Business Cost Statistics

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					Business Statistics (BUSA 3101)
      Dr. Lari H. Arjomand

              Text Book
Statistical Techniques in Business &
     Lind, Marchal, and Wathen
             14th Ed. 2010

                                  Slide 1
               Business Statistics

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                                         Slide 2
                        Chapter 1
                    What is Statistics
                                             I need
   Applications in Business and Economics    help!
   Data
   Data Sources
   Descriptive Statistics
   Statistical Inference
   Computers and
     Statistical Analysis

                                                      Slide 3
             Application Areas of Statistics

         Accounting                     Management
•   Auditing                  •   Describe employees
•   Costing                   •   Quality improvement

          Finance                       Marketing
•   Financial trends          •   Consumer preferences
•   Forecasting               •   Marketing mix effects

                                                          Slide 4
                   Applications in
               Business and Economics
   Accounting
    Public accounting firms use statistical
    sampling procedures when conducting
    audits for their clients.

   Economics
    Economists use statistical information
    in making forecasts about the future of
    the economy or some aspect of it.

                                              Slide 5
                   Applications in
               Business and Economics
   Marketing
    Electronic point-of-sale scanners at
    retail checkout counters are used to
    collect data for a variety of marketing
    research applications.

   Production
    A variety of statistical quality
    control charts are used to monitor
    the output of a production process.

                                              Slide 6
                 Applications in
             Business and Economics
 Finance
  Financial advisors use price-earnings ratios and
  dividend yields to guide their investment

                                                     Slide 7
                  Why Collect Data?

   Obtain input to a research study
   Measure performance
   Assist in formulating decision alternatives
   Satisfy curiosity
     • Knowledge for the sake of knowledge

                                                  Slide 8
                  Data and Data Sets

   Data are the facts and figures collected, summarized,
    analyzed, and interpreted.

 The data collected in a particular study are referred
  to as the data set.

                                                      Slide 9
      Elements, Variables, and Observations

 The elements are the entities on which data are
 A variable is a characteristic of interest for the elements.
 The set of measurements collected for a particular
  element is called an observation.
 The total number of data values in a data set is the
  number of elements multiplied by the number of

                                                         Slide 10
                Data, Data Sets,
     Elements, Variables, and Observations
 Names              Stock   Annual    Earn/
     Company      Exchange Sales($M) Share($)

   Dataram        AMEX        73.10        0.86
   EnergySouth     OTC        74.00        1.67
   Keystone       NYSE       365.70        0.86
   LandCare       NYSE       111.40        0.33
   Psychemedics   AMEX        17.60        0.13

                                Data Set

                                                  Slide 11
           Scales of Measurement

Scales of measurement include:
             Nominal        Interval
             Ordinal         Ratio

The scale determines the amount of information
contained in the data.

The scale indicates the data summarization and
statistical analyses that are most appropriate.

                                                  Slide 12
               Scales of Measurement

   Nominal
    Data are labels or names used to identify an
    attribute of the element.

    A nonnumeric label or numeric code may be used.

                                                   Slide 13
              Scales of Measurement

   Nominal

      Students of a university are classified by the
      school in which they are enrolled using a
      nonnumeric label such as Business, Humanities,
      Education, and so on.
      Alternatively, a numeric code could be used for
      the school variable (e.g. 1 denotes Business,
      2 denotes Humanities, 3 denotes Education, and
      so on).

                                                    Slide 14
              Scales of Measurement

   Ordinal
    The data have the properties of nominal data and
    the order or rank of the data is meaningful.

    A nonnumeric label or numeric code may be used.

                                                       Slide 15
                Scales of Measurement

   Ordinal
       Students of a university are classified by their
       class standing using a nonnumeric label such as
       Freshman, Sophomore, Junior, or Senior.
       Alternatively, a numeric code could be used for
       the class standing variable (e.g. 1 denotes
       Freshman, 2 denotes Sophomore, and so on).

                                                      Slide 16
               Scales of Measurement

   Interval
    The data have the properties of ordinal data, and
    the interval between observations is expressed in
    terms of a fixed unit of measure.

    Interval data are always numeric.

                                                        Slide 17
               Scales of Measurement

   Interval
      Melissa has an SAT score of 1205, while Kevin
      has an SAT score of 1090. Melissa scored 115
      points more than Kevin.

