Learning objectives

1
Learning objectives

• Be better able to present tables and graphs so
  that the information contained in them is
  immediately apparent to users
• Be able to tackle the problem of analysing data
  systematically

2
Data Communication

• The transmission of information through the
  medium of numbers
• Usually in the form of tables, graphs or other
  pictorial methods
• A manager usually encounters data in business
  reports, management information systems,
  accounting data, self-generated data
• Objective is to ensure that main patterns and
  exceptions in the data are immediately evident

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Rules For Data Presentation

• Round the numbers to 2 effective figures
• Reorder the numbers
• Interchange rows and columns (when desirable)
• Use summary measures (averages but not totals)
• Minimise the use of space, and lines
• Clear (but unobtrusive) labelling
• Verbal summary (of main patterns)
• (Rules1, 5, 6, 7 are applicable to accounting
  data)

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Rule 1 Round the numbers to 2 effective figures

• E.g. 34.8 ? 18.3 35 ? 18 ? 2
• E.g. 1382 1400 721 720
  79.311 79 17.1 17
  4.2 4.2 2.32 2.3
• E.g. 11242 11240 11327 11330 11231 11230

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Rule 2 Reorder the numbers

• Arrange in size order, so that patterns are
• more easily seen
• One of the columns or rows of the table
• The averages of the columns or rows
• Something external to the table, e.g.
  geographically
• NOTE Alphabetic order may be useful for detail
• information, but not for management information.

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Rule 3 Interchange Rows and Columns

• Important comparisons should be down
• columns not along rows. This makes it easier
• to compare numbers and detect trends.
• Example 570 430 99 260 570 430
  99
• 260

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Rule 4 Use Summary Measures

• Averages for rows and columns
• This makes it easier to compare rows (columns)
  and to spot quickly figures within rows
  (columns), which are above or below average.
• A total is not a summary measure, as it is of a
  different order of magnitude than the individual
  numbers.

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Rule 5 Minimise the use of space and lines

• Numbers should be close together for ease of
  comparison, gaps interfere with the ability to
  see trends.
• Grid lines should just be used to separate one
  type of number from another, e.g. summary row
  from the rest.

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Rule 6 Labelling should be clear but unobtrusive

• E.g. Avoid abbreviations and obscure labels
  which may be unfamiliar to the reader.Rule 7
  Use a verbal summary
• This helps direct attention to the main features
  of the data

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Accounting Data - Presentation

• They have a standard logical sequence
• There is a convention and tradition
• Apply Rules 1, 5, 6 7 only
• Summarise as far as possible
• The best solution is often to have 2 sets of
  accounts
• Source material for legal requirements and
  financial experts
• Communicating document to provide financial
  information to shareholders and non-accountants.

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Data Presentation-Graphs

• Graphs are good when
• Attracting attention and adding variety to a long
  list of tables communicating very simple
  patterns
• Graphs are not good when
• Communicating even slightly complex patterns
  being used for reference purposes

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Pie Charts

• Sales by Dept and Year (m)
• 1995 1996 1997
• Clothing 1.7 1.4 1.4
• Furniture 3.4 4.9 5.6
• Electrical 0.2 0.4 0.5
• Total 5.3 6.7 7.5
• relative size
• categorical data

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Bar Chart
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Histograms

• For grouped frequency tables
• The horizontal axis is a continuous scale
• It is the area of the bars that is being compared
• Time Frequency Time Frequency
• 40-45 4 55-60 44
• 45-50 13 60-65 59
• 50-55 17 65-70 7

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Ogive - Cumulative frequencies

• Cum.
• Time Freq. Freq.
• 40-45 4 4
• 45-50 13 17
• 50-55 17 34
• 55-60 44 78
• 60-65 59 137
• 65-70 7 144

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Data Analysis

• Finding out what real information, if any, a set
  of numbers contains
• Management problems with data analysis
• The statistical gap
• Lack of confidence
• Over-complication by the experts

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Guidelines For Data Analysis

• Reduce The Data Re-present The Data
• Build A Model- Find a pattern that can be
  expressed simply, E.g.
• Row averages are all equal, but with ? 10
  variation within each row
• Column 1 (y) is related to column 2 (x) by the
  equation y 2.71x 3.34
• Exceptions
• Comparisons

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Alcohol Consumption across U.S.A.

• Objective of Analysis Measure the variation in
  consumption across the USA and detect any areas
  where there are distinct differences.
• Stage 1 Reduce the data. E.g. Why population,
  total consumption and per capita figures?
• Stage 2 Re-present the data
• Rule 1. Reduce to 2 effective figures. Accuracy
  to 8 figures is spurious considering the way
  figures like these are collected.

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• Rule 2. Reorder states in decreasing population
  order, enabling consumption which is out of line
  with a states population to be easily spotted.
• Rule 3. Interchange rows and columns. - not
  necessary here.
• Rule 4. Use summary measures. With reduction of
  the data this is not necessary here either.
• Rule 5. Minimise use of space and lines - divide
  into groups of 5.
• Rule 6. Labelling clear but unobtrusive.
• Rule 7. Verbal summary.

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• Stage 3. Build a model.E.g. Per capita
  consumption is about equal to the average for all
  licensed states (2.1 wine galls. per year) ?
  30. The pattern one year earlier is much the
  same, except that overall consumption has
  increased by about 0.6 during this time.
• Stage 4. Exceptions
• District of Columbia(6.5), Nevada (7.2), and
  Alaska (3.6).

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• Stage 5. Comparison
• A comparison between 1975 and 1976 is provided.
  Other useful data would be
• Earlier years e.g. 1965, 1970.
• Breakdown into consumption by different types of
  beverage
• Changes of population in the various states over
  the same time