1. Joint Probability distribution

2. Joint Probability Distributions
Given two random variables X and Y that are defined on the same probability space, the Joint Distribution for X and Y defines the probability of events defined in terms of both X and Y.
In the case of only two random variables, this is called a Bivariate Distribution.
The concept generalizes to any number of random variables, giving a Multivariate Distribution.

3. Example
Consider the roll of a die and let A = 1 if the number is even (2, 4, or 6) and A = 0 otherwise. Furthermore, let  B = 1 if the number is prime (2, 3 or 5) and B = 0 otherwise. Find the Joint Distribution of A and B?
P(A = 0, B = 0) = {1} = 1/6
P(A = 0, B = 1) = {3, 5} = 2/6
P(A = 1, B = 0) = {4, 6} = 2/6
P(A = 1, B = 1) = {2} = 1/6

4. Example Y X X1 = 0 Y1 = 0 Y2 = 1 Rows Total 1/6 2/6 3/6 = 1/2 X2 = 1
Column Total 1

5. Joint Probability Distributions

6. Discrete Joint Probability Distribution
X and Y are Independent
f(x, y) = f(x) x f(y)
X and Y are Dependent

7. Discrete Joint Probability Distribution
A die is flipped and a coin is tossed Y X 1 2 3 4 5 6
Row Totals
Head
f(H, 1) = 1/12
1/2
Tail
f(T, 1) = 1/12
Column Totals
1/6

8. Marginal Probability Distributions
Marginal Probability Distribution: the individual probability distribution of a random variable.

9. Discrete Joint Probability Distribution
A die is flipped and a coin is tossed Y X 1 2 3 4 5 6
Marginal Probability of X
Head
f(H, 1) = 1/12
1/2
Tail
f(T, 1) = 1/12
Marginal Probability of Y
1/6

10. Joint Probability Distributions

11. Continuous Two Dimensional Distribution

12. Joint Probability Distributions
X: the time until a computer server connects to your machine , Y: the time until the server authorizes you as a valid user. Each of these random variables measures the wait from a common starting time and X <Y. Assume that the joint probability density function for X and Y is
Find the probability that X<1000 and Y<2000.

13. Joint Probability Distributions

14. Marginal Probability Distributions

15. Functions of Random Variables
Z = g(X, Y)
If we roll two dice and X and Y are the number of dice turn up in a trial, then Z = X + Y is the sum of those two numbers.
Discrete Distribution Function
Continuous
Distribution Function

16. Addition of Means
The Mean (Expectation) of a sum of random variables equals the sum of Means (Expectations).
E(X1 + X2 + … + Xn ) = E(X1) + E(X2) + … + E(Xn)

17. Multiplication of Means
The Mean (Expectation) of the product of Independent random variables equals the product of Means (Expectations).
E(X1 X2 … Xn ) = E(X1) E(X2) … E(Xn)

18. fXY(x, y) = fx(X) fy(Y) for all x and y
Independence
Two continuous random variables X and Y are said to be Independent, if
fXY(x, y) = fx(X) fy(Y) for all x and y

19. Addition of Variances
The Variance of the sum of Independent random variables equals the sum of Variances of these variables.
Б2 = Б12 + Б22 -2 БXY
БXY = Covariance of X and Y = E(XY) - E(X) E(Y)
If X and Y are independent, then
E(XY) = E(X) E(Y)
Б2 = Б12 + Б22

20. Problem 1
Let f(x, y) = k when 8 ≤ x ≤ 12 and 0 ≤ y ≤ 2 and zero elsewhere. Find k. Find P(X ≤ 11 and 1 ≤ Y ≤ 1.5) and P(9 ≤ X ≤ 13, and Y ≤ 1).

21. Problem 3
Let f(x, y) = k. If x > 0, y > 0, x +y < 3 and zero otherwise. Find k. Find P(X + Y ≤ 1) and P(Y > X).

22. Problem 7
What are the mean thickness and standard deviation of transfer cores each consisting of 50 layers of sheet metal and 49 insulating paper layers, if the metal sheets have mean thickness 0.5 mm each with a standard deviation of mm and paper layers have mean thickness 0.05mm each with a standard deviation of 0.02 mm?

23. Problem 9
A 5-gear Assembly is put together with spacers between the gears. The mean thickness of the gears is cm with a standard deviation of cm. The mean thickness of spacers is cm with a standard deviation of cm. Find the mean and standard deviation of the assembled units consisting of 5 randomly selected gears and 4 randomly selected spacers.

24. Problem 11
Show that the random variables with the density f(x, y) = x + y and g(x, y) = (x+1/2)(y+1/2) If 0 ≤ x ≤ 1 and 0 ≤ y ≤ 1 and f(x, y) = 0 and g(x, y) = 0 and zero otherwise, have the same Marginal Distribution.

25. Problem 13 f(x, y) = 0.01e-0.1(x + y)
An electronic device consists of two components. Let X and Y [months] be the length of time until failure of the first and second components, respectively. Assume that X and Y have the Probability Density
f(x, y) = 0.01e-0.1(x + y)
If x > 0 and y > 0 and zero otherwise.
a. Are X and Y dependent or independent?
b. Find densities of Marginal Distribution.
c. What is the probability that the first component has a lifetime of 10 months or longer?

26. Problem 15 f(x, y) = 0.25e-0.5(x + y)
Find P(X > Y) when (X, Y) has the Probability Density
f(x, y) = 0.25e-0.5(x + y)
If x ≥ 0, y ≥ 0 and zero otherwise.