                                                      Slide 18
               Scales of Measurement

   Ratio
    The data have all the properties of interval data
    and the ratio of two values is meaningful.

    Variables such as distance, height, weight, and time
    use the ratio scale.

    This scale must contain a zero value that indicates
    that nothing exists for the variable at the zero point.

                                                        Slide 19
               Scales of Measurement

   Ratio
      Melissa’s college record shows 36 credit hours
      earned, while Kevin’s record shows 72 credit
      hours earned. Kevin has twice as many credit
      hours earned as Melissa.

                                                       Slide 20
                 Types of Data


           Numerical             Categorical
       (Quantitative)            (Qualitative)

Discrete           Continuous

                                                 Slide 21
    Qualitative and Quantitative Data

Data can be further classified as being qualitative
or quantitative.

The statistical analysis that is appropriate depends
on whether the data for the variable are qualitative
or quantitative.

In general, there are more alternatives for statistical
analysis when the data are quantitative.

                                                      Slide 22
                 Qualitative Data

Labels or names used to identify an attribute of each

Often referred to as categorical data

Use either the nominal or ordinal scale of

Can be either numeric or nonnumeric

Appropriate statistical analyses are rather limited

                                                      Slide 23
                Quantitative Data

Quantitative data indicate how many or how much:

    discrete, if measuring how many

    continuous, if measuring how much

Quantitative data are always numeric.

Ordinary arithmetic operations are meaningful for
quantitative data.

                                                    Slide 24
                Scales of Measurement


               Qualitative                Quantitative

   Numerical            Non-numerical       Numerical

Nominal   Ordinal     Nominal   Ordinal   Interval   Ratio

                                                     Slide 25
               Cross-Sectional Data

Cross-sectional data are collected at the same or
approximately the same point in time.

 Example: data detailing the number of building
 permits issued in June 2003 in each of the counties
 of Ohio

                                                       Slide 26
                 Time Series Data

Time series data are collected over several time

 Example: data detailing the number of building
 permits issued in Lucas County, Ohio in each of
 the last 36 months

                                                   Slide 27
                 Data Sources

             Primary                   Secondary

Experiment   Survey      Observation    Published
                                       (& On-Line)

                                               Slide 28
                       Data Sources

   Existing Sources

    Within a firm – almost any department
    Business database services – Dow Jones & Co.
    Government agencies - U.S. Department of Labor
    Industry associations – Travel Industry Association
                            of America
    Special-interest organizations – Graduate Management
                                     Admission Council
    Internet – more and more firms

                                                      Slide 29
               Data Sources (Continued)

   Statistical Studies

     In experimental studies the variables of interest
     are first identified. Then one or more factors are
     controlled so that data can be obtained about how
     the factors influence the variables.

     In observational (non-experimental) studies no
     attempt is made to control or influence the
     variables of interest.
                                    a survey is a
                                  good example

                                                      Slide 30
       Data Acquisition Considerations

Time Requirement
•   Searching for information can be time consuming.
•   Information may no longer be useful by the time it
       is available.
Cost of Acquisition
• Organizations often charge for information even
    when it is not their primary business activity.
Data Errors
•   Using any data that happens to be available or
    that were acquired with little care can lead to poor
    and misleading information.

                                                      Slide 31
                      What Is Statistics?
   Collecting data
     • e.g., Survey
   Presenting data                              Why?
     • e.g., Charts & tables        Data
   Characterizing data            Analysis
     • e.g., Average


                                                 Slide 32
         Statistical Methods


Descriptive                    Inferential
 Statistics                     Statistics

                                             Slide 33
                  Descriptive Statistics

   Descriptive statistics are the tabular, graphical, and
    numerical methods used to summarize data.

             Descriptive Statistics:
              These are statistical
               methods used to
               describe data that
             have been collected.

