Lecture 7

Question 1

Random variable

Answer 1

variable that assumes numerical values associated with the random outcomes of an experiment, where one (and only one) numerical value is assigned to each sample point

Question 2

discrete random variable

Answer 2

random variables that can assume a countable number (finite and infinite) of values are called discrete. examples are counts (number of patients or number of sales)

Question 3

continuous random variable

Answer 3

random variables that can assume values corresponding to any of the points contained in one or more intervals (values that are infinite and uncountable) are called continuous. examples tend to be measurements (amount of sales or time between arrivals)

Question 4

probability distribution of a discrete random variable

Answer 4

a discrete random variable X has a countable number of possible values. The probability distribution of X lists the values and their probabilities
the probability of a discrete random variable is a graph, table, or formula that specifies the probability associated with each possible value that the random variable can assume

Question 5

what two requirements must a probability satisfy in a probability distribution of a discrete random variable

Answer 5

every probability P(xi) is a number between 0 and 1, inclusive and the sum is equal to 1

Question 6

mean or expected value of a discrete random variable

Answer 6

mu=E(x)= sum of xi times p(xi)

Question 7

variance

Answer 7

population variance sigma squared is defined as the average of the squared distanced of x from the population mean. Since x is a random variable, (x-mu)^2 is also a random variable
variance= sum of x^2p(x)-mean^2

Question 8

standard deviation

Answer 8

square root of variance

Question 9

probability statements also can be made according to the empirical rule…

Answer 9

.68, .95, 1.00