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In business, you will often have to gather information for the purposes of project planning or perhaps research. Once you have analyzed the information, you need to be able to summarize the results in an effective way in order to write reports or make presentations at meetings or seminars. Here, we will explain some of the methods of summarizing and presenting raw data. These methods are collectively known as ‘descriptive statistics’.
Key points to consider in descriptive statistics:
- have the most important points been covered?
- what is the purpose of the presentation?
- who is the audience?
- how much detail is needed?
- have you used the most appropriate method of presentation?
- have you included a title and explanation of the data source?
Methods of Summarizing and Presenting Data
Once you have considered what you want to achieve with your data, you should decide on the best way to present the information. There are a number of options available to you:
1. Tables and Frequency Distributions
Tables are used to present large amounts of numerical data in a logical order and in summary form. A well constructed table should help readers quickly access the information they need.
You may need to group or classify the ‘raw’ data to make it easier for the reader to find the information required. You can do this by using class intervals, which denote a range of figures under which the discrete variables (single units of measurement) will fall. Grouping the discrete variables in such a way ensures clarity.
For example:
- 100-199
- 200-299
- 300-399
Each discrete variable falls into a defined class, so in this case, variable 299 is in the second class.
Using a Table to Present a Frequency Distribution
Frequency distributions are the frequency with which particular variables occur in a set of data. These can be presented in tabulated form, e.g.:
The frequency distribution of salary bandings in the Caledonian Council. Total number of employees = 1,000.
Annual salary (to nearest £1,000)
Number of employees
15,000 - 19,000
140
20,000 - 24,000
620
25,000 - 29,000
150
30,000 - 34,000
40
35,000 - 39,000
35
40,000 - 44,000
15
From this information, and without the need to calculate anything, it can be deduced that the majority of employees fall into the £20-24,000 salary band.
2. Bar Charts
Bar charts are a popular way of displaying comparative information and are a particularly good way of presenting changes in quantitative information over a period of time. They provide an effective means of presenting visual information for a report or meeting
Example
The marketing manager of the Thrifty Friendly Society has a presentation to make to her directors about product sales in the last quarter.
She has the following information on the monthly sales values:
- April - £450,000
- May - £1,150,000
- June - £1,850,000
Presenting the data in the form of a bar chart would give a much more visual impact to her presentation, e.g.:
It is evident from the bar chart that June has the greatest value and that the value has increased from April through June.
Using a Component Bar Chart
A component bar chart is like a regular bar chart, but each bar is divided into its component parts.
Following on from the previous case example, the managing director has asked the marketing manager for more detailed information about the data she presented – in particular, which type of policy has the greatest sales value.
The marketing manager has the following information:
Month
Policy Type
Apr 03
May 03
Jun 03
Term
£150,000
£350,000
£800,000
Whole of life
£200,000
£550,000
£750,000
Endowment
£100,000
£250,000
£300,000
Presenting the data in the form of a component bar chart easily shows a comparison between the types of policies, e.g.:
In this example, it is evident that the whole of life policy has the greatest sales value and endowment has the least sales value.
3. Pie Charts
Sometimes you don’t need to know the actual value of each component piece of quantitative information for something you are analyzing. You may be more interested to find out the comparative value of each proportion of the whole. Pie charts are an effective visual means of displaying this information. They are a useful way of evaluating, for example, how the expenditure in a budget has been allocated.
Example
The Chancellor of the Exchequer is going to present the budget allocation for each of the four key areas of expenditure in the next fiscal year (fiscal year = 06 April to 05 April).
From the pie chart, it is clear that expenditure on Defense forms the largest slice of the pie.
4. Graphs
Line graphs are a good way of showing how sets of numerical data have changed over time. They are useful for three reasons:
- They give a visual representation of patterns over time.
- They can be used to forecast or extrapolate what might happen in the future.
- They give a visual representation of variances or exceptions (i.e. sudden increases or decreases).
You can also add a trend line to line graphs to show the general trend of the data, for example, if the data has increased or decreased over time. Software applications such as Excel give you the option of adding a trend line to your graphs. They are calculated using statistical analysis such as linear regression.
Example
Q1
2,250,000
Q2
3,450,000
Q3
4,500,000
Q4
3,000,000
If your data is widely dispersed, a scatter graph may be more useful for displaying the information. You will be able to clearly see if the information is clustered, evenly spread, or if there is a general pattern. By adding a trend line, you can see the trend of the data.
Example
How to Design Graphs
Microsoft Word: The table function on the toolbar helps you to construct simple tables and frequency distributions without using calculations.
Microsoft Excel: Constructing graphs and charts on Excel is quite straightforward, provided you have a basic understanding of how to build spreadsheets and enter some simple formulae.
For example, the information in the table below was copied and imported into Excel, highlighted, and using the chart icon in the Excel toolbar, sample customized charts were tried out until the most appropriate chart was found. The chart was built by using menu prompt information.
Month
Policy Type
Apr 03
May 03
Jun 03
Term
150,000
350,000
800,000
Whole Life
200,000
550,000
750,000
Endowment
100,000
250,000
300,000
