Types of Data

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Types of Data
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Introduction

• Control charts give us a picture of our process
  over time. This picture tells us when to leave
  our process alone (i.e., the process is in
  control) or when to look for a problem (i.e., an
  assignable cause is present).
• There are many different types of control charts.
  However, you can group control charts into two
  major categories. The type of data being charted
  distinguishes these two categories. There are
  two types of data you can have attributes data
  and variables data.
• Both these types of data are introduced in this
  module. With attributes data, there is a need to
  develop specific descriptions. These
  descriptions, which are called operational
  definitions, are also introduced in this module.
  
• For variables data, the standard deviation is an
  important measurement as well as the average.
  Both of these terms are explored in more detail
  below.

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Objectives

• In this module you will learn
• What attributes data are.
• What an operational definition is.
• What variables data are.
• What the average and standard deviation are.
• It is important to know what type of data you
  will collect so you can determine what type of
  control chart to construct. Different charts
  will give different information. Attributes
  charts include p, np, c and u charts. Variables
  charts include Xbar-R charts, Xbar-s charts,
  individuals charts and moving average and moving
  range charts.

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Attributes Data

• Attributes control charts are based on attributes
  data. These types of data are often referred to
  as discrete data. There are two kinds of
  attributes data yes/no type of data and
  counting data. p and np control charts are used
  with yes/no type data c and u charts are used
  with counting type data. The two types of
  attributes data are described below.

Yes/No Data
Counting Data
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Yes/No Data

• For one item, there are only two possible
  outcomes either it passes or it fails some
  preset specification. Each item inspected is
  either defective (i.e., it does not meet the
  specifications) or is not defective (i.e., it
  meets specifications). Examples of the yes/no
  attributes data are
• mail delivery is it on time or not on time?
• phone answered is it answered or not answered?
• invoice correct is it correct or not correct?
• stock item is it in stock or not in stock?
• cycle count is it correct or not correct?
• product in-spec or out of spec?
• supplier material received on-time or not
  on-time?

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Counting Data

• With counting data, you count the number of
  defects. A defect occurs when something does not
  meet a preset specification. It does not mean
  that the item itself is defective. For example,
  a television set can have a scratched cabinet (a
  defect) but still work properly. When looking at
  counting data, you end up with whole numbers such
  as 0, 1, 2, 3 you can't have half of a defect.
• To be considered counting data, the opportunity
  for defects to occur must be large the actual
  number that occurs must be small. For example,
  the opportunity for customer complaints to occur
  is large. However, the number that actually
  occurs is small. Thus, the number of customer
  complaints is an example of counting type data.
  Other examples are
• number of mistakes in picking
• number of items shipped incorrectly
• number of accidents for delivery trucks

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Exercise

• For your organization, what are some examples of
  yes/no type data and counting type data. List
  your responses below.
• Yes/No
• Counting

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Operational Definitions

• When working with attributes data, you have to
  have a clear understanding of whether the item
  you are looking at is defective or not (yes/no
  type data) or whether it should be counted as a
  defect (counting type data). In order to know
  whether a shipment was on time or to count the
  number of on-time shipments, you have to have a
  definition of what "on time" means. Is "on time"
  anywhere from 155 p.m. to 205 p.m., anytime
  before 200 p.m., or anytime between 200 p.m.
  and 215 p.m.? This clear understanding of a
  quality expectation is called an operational
  definition.

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Operational Definition

• According to Dr. W. Edwards Deming, an
  operational definition includes
• a written statement (and/or a series of examples)
  of criteria or guidelines to be applied to an
  object or to a group.
• a test of the object or group for conformance
  with the guidelines that includes specifics such
  as how to sample, how to test, and how to
  measure.
• a decision yes, the object or the group did
  meet the guidelines no, the object or group
  did not meet the guidelines or the number of
  times the object or group did not meet the
  guidelines.

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Operational Definitions

• Using an invoice error example, the written
  statement may read "An invoice error is an
  incorrect shipping amount or a wrong price." The
  test could be to
• compare every invoice to the packing list to
  check for incorrect shipping amounts and,
• compare every invoice to a price schedule to
  check for wrong prices.
• Based on these guidelines and a test for
  conformance with these guidelines, you could make
  a decision as to whether an invoice is defective
  or how many defects an invoice contains.

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Exercise

• Select one of the variables below. Develop an
  operational definition for the variable.
• On-Time Delivery
• Rework in a Department
• Injury at Work
• Customer Complaint
• Invoice Error

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Variables Data

• Variables control charts are based on variables
  data. Variables data consist of observations
  made from a continuum.
• That is, the observation can be measured to any
  decimal place you want if your measurement system
  allows it.
• Some examples of variables data are contact time
  with a customer, sales dollars, amount of time to
  make a delivery, height, weight, and costs.

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Exercise  

• For your organization, what are some examples of
  variables data? Record your answers below.

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Average and Standard Deviation

• In dealing with variables data, the average and
  standard deviation are very important parameters.
  One must understand what is meant by these
  terms.  
• The average (also called the mean) is probably
  well understood by most. It represents a
  "typical" value. For example, the average
  temperature for the day based on the past is
  often given on weather reports. It represents a
  typical temperature for the time of year.
• The average is calculated by adding up the
  results you have and dividing by the number of
  results. For example, suppose the last five
  customer complaints took 5, 6, 2, 3, and 8 days
  to close. The average is determined by adding up
  these five numbers and dividing by 5. The
  average is denoted by and in this case is

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Average and Standard Deviation

• While the average is understood by most, few
  understand the standard deviation, denoted by the
  letter s.
• The standard deviation can be thought of as an
  average distance (the standard) that each
  individual point is away from the mean.
• The equation for the standard deviation is given
  below.
• We will be using control charts to estimate what
  our process average is and what the process
  standard deviation is. For these two numbers to
  have any meaning, the process must be in
  statistical control.

Standard Deviation
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Summary

• Control charts can be divided into two major
  categories attribute control charts and
  variable control charts.
• Attribute control charts are based on attribute
  data. There are two types of attributes data
  yes/no type and counting data. Yes/no type
  attributes data have only two possible outcomes
  either the item is defective or it is not
  defective. With counting type attributes data,
  the number of defects is counted. With
  attributes data, there is the need for
  operational definitions. Operational definitions
  are used to determine what constitutes a
  defective item or a defect.
• Variable control charts are based on variables
  data. Variables data are data from a continuum.
  The basic probability distribution underlying the
  calculation of control limits for variables data
  is the normal distribution.