a random variable (X) the number of heads: X = no. of heads. Note: The same value may be assigned to different outcomes of the possibility space. (ii) Let a random variable (X) head as +1 and tail as –1: Probability Distribution:
(a) Discrete Probability Distribution, and (b) Continuous Probability Distribution. (a) Discrete Probability Distribution: Let a discrete random variable X assume values x1, x2, x3, ……….., xn with respective probabilities P(x1), P(x2), P(x3), …………, P(xn). Since the random variable takes a discrete set of values, it is also called a discrete probability distribution. A discrete probability distribution may take the form of a table, a graph or a mathematical equation. A probability distribution is similar to a relative frequency distribution with probabilities replacing relative frequencies. A discrete probability distribution must possess the following two properties: (i) 0 ≤ P(xi) ≤ 1 (ii) ∑P(xi) = 1, which means that the sum of probabilities is equal to one. Example: A coin is tossed three times. Find the probability distribution of the random variable number of heads. Solution: S = {HHH, HHT, HTH, THH, HTT, THT, TTH, TTT}
Example: Determine whether the function for X = 1, 2, 3 and 4 can be a probability distribution.Solution:
(b) Continuous Probability Distribution: As we known that a random variable which can assume all possible values within a given interval is called a continuous random variable. Within a given interval, there are an infinite number of values. For example, there may be an infinite number of weights between 69.5 kgs and 70.5 kgs. In case of a continuous random variable, therefore, we compute probabilities for various intervals of continuous random variable, such as P(a ≤ X ≤ b) or P(X ≥ c). The probability distribution of a continuous random variable cannot be presented in tabular form. It can be represented by means of a formula or through a graph. The formula is necessarily in the form of a function of the numerical values of the continuous random variable X. For e.g., a continuous random variable can assume values between X = 2 and X = 4 and the function is given by: The continuous probability distribution is further discussed in detail later. Mean and Variance of a Random Variable: In a probability distribution of a random variable X, the mean, also referred to as ‘Mathematical Expectation’ or ‘Expected Value’, and the variance are defined as: μ = E(X) = Σ X · P(X) and σ2 = V(X) = Σ X2 · P(X) – [E(X)]2 Distribution Function: A function showing probabilities that a random variable X has a value less than or equal to x is called the ‘cumulative distribution function’ or ‘distribution function of x’. Symbolically, the cumulative distribution function, denoted by f(x) is defined as: The cumulative distribution function has the following properties: (i) f(– ∞) = 0 and f(∞) = 1, which means that f(x) is an increasing function ranging from 0 to 1. (ii) If a < b then f(a) < f(b) for any real numbers a and b. For a discrete random variable, distribution function is obtained by cumulating probabilities just as we obtained cumulative distribution. The distribution function for the probability distribution of the previous two examples is as below:
Example: Calculate the mean and variance for the following probability distribution:
Solution:
μ = E(X) = Σ X · P(X) = 2.4 σ2 = V(X) = Σ X2 · P(X) – [E(X)]2 = 8.06 – (2.4)2 = 2.3 Binomial Probability Distribution:
(i) If an experiment contains only two possible outcomes, i.e., success or failure. (ii) The probability of ‘success’ is denoted by ‘p’ and the probability of ‘failure’ is denoted by ‘q’ where q = 1 – p or p + q = 1. (iii) Such an experiment is repeated n times independently. In independent repetitions, the probability p remains constant.
Where x = 1, 2, 3, 4, ………, n The above formula is ‘Binomial Probability Distribution’. The two constant quantities p and n are called the parameters of a Binomial Distribution. The quantity q is not a separate parameter because q = 1 – p. Mean and Variance of a Binomial Distribution: The mean and variance of a binomial distribution are directly evaluated in terms of its parameters p and n. Example: A coin is tossed 3 times. ‘Number of heads’ in 3 tosses is the random variable X. Calculate probabilities of all possible values of X. Also calculate mean and variance. Solution: Experiment: A coin is tossed for 3 times. Success: Head p = P(success) = P(head) = ½ n = number of times the coin is tossed = 3 x = 0, 1, 2, 3. Now applying the Binomial Formula: Mean and Variance:
Hyper Geometric Probability Distribution:
(i) There are N items of which K are of first kind and the remaining (N – K) are of second kind, (ii) A sample of n items is randomly drawn without replacement from the N items. Number of items of first kind in the sample is the random variable X: Possible values of X are 0, 1, 2, ………, k when n ≥ K and 0, 1, 2, ……….., n when n < K
Where x = 0, 1, 2, 3, …….., k when n ≥ k And x = 0, 1, 2, 3, ………, n when n< k The above formula is called ‘Hyper Geometric Probability Distribution’. A schematic explanation of this formula may be given as: Example: A committee of 3 persons is to be formed from among 3 men and 2 women. If the selection of the committee members is random, construct the probability distribution of the random variable ‘Number of women in the committee’. Solution: The Hyper Geometric Probability Distribution of RV ‘No. of Women in the Committee’ is as follows: Poisson Probability Distribution:
Where x = 0, 1, 2, 3, ……..
