BUS2302
Assignment 3
Software Instructions
Christopher Anthony
3/16/2015
Spreadsheets are one of the modern person’s greatest friends. Computers have given arise to vast
amounts of computational power and the analysis of numbers in different ways to help people see their
earnings from a different perspective they never thought that they could before.
However, spreadsheets can easily be misleading. Whether accidental or on purpose the data can be
construed to display results that do not reflect their true nature.
Bar graphs in particular have a history for misleading the viewer. This is due to the fact that the bar
graph does not count out the outliers of the data set and does not show the effect a high outlier has on
the results.
Another form of this is over simplifying your data that you collected. For example:
You have Company A, and Company B; Company A has had over 100,000 transactions in 2014 netting
them $2,000,000, Company B has had 50,000 transactions in 2014 giving netting them $4,000,000. Now
by looking at this data forthright anyone would conclude that Company B is making more money, thus is
a more successful business.
Now – let us get a visual representation of this data in two separate ways.
Notice how in the Sales Graph it appears
Sales
that company A is a lot more successful than
120000
company B – due to just a simple (and
100000
common) misleading technique which shows
80000
60000
Company A
total transactions rather than net profit.
40000
Company B
However, when you go down to graph #2 it
20000
makes it clear that Company B is much more
0
Company A
successful than its competitor.
Company B
It is very important when looking at charts to
Net Profits
read the wording carefully. Look at
5000000
formatting and try to find the data the charts
4000000
are based on before making any
3000000
Company A
2000000
Company B
1000000
0
Company A
Company B
assumptions on them.
Line graphs are often misinterpreted as well. Line graphs are essentially scatter plots that connect the
dots in a linear fashion to give the viewer some sort of relationship between the points. However, just
because the line is there does not necessarily mean that a relationship is present. Much like the bar
graph having an outlier can ruin the accuracy of the graph by highlighting trends that are not there.
Altering and making changes to the X and Y axis scaling can also have misleading effects on charts.
Having the scale start too high on a data set with similar sized numbers can make a minute change in
value look like a drastic change.
Here is an example of a misleading graph plotted with the following data
Year
Gas Prices
2013
1.3
2014
1.299
2015
1.32
Gas Prices
Since the y-axis starts at 1.29 and end
1.33
at 1.33 it makes the scaling of small
1.33
changes in this case 2 cents look as if
1.32
gas prices are tripling in 2015. This is a
1.32
very common form of misleading data
1.31
Gas Prices
1.31
in charts. Always remember to look at
the scaling of a graph to ensure you
1.30
are not being misguided to interpret
1.30
data incorrectly.
1.29
2013
2014
2015
To conclude, in order to accurately represent data it is important to keep the following in mind:
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Make the Y-axis properly scaled
Input correct data
Avoid line graphs (use scatterplot with a trendline)
Properly label both of the axes
When you are the viewer of a graph remember to check the sources of the graphs, the axes scales and
most importantly that the data is plotted correctly.
Common features of spreadsheet programs that should be used to display and analyze data are:
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Sorting
Charts
Conditional Formatting
Pivot Tables
Tables
What-If Analysis
Trendlines
Sorting allows the user to easily organize their data. It can be from largest number to lowest number
(vice-versa) or A-Z and many others, but these are the two most common forms of simple sorting of data
and can be helpful in finding the lower limits and upper limits in a data set.
For organizational spreadsheets sorting the data from A-Z allows you to filter results per person –
categorizing it for an individual. This creates an opportunity to isolate a variable and find the results that
it produces.
Charts allow your data to be displayed in many different formats. These can range from pie-charts, line
graphs, bar graphs, area graphs and many others to help visualize the data you are inputting. Visual aids
are very powerful and often make or break whether or not your data is well received. However, be wary
using charts because they can often mislead and alter data in discrete ways.
Conditional Formatting is a great way to analyze your data set. Conditional formatting allows the user to
create a condition (criteria) and an output which changes the format of whichever cells meet the
criteria.
This is very useful; one use I can see for conditional formatting is taking stock in a large store. You can
make a conditional formatting to highlight all items that are running low on stock be highlighted in red
with a note saying “PLEASE ORDER” in order to efficiently go through your large spreadsheet of data.
Pivot tables allow you to get detailed analysis in charts and analyze data within a given cell range. This
allows you to filter your data in ways such as
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Sum
Count
Average
Max
Min
X-Axis Field
Y-Axis Field
Pivot tables can provide you with auto-calculations and filterable charts. These are aimed more at the
business-user who prepares and uses their own charts in order to get insight onto their business.
However, often times a simple table will suffice for effective data analysis. Tables are applied to a cell
range with headers and data. The columns can be filtered and sorted as many times as you wish in many
different ways, you can hide certain results and only show one particular result.
Charts are very useful when you have a large amount of data that you need to quickly sift through. The
simplicity and ease of use is what gives the basic chart its power.
What-if analysis allows you to try put in different inputs into a scenario and see what would happen with
these inputs. This is particularly useful when trying to extrapolate data on projected sales. Although this
sort of analysis is basic on excel – what if analysis has given rise to corporate analytics and huge
corporations based on it.
One of my personal favorite features in spreadsheet software is the ability to plot trendlines on a graph
with a click of a button. Trendlines are very useful when trying to interpret data because they
compensate for outliers and don’t skew the data off because of them.
This can not only allow you to foresee how your data will be like in the future, but allows your current
data to be accurately and effectively perceived.
Spreadsheets have many built in analytic features to help the user get the most insights out of there
data. Some of the most basic tools are also the most useful in this case; tools such as sorting, charts,
conditional formatting, pivot tables, tables, what-if analysis and trendlines can help create effective
delivery and great analysis.
References
Excel What-If Analysis. (n.d.). Retrieved March 17, 2015, from http://www.exceleasy.com/data-analysis/what-if-analysis.html
Graphs and Charts. (n.d.). Retrieved March 17, 2015, from
http://cs.iupui.edu/~aharris/mmcc/mod6/abss8.html
Misleading Graphs: Real Life Examples. (n.d.). Retrieved March 17, 2015, from
http://www.statisticshowto.com/misleading-graphs/
Misleading with pictures: The pitfalls of data visualization. (2014, March 12). Retrieved March
17, 2015, from https://figureoneblog.wordpress.com/2014/03/12/misleading-withpictures-the-pitfalls-of-data-visualization/
Using Excel for Data Analysis. (n.d.). Retrieved March 17, 2015, from
http://people.umass.edu/evagold/excel.html