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Chapter 5 Discrete Random Variables and Probability Distributions Statistika.

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1 Chapter 5 Discrete Random Variables and Probability Distributions Statistika

2 After completing this chapter, you should be able to:  Interpret the mean and standard deviation for a discrete random variable  Use the binomial probability distribution to find probabilities  Describe when to apply the binomial distribution  Use the hypergeometric and Poisson discrete probability distributions to find probabilities  Explain covariance and correlation for jointly distributed discrete random variables Chapter Goals

3  Variabel Random/Random Variable  Variabel random adalah suatu fungsi yang memetakan anggota ruang sampelke bilangan real. Nilainya berubah- ubah Introduction to Probability Distributions Random Variables Discrete Random Variable Continuous Random Variable

4  Hanya dapat bernilai angka yang dapat dicacah Contoh:  Melempar dadu 2x Ambil X = # mata dadu 4 muncul (X bisa bernilai 0, 1, atau 2 )  Melempar koin 5x. X = # Muka muncul (X = 0, 1, 2, 3, 4, or 5) Discrete Random Variables

5 Eksperimen: Melempar 2 koin. X = # Head. T T Discrete Probability Distribution Nilai x Probabiliti 0 1/4 = /4 = /4 =.25 4 hasil yg mungkin T T H H HH Distribusi Probabilitas x Probabiliti Tunjukkan P(x), P(X = x), u semua x:

6  P(x)  0 for any value of x  The individual probabilities sum to 1; (The notation indicates summation over all possible x values) Probability Distribution Required Properties

7 Cumulative Probability Function  The cumulative probability function, denoted F(x 0 ), shows the probability that X is less than or equal to x 0  In other words,

8 Expected Value (mean) dari distribusi diskrit (Weighted Average), Formula :  Contoh: Melempar 2 koin, X = # of heads, compute expected value of x: E(x) = (0 x.25) + (1 x.50) + (2 x.25) = 1.0 Nilai Harapan/Expected Value x P(x)

9 Variance and Standard Deviation  Variance of a discrete random variable X  Standard Deviation of a discrete random variable X

10 Standard Deviation Example  Example: Toss 2 coins, X = # heads, compute standard deviation (recall E(x) = 1) Possible number of heads = 0, 1, or 2

11 Functions of Random Variables  If P (x) is the probability function of a discrete random variable X, and g(X) is some function of X, t hen the expected value of function g is

12 Linear Functions of Random Variables  Let a and b be any constants.  a) i.e., if a random variable always takes the value a, it will have mean a and variance 0  b) i.e., the expected value of b·X is b·E(x)

13 Linear Functions of Random Variables  Let random variable X have mean µ x and variance σ 2 x  Let a and b be any constants.  Let Y = a + bX  Then the mean and variance of Y are  so that the standard deviation of Y is

14 X P(X)0,10,150,25 0,150,1 Chap 5-14  Contoh : Data penjualan laptop perbulan  Carilah nilai harapan banyaknya laptop terjual perbulan E(X) dan variansinya V(X)  Jika untuk satu laptop diambil keuntungan 500 ribu dan biaya pemeliharaan bulanan 600 ribu, hitunglah nilai harapan keuntungan penjualan laptop perbulan

15 Probability Distributions Continuous Probability Distributions Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions Uniform Normal Exponential Ch. 5Ch. 6

16 The Binomial Distribution Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions

17 Bernoulli Distribution  Consider only two outcomes: “success” or “failure”  Let P denote the probability of success  Let 1 – P be the probability of failure  Define random variable X: x = 1 if success, x = 0 if failure  Then the Bernoulli probability function is

18 Bernoulli Distribution Mean and Variance  The mean is µ = P  The variance is σ 2 = P(1 – P)

19 Binomial Probability Distribution  n percobaan bernoulli  e.g., 15 lemparan koin;  10 bola lampu diambil dari gudang  Antar percobaan saling asing dan independen  Ada x sukses dan n-x gagal, sebanyak n kombinasi x  Kita tertarik pada VR X = # kejadian sukses  P(X=x) ?

