Section 2 Variation Fundamentals
Lesson S02-02
Text: Section 2 pages 4 – 9
Duration: 22 minutes
In order to understand whether the information is varying in a predicted way or an unusual way, we must first understand the expected variation in the system. Once we understand the degree of variability that is expected, we can identify whether a data value is beyond that expected amount.
The idea in SPC is to view a stable process sufficiently long so that the typical variation is understood. With that information, limits of expected variation can be computed.
Statisticians model or describe data using several types of distributions. Most people are aware of the “Normal” or “bell-shaped” distribution, but there are many other common distributions such as the F, t, Chi-Squared, Exponential, Lognormal, Weibull, and so on.
Data may stack up to form a variety of shapes (patterns). A commonly known distribution is called the Normal distribution, and one of its characteristics is its bell shape.
Drive Times and The Normal Distribution
What do μ (mu) and σ (sigma) represent?
A. They are the mean and variance of any distribution.
B. They are parameters for the exponential distribution.
C. They are the mean and standard deviation of a normal distribution.
D. They are given values for stat class problems only.
AnswerC. They are commonly used to represent the mean and standard deviation of a normal distribution.
View next video for definitions and discussion.
Normal Probability Density Function Equation
What is the equation of a line on an X Y plane?
A. y = mx^2 – b
B. y = mx + b
C. y + x = 1
D. y = ax^2 + bx + c
AnswerB. The equation of a line on the X Y plane is y = mx + b where m is the slope and b is the Y-intercept.
View next video for equation answer and discussion.
Limits of Expect Value
An Example Using the Normal Curve
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