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Quantitative Methods

Quantitative Methods. Varsha Varde. PROBABILITY DISTRIBUTION. We Studied frequency distribution of raw data This helps us in knowing which outcomes occurred most often, which occurred least often etc.

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Quantitative Methods

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  1. Quantitative Methods Varsha Varde

  2. PROBABILITY DISTRIBUTION • We Studied frequency distribution of raw data • This helps us in knowing which outcomes occurred most often, which occurred least often etc. • After studying a large number of such patterns of data, statisticians derived a number of model patterns or model distributions • These distributions are called probability distributions • They combine the concept of frequency distribution with the concept of probability • Probability distributions are of two types: Discrete and Continuous Varde Varde

  3. Probability Distribution • A probability distribution is an array (or arrangement) that shows the probabilities of individual possible outcomes or of different values of a variable. • The sum of all the probabilities in a probability distribution is always equal to one, that is, if X is a variable with N possible values X1, X2, …, XN taken with probabilities P (X1), P (X2), .., P (XN) then N • ∑ P (Xi) = 1 i = 1 Varde Varde

  4. Discrete Probability Distribution • A probability distribution is said to be discrete if the values of the corresponding random variable are discrete. • We shall describe two typical discrete probability distributions namely, Binomial and Poisson distributions Varde Varde

  5. Continuous Probability Distribution • A probability distribution is continuous if the values of the corresponding random variable are continuous, that is, they fall in an interval. • We shall study one typical continuous distribution, namely, normal distribution . • Three more typical distributions of continuous type ( t distribution, chi square distribution and F distribution) Varde Varde

  6. Mean of a Probability Distribution • The mean of a probability distribution is the number obtained by multiplying all the possible values of the variables by the respective probabilities and adding these products together. It indicates the expected value the corresponding variable would take. • It is generally denoted by the greek letter μ or E(x) Varde Varde

  7. Variance of Probability Distribution • Variance of a probability distribution is the number obtained by multiplying each of the squared deviations from the mean by its respective probability and adding these products. • It is generally denoted by the greek letter σ2 orv(x) Varde Varde

  8. Formulae for Mean & Variance • If X is a variable with N possible values X1, …, XN taken with probabilities P (X1), P (X2), … P (XN) then • the expected value or the mean μ of this probability distribution is N • E(x)=μ = ∑ Xi P (Xi) • i = 1 • variance σ2 of this probability distribution is • N • σ2= ∑ (Xi – μ)2 P (Xi) i = 1 • Standard Deviationσ = √V (X) = √∑ (x - µ)2p(x) Varde Varde

  9. EXAMPLE • Roll a pair of dice and work out the probability distribution of the sums of the spots that appear on the faces. • The variable sum of dots on two faces would lie between 2 and 12 • The probability distribution of this variable would be: • Sum: 2 , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 • Prob: 1/36, 2/36 , 3/36, 4/36, 5/36 ,6/36 ,5/36, 4/36, 3/36, 2/36, 1/36 • It can be seen that the sum of the probabilities in this case is 36 / 36 = 1 indicating that this is a complete probability distribution. Varde Varde

  10. EXAMPLE • The mean μ of this probability distribution is: • μ = 2 /36 + 6/36 + 12/36 + 20/36 + 30/36 + 42/36 + 40/36 + 36/36 + 30/36 + 22/36 +12/36 = 7 • The variance σ2 is given by: • σ2 = 25/36 + 32/36 + 27/36 + 16/36 + 5/36 + 0/36 + 5/36 + 16/36 + 27/36 + 32/36 + 25/36 – • = 212 / 36 = 5.83 • σ = √5.83 = 2.415 • Thus when a pair of dice is rolled the expected value of the sum of spots that would appear is 7 with the actual value differing from this expected value to the extent of 2.41 on either side. Varde Varde

  11. Random Variables and DiscreteDistributions

  12. Contents. • Random Variables • Expected Values and Variance • Binomial • Poisson Varde Varde

  13. The discrete r.v • The discrete r.v arises in situations when possible outcomes are discrete . • Example. Toss a coin 3 times, then • S = {HHH,HHT,HTH,HTT, THH, THT, TTH, TTT} • Let the variable of interest, X, be the number of heads observed then relevant events would be • {X = 0} = {TTT} • {X = 1} = {HTT,THT,TTH} • {X = 2} = {HHT,HTH, THH} • {X = 3} = {HHH}. • The relevant question is to find the probability of each these events. • Note that X takes integer values even though the sample space consists of H’s and T’s. Varde Varde

