##### State and apply the Central Limit Theorem

For this chapter, a certain knowledge of normal distribution and knowing how to use a table for the normal distribution is assumed The central limit theorem is of the most important results in the probability theory. It states that the…

##### Calculate joint moments, such as the covariance and the correlation coefficient

Covariance and Correlation Coefficient for Joint Random Variables In learning outcomes covered previously, we have looked at the joint p.m.f. of two discrete/continuous random variables $$X$$ and $$Y$$, and we have also established the condition required for $$X$$ and $$Y$$ what…

##### Calculate variance, standard deviation for conditional and marginal probability distributions

Variance and standard deviation for joint random variables The second moment or variance is a derivative of the first moment and it is equal to: $$Var(X,Y)= E(g(X^2,Y^2)) – (E[g(X,y)])^2$$ The standard deviation of joint random variables is no more than…

##### Explain and apply joint moment generating functions

The moment-generating function Introduction We can derive moments of most distributions by evaluating probability functions by integrating or summing values as necessary. However, moment generating functions present a relatively simpler approach to obtaining moments. In the univariate case, the moment…

##### Calculate moments for joint, conditional, and marginal random variables

Moments of a Probability Mass function The n-th moment about the origin of a random variable is the expected value of its n-th power. Moments about the origin are $$E(X),E({ X }^{ 2 }),E({ X }^{ 3 }),E({ X }^{ 4 }),….\quad$$ For the…

##### Determine conditional and marginal probability functions

Conditional Distributions Conditional probability is a key part of Baye’s theorem. In plain language, it is the probability of one thing being true given that another thing is true. It differs from joint probability, which is the probability that both things…

##### Explain and perform calculations concerning joint probability functions

Bivariate Distributions of the discrete type (Joint Probability) Sometimes certain events can be defined by the interaction of two measurements. These types of events that are explained by the interaction of the two variables constitute what we call bivariate distributions….