CHAPTER 6: NORMAL PROBABILITY DISTRIBUTIONS
(CONTINUOUS VARIABLES)
Def: If a continuous random variable has a distribution with a graph t
symmetric and bell-shaped, and it can be described by the equation
1 x
(
)
, we say that it has a normal distributi

Sec 6.3
Applications of Normal Distributions
When working with a normal distribution that is nonstandard
( with a mean different from 0 and/or a standard deviation different fr
we use the following formula to transform a value x to a z score,
then proceed

CHAPTER 5
Sec 5.2
Discrete Probability Distribution
Probability Distribution
In this chapter we create a table describing what we expect to happen
such as the population parameters mean and the population standa
Def: A random variable is a variable, x, th

Sec 5.4
Parameters for Binomial Distributions
Mean:
np
Variance:
2 npq
Std. dev:
npq
q=1-p
Requirements:
1. The procedure has a fixed number of trials.
2. The trials must be independent.
3. Each trial must have outcomes classified into two categories:

Sec 6.5
The Central Limit Theorem (CLT)
For all samples of the same size n with n > 30, the sample distributio
can be approximated by a normal distribution with mean and a
/ .
standard deviation of
According to the CLT, the original population can have an

Sec 5.3 Binomial Probability Distribution
Def: A binomial probability distribution results from a procedure
that meets all the following requirements:
1. The procedure has a fixed number of trials.
2. The trials must be independent.
3. Each trial must hav

SEC. 4.3
Addition Rule
One selection (one trial)
P(A or B)= P(A) + P(B) -P(A and B)
Def: Events A and B are disjoint ( or Mutually exclusive) if they can no
at the same time. ( That is, disjoint events do not overlap)
Complementary Events and the addition