  14. Discrete Distributions • The probability distribution of a discrete r.v., X, assigns a probability p(x) for each possible x such that • (i) 0≤ p(x) ≤1, and • (ii) ∑ p(x) = 1 • where the summation is over all possible values of x. Varde Varde

  15. Discrete distributions in tabulated form • Example. • Which of the following defines a probability distribution? (i) X 0 1 2 p(x) 0.30 0.50 0.20 (ii) X 0 1 2 p(x) 0.60 0.50 -0.10 (iii) x -1 1 2 p(x) 0.30 0.40 0.20 Varde Varde

  16. Expected Value and Variance • Definition The expected value of a discrete r.v X is denoted by µ and is defined to be • µ =∑xp(x). • Notation: The expected value of X is also denoted by µ = E[X]; or sometimes µXto emphasize its dependence on X. • Definition If X is a r.v with mean µ, then the variance of X is defned by • σ2 =∑(x - µ)2p(x) • Notation: Sometimes we use σ2 = V (X) (or σ2X). Varde Varde

  17. Standard Deviation • Definition If X is a r.v with mean µ, then the standard deviation of X, denoted by • σX, (or simply σ) is defined by • σX = √V (X) = √∑ (x - µ)2p(x) Varde Varde

  18. Discrete Distributions-Binomial. • The binomial experiment (distribution) arises in following situation: • (i) the underlying experiment consists of n independent and identical trials; • (ii) each trial results in one of two possible outcomes, a success or a failure; • (iii) the probability of a success in a single trial is equal to p and remains the same throughout the experiment; and • (iv) the experimenter is interested in the r.v X that counts the number of successes observed in n trials. • A r.v X is said to have a binomial distribution with parameters n and p if • p(x) = nCx pxqn-x(x = 0, 1, . . . , n) • where q = 1- p. • Mean: µ = np • Variance: σ2= npq, Standard Deviationσ= v npq Varde Varde

  19. Bernoulli. • when probability of occurrence of a particular event is constant say p ,the Binomial Distribution gives probabilities of number of occurrences of the event in a series of n trials • A r.v X is said to have a Bernoulli distribution with parameter p if n=1 viz only one trial is performed • Formula: p(x) = px(1 - p)1-x ; x = 0, 1. • Tabulated form: • X 0 1 • p(x) 1-p p • Mean: µ = p • Variance: σ2= pq ,σ = v pq Varde Varde

  20. Examples of Binomial Situation • There are many situations where the outcomes can be grouped into two categories. Binomial distribution is appropriate in describing these situations • An employee aspiring for promotion may either be promoted or not promoted, • A loan application may either be sanctioned or not sanctioned, • Amount advanced may either be recovered or not recovered, • A manager may either be retained at HO or may be transferred • Indian Captain may either win a toss or lose • All these situations are such that the outcomes can be grouped into two categories and hence binomial distribution can be used to explain and analyse the underlying situation.

  21. Example • Binomial Tables. • Cumulative probabilities are given in the table. • Example. Suppose X has a binomial distribution with n = 10, p = .4. Find • (i) P(X = 4) = .633 • (ii) P(X <6) = P(X ≤5) = .834 • (iii) P(X >4) = 1 - P(X ≤4) = 1 - .633 = .367 • (iv) P(X = 5) = P(X ≤5) - P(X≤4) = .834 - .633 = .201 • Exercise: Answer the same question with p = 0.7 Varde Varde

  22. Poisson. • In situations when n is large and p is small the binomial distribution assumes a limiting form known as the Poisson distribution • The Poisson random variable arises when we can count number of occurrences of an event but cannot count the number of trials made examples include • number of accidents on a road , arrivals at an emergency room, number of defective items in a batch of items, number of goals scored in a football match • A r.v X is said to have a Poisson distribution with parameter m > 0 if • p(x) = e-m.mx/x!, x = 0, 1, . . . • Mean: µ = m • Variance: σ2 = m, σ= vm • Note: e 2.71828 Varde Varde

  23. Example • Suppose the number of typographical errors on a single page of your book has a Poisson distribution with parameter m= 1/2. Calculate the probability that there is at least one error on this page. • Solution. Letting X denote the number of errors on a single page, we have • P(X ≥ 1) = 1 − P(X = 0) = 1 − e−0.5 =0.395 • Rule of Thumb. The Poisson distribution provides good approximations to binomial probabilities when n is large and μ = np is small, preferably with np ≤ 7. • Example. Suppose that the probability that an item produced by a certain machine will be defective is 0.1. Find the probability that a sample of of 10 items will contain at most 1 defective item. • Solution. Using the binomial distribution, the desired probability is • P(X ≤ 1) = p(0) + p(1) =10C0 (0.1)0(0.9)10 +10C1(0.1)1(0.9)9 = 0.7361 • Using Poisson approximation, we have m= np = 1 • e−1 +1. e−1 ≈0.7358 • which is close to the exact answer. Varde Varde