27. Problem 17 f(0, 0) = f(1, 1) = 1/8 f(0, 1) = f(1, 0) = 3/8
Let (X, Y) have the Probability Function
f(0, 0) = f(1, 1) = 1/8
f(0, 1) = f(1, 0) = 3/8
Are X and Y independent?

28. Marginal Probability Distributions
Example: For the random variables in the previous example, calculate the probability that Y exceeds 2000 milliseconds.

29. Conditional Probability Distributions
When two random variables are defined in a random experiment, knowledge of one can change the probabilities of the other.

30. Conditional Mean and Variance

31. Conditional Mean and Variance
Example: From the previous example, calculate
P(Y=1|X=3), E(Y|1), and V(Y|1).

32. Independence
In some random experiments, knowledge of the values of X does not change any of the probabilities associated with the values for Y.
If two random variables are independent, then

33. Multiple Discrete Random Variables
Joint Probability Distributions
Multinomial Probability Distribution

34. Joint Probability Distributions
In some cases, more than two random variables are defined in a random experiment.
Marginal probability mass function

35. Joint Probability Distributions
Mean and Variance

36. Joint Probability Distributions
Conditional Probability Distributions
Independence

37. Multinomial Probability Distribution
A joint probability distribution for multiple discrete random variables that is quite useful in an extension of the binomial.

38. Multinomial Probability Distribution
Example: Of the 20 bits received, what is the probability that 14 are Excellent, 3 are Good, 2 are Fair, and 1 is Poor? Assume that the classifications of individual bits are independent events and that the probabilities of E, G, F, and P are 0.6, 0.3, 0.08, and 0.02, respectively.
One sequence of 20 bits that produces the specified numbers of bits in each class can be represented as: EEEEEEEEEEEEEEGGGFFP
P(EEEEEEEEEEEEEEGGGFFP)=
The number of sequences (Permutation of similar objects)=

39. Two Continuous Random Variables
Joint Probability Distributions
Marginal Probability Distributions
Conditional Probability Distributions
Independence

40. Conditional Probability Distributions

41. Conditional Probability Distributions
Example: For the random variables in the previous example, determine the conditional probability density function for Y given that X=x
Determine P(Y>2000|x=1500)

42. Conditional Probability Distributions
Mean and Variance

43. Conditional Probability Distributions
Example: For the random variables in the previous example, determine the conditional mean for Y given that x=1500

44. Independence

45. Independence
Example: Let the random variables X and Y denote the lengths of two dimensions of a machined part, respectively.
Assume that X and Y are independent random variables, and the distribution of X is normal with mean 10.5 mm and variance (mm)2 and that the distribution of Y is normal with mean 3.2 mm and variance (mm)2.
Determine the probability that 10.4 < X < 10.6 and 3.15 < Y < 3.25.
Because X,Y are independent

46. Multiple Continuous Random Variables

47. Multiple Continuous Random Variables
Marginal Probability

48. Multiple Continuous Random Variables
Mean and Variance
Independence

49. Covariance and Correlation
When two or more random variables are defined on a probability space, it is useful to describe how they vary together.
It is useful to measure the relationship between the variables.

50. Covariance
Covariance is a measure of linear relationship between the random variables. \
The expected value of a function of two random variables
h(X, Y ).

51. Covariance

52. Covariance

53. Covariance
Example: For the discrete random variables X, Y with the joint distribution shown in Fig. Determine

54. Correlation
The correlation is a measure of the linear relationship between random variables.
Easier to interpret than the covariance.

55. Correlation
For independent random variables

56. Correlation
Example: Two random variables , calculate the covariance and correlation between X and Y.

57. Bivariate Normal Distribution
Correlation

58. Bivariate Normal Distribution
Marginal distributions
Dependence

59. Bivariate Normal Distribution
Conditional probability

60. Bivariate Normal Distribution
Ex. Suppose that the X and Y dimensions of an injection-modeled part have a bivariate normal distribution with Find the P(2.95<X<3.05,7.60<Y<7.80)

61. Bivariate Normal Distribution
Ex. Let X, Y : milliliters of acid and base needed for equivalence, respectively. Assume X and Y have a bivariate normal distribution with
Covariance between X and Y
Marginal probability distribution of X
P(X<116)
P(X|Y=102)
P(X<116|Y=102)

62. Linear Combination of random variables

63. Linear Combination of random variables
Mean and Variance

64. Linear Combination of random variables
Ex. A semiconductor product consists of 3 layers. The variances in thickness of the first, second, and third layers are 25,40,30 nm2 . What is the variance of the thickness of the final product? Let X1, X2, X3, and X be random variables that denote the thickness of the respective layers, and the final product. V(X)=V(X1)+V(X2)+V(X3)= =95 nm2

65. Discrete Joint Probability Distribution
Given a bag containing 3 black balls, 2 blue balls and 3 green balls, a random sample of 4 balls is selected. Find Joint Probability Distribution of X and Y, if X = black balls and Y = blue balls.
f(x, y) 1 2
Row Totals
2/70
3/70
5/70
18/70
9/70
30/70 3
0/70
Column Total
15/70
40/70

66. Marginal Probability Distributions
f(x, y) 1 2
Marginal Probability of Y
2/70
3/70
5/70
18/70
9/70
30/70 3
0/70
Marginal Probability of X
15/70
40/70

67. Independence

68. Quiz # 4
Given a bag containing 3 black balls, 2 blue balls and 3 green balls, a random sample of 4 balls is selected. Find Joint Probability Distribution of X and Y, if X = black balls and Y = blue balls.