(i) The number of events per unit of time or space remains stable for a long period of time. This is the parameter of the distribution denoted by λ. (ii) The number of events in one time period is independent of the number of events in another time period. Example: In an industry, the average number of damaged output units per week is 10. What is the probability that there will be (i) no damaged unit in the next week, (ii) 5 damaged units in the next week, and (iii) 15 damaged units in the next week. Solution: (i) no damaged unit in the week: X = number of damaged output units next week = 0 λ = average number of damaged units per week = 10 (ii) 5 damaged units in the next week: X = number of damaged output units next week = 5 λ = average number of damaged units per week = 10 (iii) 15 damaged units in the next week: X = number of damaged output units next week = 15 λ = average number of damaged units per week = 10 Poisson Approximation to Binomial Distribution: The computations involved in the binomial distributions become quite tedious when n is large. In such cases the binomial distribution can be approximated to a Poisson distribution with λ = n ּ p under the following conditions: (i) n is very large, (ii) p is very small, and (iii) n ּ p is finite. A frequently used rule of thumb is that the approximation is appropriate when p ≤ 0.05 and n ≥ 20. However, the Poisson distribution sometimes provides close approximations even in cases where n is not large nor p is very small. Example: In a village, the local government approximated that 2% of the population are infected with seasonal flu due to absence of proper medication. What is the probability that the number of infected persons in a random sample of 50 will be 4? Solution: Using binomial distribution with: n = 50, p = 0.02 and x = 4 Using Poisson approximation to the binomial with: λ = n ּ p = 50 × 0.02 = 1 The Poisson probability is close to the binomial probability. Mean and Variance of Poisson Distribution: The mean of a Poisson Random Variable is the parameter of the Poisson distribution λ, that is: E(X) = λ The variance is also the parameter λ: V(X) = λ Thus mean and variance of Poisson distribution are equal to λ. Continuous Probability Distribution (In Detail):
(i) the function is non-negative for all possible values of the random variable, and (ii) the total area under the curve of the function is one. This function is called ‘probability density function’ and its curve a ‘probability curve’.
Normal Probability Distribution:
Where – ∞ ≤ x ≤ ∞
Area under Normal Curve:
(i) Determine the z-values for each limit of interval, (ii) From the normal area table, determine the area for each z-value, (iii) Subtract the smaller area from the larger one.
Where μ and σ are the mean and standard deviation of the random variable z.
x = μ + σ · z
Example: A normal random variable x has mean µ = 24 and standard deviation σ = 1.8. Determine z values for x = 14, 15.9, 29.2 and 33. Also show these values on normal curve. Solution: Example: A normal random variable x has mean μ = 36 and standard deviation 2.05, determine the values of x for z = – 3.36, – 1.8, 0.95 and 2.75. Solution: x = μ + σ · z For z = – 3.36; x = 36 + 2.05 × (– 3.36) = 29.112 ≈ 29.11 For z = – 1.8; x = 36 + 2.05 × (– 1.8) = 32.31 For z = 0.95; x = 36 + 2.05 × 0.95 = 37.9475 ≈ 37.95 For z = 2.75; x = 36 + 2.05 × 2.75 = 41.6375 ≈ 41.64 Example: The mean and SD of a normal random variable are 34.5 and 5.8 respectively. Find the following areas: (i) to the left of 19.5 (ii) to the right of 40 (iii) between 19.5 and 40 Solution: (i) to the left of 19.5, i.e., P(x ≤ 19.5): P(– ∞ ≤ x ≤ 19.5) = P(– ∞ ≤ z ≤ –2.59) = 0.0048 Where (ii) to the right of 40, i.e., P (x ≥ 40): P(40 ≤ x ≤ ∞) = P(0.95 ≤ z ≤ ∞) = 0.8289 Where (ii) between 19.5 and 40, i.e., P(19.5 ≤ x ≤ 40): P(19.5 ≤ x ≤ 40) = P(– 2.59 ≤ z ≤ 0.95) = 0.8289 – 0.0048 = 0.8241 Continuity Correction:
Normal Approximation to Binomial Distribution: A Binomial Distribution with large n and moderate p can be approximated to a Normal Distribution with mean μ = nּp and :μ = nּp Example: A pair of dice is rolled for 800 times. What is the probability that a total of 6 occur: (i) at least 100 times, and (ii) between 150 to 300 times. Solution: (i) Probability of at least 100 times, i.e., P(100 ≤ x ≤ 800) or P(99.5 ≤ x ≤ 800.5): P(–1.19 ≤ z ≤ 70.49) From ‘Normal Area Table’ the Normal Area corresponding to – 1.19 is 0.1170 = 1 – 0.1170 = 0.8830 (ii) Probability of between 150 and 300 times, i.e., P(130 ≤ x ≤ 300) or P(149.5 ≤ x ≤ 300.5): P(1.88 ≤ z ≤ 19.37) From ‘Normal Area Table’ the Normal Area corresponding to 1.88 is 0.9699 = 1 – 0.9699 = 0.0301 |