# Chapter 6 Lessons 1 and 2

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Graphs of Normal Probability Distributions Lesson 1

This distribution was studied by the French mathematician Abraham de Moivre (1667–1754) and later by the German mathematician Carl Friedrich Gauss (1777–1855), whose work is so important that the normal distribution is sometimes called *Gaussian*.

Application

Applications of a normal probability distribution are so numerous that some mathematicians refer to it as “a veritable Boy Scout knife of statistics.”

A rather complicated formula, presented later in this section, defines a normal distribution in terms of *m* and *s*, the mean and standard deviation of the population distribution.

Bell Shape Curve

We see that a general normal curve is smooth and symmetrical about the vertical line extending upward from the mean *m*.

Notice that the highest point of the curve occurs over *m*. If the distribution were graphed on a piece of sheet metal, cut out, and placed on a knife edge, the balance point would be at *m*.

We also see that the curve tends to level out and approach the horizontal (*x *axis) like a glider making a landing.

However, in mathematical theory, such a glider would never quite finish its landing because a normal curve never touches the horizontal axis.

The parameter *s* controls the spread of the curve. The curve is quite close to the horizontal axis at *m* + 3*s* and *m* – 3*s*.

Thus, if the standard deviation *s* is large, the curve will be more spread out; if it is small, the curve will be more peaked.

Math is Cool

Very Easy Once Learned

Then it begins to cup upward as we go to the lower part of the bell.

The exact places where the *transition *between the upward and downward cupping occur are above the points *m* + *s* and *m* – s.

In the terminology of calculus, transition points such as these are called *inflection points*.

The parameters that control the shape of a normal curve are the mean *m* and the standard deviation *s*. When both *m* and *s* are specified, a specific normal curve is determined. In brief, *m*locates the balance point and *s* determines the extent of the spread.

Empirical Rule

The preceding statement is called the *empirical rule *because, for symmetrical, bell-shaped distributions, the given percentages are observed in practice.

distribution, the empirical

rule is a direct consequence

of the very nature of the

distribution

Empirical Rule

Notice that the empirical rule is a stronger statement than Chebyshev’s theorem in that it gives*definite percentages*,* *not just lower limits.

Of course, the empirical rule applies only to normal or symmetrical, bell-shaped distributions, whereas Chebyshev’s theorem applies to all distributions.

The playing life of a Sunshine radio is normally distributed with mean *m* = 600 hours and standard deviation *s* = 100 hours.

What is the probability that a radio selected at random will last from 600 to 700 hours?

The probability that the playing life will be between 600 and 700 hours is equal to the percentage of the total area under the curve that is shaded in Figure 6-4.

Solution to the Problem

Since *m* = 600 and *m* + *s* = 600 + 100 = 700, we see that the shaded area is simply the area between *m* and *m* + *s*.

The area from *m* to *m* + *s* is 34% of the total area.

This tells us that the probability a Sunshine radio will last between 600 and 700 playing hours is about 0.34.

Control Charts Lesson 2

If we are examining data over a period of equally spaced time intervals or in some sequential order, then *control charts *are especially useful.

Business managers and people in charge of production processes are aware that there exists an inherent amount of variability in any sequential set of data.

The sugar content of bottled drinks taken sequentially off a production line, the extent of clerical errors in a bank from day to day, advertising expenses from month to month, or even the number of new customers from year to year are examples of sequential data.

There is a certain amount of variability in each.

A random variable *x *is said to be in *statistical control *if it can be described by the *same*probability distribution when it is observed at successive points in time.

Control charts combine graphic and numerical descriptions of data with probability distributions.

Control charts were invented in the 1920s by Walter Shewhart at Bell Telephone Laboratories.

Since a control chart is a *warning device*,* *it is not absolutely necessary that our assumptions and probability calculations be precisely correct.

For example, the *x *distributions need not follow a normal distribution exactly. Any moundshaped and more or less symmetrical distribution will be good enough.

Control Chart Example

Susan Tamara is director of personnel at the Antlers Lodge in Denali National Park, Alaska.

Every summer Ms. Tamara hires many part-time employees from all over the United States. Most are college students seeking summer employment.

One of the biggest activities for the lodge staff is that of “making up” the rooms each day. Although the rooms are supposed to be ready by 3:30 P.M., there are always some rooms not made up by this time because of high personnel turnover.

Every 15 days Ms. Tamara has a general staff meeting at which she shows a control chart of the number of rooms not made up by 3:30 P.M. each day.

From extensive experience, Ms. Tamara is aware that the distribution of rooms not made up by 3:30 P.M. is approximately normal, with mean *m* = 19.3 rooms and standard deviation *s* = 4.7 rooms.

This distribution of *x *values is acceptable to the top administration of Antlers Lodge.

Solution

A control chart for a variable *x *is a plot of the observed *x *values (vertical scale) in time sequence order (the horizontal scale represents time).

Place horizontal lines at

the mean *m* = 19.3

the control limits *m* ± 2*s* = 19.3 ± 2(4.7), or 9.90 and 28.70

the control limits *m* ± 3*s* = 19.3 ± 3(4.7), or 5.20 and 33.40A control chart for a variable *x *is a plot of the observed *x *values (vertical scale) in time sequence order (the horizontal scale represents time).

Place horizontal lines at

the mean *m* = 19.3

the control limits *m* ± 2*s* = 19.3 ± 2(4.7), or 9.90 and 28.70

the control limits *m* ± 3*s* = 19.3 ± 3(4.7), or 5.20 and 33.40

Solution

Once we have made a control chart, the main question is the following: As time goes on, is the *x*variable continuing in this same distribution, or is the distribution of *x *values changing?

If the *x *distribution is continuing in more or less the same manner, we say it is *in statistical control*. If it is not, we say it is *out of control*.

Many popular methods can set off a warning signal that a process is out of control.

Remember, a random variable *x *is said to be *out of control *if successive time measurements of *x*indicate that it is no longer following the target probability distribution.

We will assume that the target distribution is (approximately) normal and has (user-set) target values for *m* and *s*.

Three of the most popular warning signals are described next.

Solution

Remember, a control chart is only a warning device, and it is possible to get a false alarm.

A false alarm happens when one (or more) of the out-of-control signals occurs, but the *x*distribution is really on the target or assigned distribution. In this case, we simply have a rare event (probability of 0.003 or 0.004).

In practice, whenever a control chart indicates that a process is out of control, it is usually a good precaution to examine what is going on. If the process is out of control, corrective steps can be taken before things get a lot worse.

Solution

From an intuitive point of view,

signal I can be thought of as a blowup, something dramatically out of control.

Signal II can be thought of as a slow drift out of control.

Signal III is somewhere between a blowup and a slow drift.