                                                         Slide 34
       Example: Hudson Auto Repair

   The manager of Hudson Auto
would like to have a better
understanding of the cost
of parts used in the engine
tune-ups performed in the
shop. She examines 50
customer invoices for tune-ups. The costs of parts,
rounded to the nearest dollar, are listed on the next

                                                    Slide 35
           Example: Hudson Auto Repair

   Sample of Parts Cost for 50 Tune-ups

     91     78   93   57    75   52    99   80   97   62
     71     69   72   89    66   75    79   75   72   76
     104    74   62   68    97   105   77   65   80   109
     85     97   88   68    83   68    71   69   67   74
     62     82   98   101   79   105   79   69   62   73

                                                            Slide 36
      Tabular Summary:
Frequency and Percent Frequency

  Parts       Parts      Percent
 Cost ($)   Frequency   Frequency
  50-59          2           4
  60-69         13          26
  70-79         16          32
  80-89          7          14
  90-99          7          14
 100-109         5          10
                50         100

                                       Slide 37
                 Graphical Summary: Histogram

                          Tune-up Parts Cost

                   50-59 60-69 70-79 80-89 90-99 100-110 Cost ($)

                                                                    Slide 38
         Numerical Descriptive Statistics

 The most common numerical descriptive statistic
  is the average (or mean).
 Hudson’s average cost of parts, based on the 50
  tune-ups studied, is $79 (found by summing the
  50 cost values and then dividing by 50).

                                                    Slide 39
                    Inferential Statistics
   Involves
     • Estimation
     • Hypothesis                            Population?
   Purpose
     • Make decisions about
       population characteristics

Inferential Statistics: These are
statistical methods used to find out
something about population based
on a sample.
                                                    Slide 40
               Statistical Inference

Population   - the set of all elements of interest in a
               particular study
Sample - a subset of the population

Statistical inference - the process of using data obtained
                        from a sample to make estimates
                        and test hypotheses about the
                        characteristics of a population
Census - collecting data for a population

Sample survey - collecting data for a sample

                                                          Slide 41
     Process of Statistical Inference

    1. Population
    consists of all        2. A sample of 50
 tune-ups. Average         engine tune-ups
   cost of parts is          is examined.

4. The sample average     3. The sample data
                           provide a sample
is used to estimate the   average parts cost
  population average.     of $79 per tune-up.

                                                Slide 42
    Statistical Analysis Using Microsoft Excel

 Statistical analysis typically involves working with
  large amounts of data.
 Computer software is typically used to conduct the
 Frequently the data that is to be analyzed resides in a
 Modern spreadsheet packages are capable of data
  management, analysis, and presentation.
 MS Excel is the most widely available spreadsheet
  software in business organizations.

                                                     Slide 43
     Statistical Analysis Using Microsoft Excel
   3 tasks might be needed:                              A
    • Enter Data                                     1   Cost
    • Enter Functions and Formulas                   2      91
                                                     3      71
    • Apply Tools                                    4     104
                                                     5      85
                                                     6      62
                                                     7      78
                                                     8      69

                                   D                E
                                  Mean   =AVERAGE(A2:A71)
                                Median   =MEDIAN(A2:A71)
                                  Mode   =MODE(A2:A71)
                                 Range   =MAX(A2:A71)-MIN(A2:A71)

                                                         Slide 44
Statistical Analysis Using Microsoft Excel

   Excel Worksheet (showing data)
               A               B          C            D
                                         Parts       Labor
1         Customer         Invoice #    Cost ($)    Cost ($)
2   Sam Abrams                  20994          91         185
3   Mary Gagnon                 21003          71         205
4   Ted Dunn                    21010         104         192
5   ABC Appliances              21094          85         178
6   Harry Morgan                21116          62         242
7   Sara Morehead               21155          78         148
8   Vista Travel, Inc.          21172          69         165
9   John W illiams              21198          74         190

     Note: Rows 10-51 are not shown.

                                                           Slide 45
Statistical Analysis Using Microsoft Excel

   Excel Formula Worksheet
      C         D     E           F                   G
     Parts    Labor
1   Cost ($) Cost ($)
2         91      185     Average Parts Cost =AVERAGE(C2:C51)
3         71      205
4       104       192
5         85      178
6         62      242
7         78      148
8         69      165
9         74      190
    Note: Columns A-B and rows 10-51 are not shown.

                                                          Slide 46
Statistical Analysis Using Microsoft Excel

   Excel Value Worksheet
      C         D     E           F                   G
     Parts    Labor
1   Cost ($) Cost ($)
2         91      185     Average Parts Cost          79
3         71      205
4       104       192
5         85      178
6         62      242
7         78      148
8         69      165
9         74      190
    Note: Columns A-B and rows 10-51 are not shown.

                                                           Slide 47
End of Chapter 1

                   Slide 48
                Slide 48

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