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WARM – UP
#1. A company produces ceramic floor tiles which are
supposed to have a surface area of 16 square inches.
Due to variability in the manufacturing process, the actual
surface area of all tiles has a normal distribution with
mean 16.1 square inches and standard deviation 0.2
square inches. What is the proportion of tiles produced
by the process with surface area less than 16.0 square
inches?
16 16.1
P(x < 16) = ?
P z
0.2
P(z < - 0.5) = Normalcdf(-E99, -0.5) = 0.3085
1
#2. The weight of a randomly selected can of a new soft
drink is known to have a normal distribution with mean
8.3 ounces and standard deviation 0.2 ounces. What
weight (in ounces) should be stamped on the can so that
only 2% of the cans are underweight?
? 8.3
2.05
P(x < ?) = .02
0.2
z = InvNorm(.02) = -2.05
? = 7.89
1
RANDOM VARIABLES
A Random Variable is a variable whose value is a numerical
outcome of a random phenomenon. Usually denoted by X.
A Discrete Random Variable, X, has a finite or countable
number of possible values. The Probability Distribution of
X lists the values and their respective probabilities.
The following probability distribution represent the
AP Statistics scores from previous years.
AP Score
1
2
3
4
5
Probability 0.14 0.24 0.34 0.21 0.07
EXAMPLE:
1. All probabilities are: 0 ≤ p(x) ≤ 1
2. The sum of all probabilities equals one: Σ pi(x) = 1
A Continuous Random Variable, X, takes on all the infinite
numerical values within an interval. The Probability
Distribution of X is described by a Density curve. The
probability of any event is the area under the density curve.
UNIFORM Density Curve
Let X represent any random number between 0 and 5.
Find the following Probabilities:
EXAMPLE #1:
?
A= B·h
1 = 5·h
0
1
2
3
4
5
1. What is the Probability of any one number occurring?
0.24
P( 0.4 ≤ X ≤ 3.5) = 0.62
P(X > 2.2) = P(X ≥ 2.2) = 0.56
2. P( 0 ≤ X ≤ 1.2) =
3.
4.
1/5 = 0.2
NORMAL Density Curve
Let X represent any random number from the normal
distribution with mean 2.5 and standard deviation 0.8.
x
z
EXAMPLE #2:
1
.1
.9
0.0512
P( 0.4 ≤ X ≤ 3.5) = 0.8900
P(X > 2.2) = P(X ≥ 2.2) = 0.6462
1. P( 0 ≤ X ≤ 1.2) =
2.
3.
1.7 2.5 3.3 4.1 4.9
xU 2.5
xL 2.5
z
.8
.8
1
1
Normalcdf (zL, zU)
OR
Normalcdf (xL, xU, μ, σ)