  24. Continuous Distributions Contents. • 1. Standard Normal • 2. Normal Varde Varde

  25. Introduction • RECALL: The continuous rv arises in situations when the population (or possible outcomes) are continuous. • Example. Observe the lifetime of a light bulb, then • S = {x, 0 ≤ x < ∞} • Let the variable of interest, X, be observed lifetime of the light bulb then relevant events • would be {X ≤ 100}, {X ≥ 1000}, or {1000 ≤ X ≤ 2000}. • The relevant question is to find the probability of each these events. • Important. For any continuous pdf the area under the curve is equal to 1. Varde Varde

  26. Standard Normal Distribution • Standard Normal. A normally distributed (bell shaped) random variable with μ = 0 and σ = 1 is said to have the standard normal distribution. It is denoted by the letter Z. • pdf of Z: • f(z) =1/√2π e−z2/2 ;−∞ < z < ∞, • Tabulated Values. • Values of P(0 ≤ Z ≤ z) are tabulated in standard normal tables • Critical Values: zαof the standard normal distribution are given by • P(Z ≥ zα) = α • which is in the tail of the distribution. Varde Varde

  27. Examples. • (i) P(0 ≤ Z ≤ 1) = .3413 • (ii) P(−1 ≤ Z ≤ 1) = .6826 • (iii) P(−2 ≤ Z ≤ 2) = .9544 • (iv) P(−3 ≤ Z ≤ 3) = .9974 • Examples. Find z0 such that • (i) P(Z > z0) = .10; z0 = 1.28. • (ii) P(Z > z0) = .05; z0 = 1.645. • (iii) P(Z > z0) = .025; z0 = 1.96. • (iv) P(Z > z0) = .01; z0 = 2.33. • (v) P(Z > z0) = .005; z0 = 2.58. • (vi) P(Z ≤ z0) = .10, .05, .025, .01, .005. (Exercise Varde Varde

  28. Normal • A rv X is said to have a Normal pdf with parameters μ and σ if • Formula: • f(x) =1/σ√2π{ e−(x−μ)2/2σ2 } ;−∞ < x < ∞, • .Properties • Mean: E[X] = μ -∞ < μ < ∞; • Variance: V (X) = σ2 0 < σ < ∞ • Graph: Bell shaped. • Area under graph = 1. Varde Varde

  29. Standardizing a normal r.v.: • Standardizing a normal r.v.: • Z-score: • Z =( X − μX ) / σX • OR (simply) • Z = ( X − μ )/ σ • Conversely, • X = μ + σZ . Varde Varde

  30. Example • Example If X is a normal rv with parameters μ = 3 and σ2 = 9, find (i) P(2 < X < 5),(ii) P(X >0), and (iii) P(X >9). • Solution • (i) P(2 < X < 5) = P(−0.33 < Z < 0.67) = .3779. • (ii) P(X >0) = P(Z > −1) = P(Z < 1) = .8413. • (iii) P(X >9) = P(Z > 2.0) = 0.5 − 0.4772 = .0228 • Exercise Refer to the above example, find P(X <−3). • Example The length of life of a certain type of automatic washer is approximately normally distributed, with a mean of 3.1 years and standard deviation of 1.2 years. If this type of washer is guaranteed for 1 year, what fraction of original sales will require replacement? • Solution Let X be the length of life of an automatic washer selected at random, then • z =(1 − 3.1)/1.2 =−1.75 • Therefore • P(X <1) = P(Z < −1.75) = Varde Varde

  31. Normal Approximation to the Binomial Distribution. • When and how to use the normal approximation: • 1. Large n, i.e. np ≥ 5 and n(1 − p) ≥ 5. • 2. The approximation can be improved using correction factors. • Example. Let X be the number of times that a fair coin, flipped 40, lands heads. • (i) Find the probability that X = 20. (ii) Find P(10 ≤ X ≤ 20). Use the normal approximation. • Solution Note that np = 20 and np(1 − p) = 10. • P(X = 20) = P(19.5< X < 20.5) • = P([19.5 − 20]/√10<[X − 20/]√10<[20.5 − 20]/√10) • P(−0.16 < Z < 0.16) = .1272. • The exact result is • P(X = 20) =40C20(0.5)20(0.5)20 = .1268 Varde Varde

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