20  A manufacturing plant labels items as either defective or acceptable  A firm bidding for contracts will either get a contract or not  A marketing research firm receives survey responses of “yes I will buy” or “no I will not”  New job applicants either accept the offer or reject it Possible Binomial Distribution Settings

21 P(x) = Peluang x sukses dari n trial, n-x gagal Contoh: Menjawab lima soal B-S, x = # jawaban benar: n = 5 P = P = ( ) = 0.5 x = 0, 1, 2, 3, 4,5 P(x) n x ! nx P(1- P) X n X ! ( ) !    Binomial Distribution Formula

22 Berapa probabilitas benar satu soal ? x = 1, n = 5, and P = 0.5

23 Binomial Distribution  Bentuk dari distribusi binomial berdasar nilai p dan n n = 5 P = 0.1 n = 5 P = 0.5 Mean x P(x) x P(x) 0  n = 5 and P = 0.1  n = 5 and P = 0.5

24  Mean Binomial Distribution Mean and Variance  Variance and Standard Deviation Wheren = sample size P = probability of success (1 – P) = probability of failure

25 Binomial Characteristics n = 5 P = 0.1 n = 5 P = 0.5 Mean x P(x) x P(x) 0 Examples

26 Using Binomial Tables Nx…p=.20p=.25p=.30p=.35p=.40p=.45p= ………………………………………………………… Examples: n = 10, x = 3, P = 0.35: P(x = 3|n =10, p = 0.35) =.2522 n = 10, x = 8, P = 0.45: P(x = 8|n =10, p = 0.45) =.0229

27  Select PHStat / Probability & Prob. Distributions / Binomial… Using PHStat

28  Enter desired values in dialog box Here:n = 10 p =.35 Output for x = 0 to x = 10 will be generated by PHStat Optional check boxes for additional output Using PHStat

29 PHStat Output P(x = 3 | n = 10, P =.35) =.2522 P(x > 5 | n = 10, P =.35) =.0949

30 The Poisson Distribution Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions

31  Apply the Poisson Distribution when:  You wish to count the number of times an event occurs in a given continuous interval  The probability that an event occurs in one subinterval is very small and is the same for all subintervals  The number of events that occur in one subinterval is independent of the number of events that occur in the other subintervals  There can be no more than one occurrence in each subinterval  The average number of events per unit is (lambda) The Poisson Distribution

32 Poisson Distribution Formula where: x = number of successes per unit = expected number of successes per unit e = base of the natural logarithm system ( )

33  Mean Poisson Distribution Characteristics  Variance and Standard Deviation where = expected number of successes per unit

34 Using Poisson Tables X Example: Find P(X = 2) if =.50

35 Graph of Poisson Probabilities X = P(X = 2) =.0758 Graphically: =.50

36  The shape of the Poisson Distribution depends on the parameter : Poisson Distribution Shape = 0.50 = 3.00

37  Sebuah gerbang tol memiliki tingkat kedatangan rata- rata 360 kendaraan per jam.  Berarti permenit rata-ratanya adalah 6 kendaraan  Hitunglah peluang dalam satu menit ada 10 mobil yang datang ? Chap 5-37

38 Contoh soal distribusi Poisson Rata-rata jumlah panggilan lewat telepon yang masuk di bagian pelayanan adalah 5 buah permenit. 1.Berapa probabilitas dalam satu menit tertentu tidak terdapat panggilan yang masuk dari pelanggan? 2.Berapa probabilitas dalam satu menit lebih dari 7 panggilan masuk?

39  Select: PHStat / Probability & Prob. Distributions / Poisson… Poisson Distribution in PHStat

40  Complete dialog box entries and get output … Poisson Distribution in PHStat P(X = 2) =


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