This is a Guest Post by Robert on Visualization Techniques for Excel KPI Dashboards.
This 6 Part Tutorial on Management Dashboards Teaches YOU:
Creating a Scrollable List View in Dashboard
Add Ability to Sort on Any KPI to the Dashboard
Highlight KPIs Based on Percentile
Add Microcharts to KPI Dashboards
Compare 2 KPIs in the Dashboards Using Form Controls
Show the Distribution of a KPI using Box Plots
In this final post we will learn how to add a box plot to show the distribution of values
The solution
The most common way in descriptive statistics to visualize the distribution of sets of numerical data is a box plot. But according to my experience in day to day business, most business people are not familiar with this type of visualization.
Therefore we try to create a simpler chart which is hopefully easier to understand:
The light grey bar visualizes the range of all values, the dark grey bar the range of the 10 items displayed on the management dashboard table. The cross shows the total average and – similar to the bullet graphs – the vertical line represents the target. This is less information than a real box whisker plot would provide, but I guess it will be easier to understand.
The implementation
Download the Excel KPI dashboard final workbook and read on how to create a simplified box plot.
- Let’s bring our ducks in a row first. Calculate all necessary data to be shown in the box plots: the minimum and maximum of the total data and of the 10 displayed items on the dashboard, the average and the target. The formulas are quite simple. You can find them in the downloaded workbook in
calculation!AZ23:BE27. - The basis of our visualization is a stacked bar chart with only one category and 4 data series:
- the invisible bar (the bar between 0 and the total minimum),
- the left light grey bar (the bar between the total minimum and the minimum of the displayed 10 items),
- the dark grey bar (the bar between the minimum and maximum of the 10 displayed values) and
- the right light grey bar (the bar between the maximum of the 10 displayed items and the total maximum).
Again the formulas to calculate these values are quite simple (see calculation!BF23:BI27).
- Create a stacked bar chart and format the bars accordingly (no fill color and no border for the invisible bar, light and dark grey fill colors for the other bars).
- Add the average and the target values as additional series to the chart and change the chart type of these new series to XY scatter charts (X is the average / target value, Y is a dummy 1). Format the average as a cross (or whatever you choose) and use the error bars to format the target as a vertical line. The method of creating a combination chart of bars and XY scatters is pretty much the same we used in the 4th post of the KPI dashboard series (here).
- Remove or hide all unnecessary chart elements: no fill color and no border for plot or chart area; no line, tick marks etc. for the vertical axes, etc.
- Repeat steps 3 to 5 to create charts for all 5 KPI.
- Bring the charts to the dashboard, position them and add a caption to explain the chart elements.
That’s it. Play around with the new feature: change the sort criteria or sort order or scroll up and down the dashboard table and see how the new charts are changing.
Final Remark
This is a simplified version of box plot visualization and works only for data sets with positive values. Of course there is also a more sophisticated way of creating charts like this for any data (positive and negative values, i.e. bars crossing the vertical axis). This is a bit more complicated since you need 8 data series for the bar chart instead of 4 but the principle is exactly the same.
Our final KPI dashboard looks like this (click on it for a larger version):
What’s next?
With this last part I guess the time may have come to end the series about Excel Management KPI Dashboards here and to hand over the further development of this dashboard to the readers of Chandoo.org.
I do hope the series of 6 posts have been useful for your daily work and provided new ideas. Make sure you have downloaded the Excel KPI dashboard tutorial workbook
Thanks for all your comments and appreciations.
Last but not least: Chandoo, my friend, once more thank you so much for hosting my ideas at Chandoo.org.
Kind regards from Munich
Robert
Chandoo’s note
If not for Robert’s mail in August suggesting these wonderful ideas as posts in PHD, I would never have learned these things or shared them with you all. I am thankful to him for that.
Well, I am constantly trying to learn new dashboard techniques and I will try to share the worthy ones with you all. Meanwhile if you have a good idea for excel dashboards (or charts, techniques etc.) and would like to share with everyone, feel free to drop a comment or write to me. I will be *happy* to feature your ideas.
Further Reading on Dashboards using Excel
Checkout our exclusive section: Excel Dashboards for more tutorials, tips, design principles.
You can also consider joining my Excel School program to learn how to make world-class dashboards.
12 Responses to “29 Excel Formula Tips for all Occasions [and proof that PHD readers truly rock]”
Some great contributions here.
Gotta love the Friday 13th formula 😀
Great tips from you all! Thanks a lot for sharing! bsamson, particularly you helped me on a terribly annoying task. 🙂
(BTW, Chandoo, it's not exactly "Find if a range is normally distributed" what my suggestion does. It checks if two proportions are statistically different. I probably gave you a bad explanation on twitter, but it'd be probably better if you fix it here... 🙂 )
Great compilation Chandoo
For the "Clean your text before you lookup"
=VLOOKUP(CLEAN(TRIM(E20)),F5:G18,2,0)
I would like to share a method to convert a number-stored-as-text before you lookup:
=VLOOKUP(E20+0,F5:G18,2,0)
@Peder, yeah, I loved that formula
@Aires: Sorry, I misunderstood your formula. Corrected the heading now.
@John.. that is a cool tip.
Hey Chandoo,
That p-value formula is really great for a statistics person like me.
What a p-value essentially is, is the probability that the results obtained from a statistical test aren't valid. So for example, if my p value is .05, there's a 5% probability that my results are wrong.
You can play with this if you install the Data Analysis Toolpak (which will perform some statistical tests for you AND provide the P Value.)
Let's say for example I've got two weeks of data (separated into columns) with the number of hours worked per day. I want to find out if the total number of hours I worked in week two were really all the different than week one.
Week1 Week2
10 11
12 9
9 10
7 8
5 8
Go to Data > Data Analysis > T-Test Assuming Unequal Variances > OK
In the Variable 1 Box, select the range of data for week 1.
In the Variable 2 Box, select the range of data for week 2.
Check "Labels"
In the Alpha box, select a value (in percentage terms) for how tolerant you are of error.
.05 is the general standard; that is to say I am willing to accept a 95% level of confidence that my result is accuarate.
Select a range output.
Excel calculates a number of results: Average (mean) for each week's data, etc.
You'll notice however that there are two P Values; one-tail and two-tail. (one tail tests are for > or .05), the number of hours I worked in week two is statistically equivalent to the number of hours I worked in week one.
So here’s a way you might want to use this. You put up a new entry on your blog. You think it’s the best entry ever! So you pull your webstats for this week and compare it to last week. You gather data for each week on the length of time a visitor spends on your website. The question you’re trying to prove statistically is whether there’s an average increase in the amount of time spent on your website this week as compared to last week (as a result of your fancy new blog post). You can run the same statistical test I illustrated above to find out. Incidentally, it matters very little to the stat test whether the quantity of visitors differs or not.
Anyhow, the Data Analysis toolpack doesn't perform a lot of stat tests that folks like me would like to have access to. In those cases I have to either use different software, or write some very complicated mathematical formulas. Having this p-value formula makes my life a LOT easier!
Thanks!
Eric~
Fantastic stuf..One line explanation is cool.
Thanks to all the contributors
OS
Take FirstName, MI, LastName in access (you can fix it to work in excel) capitalize first letter of each and lowercase the rest and add ". " if MI exists then same for last name:
Full Name: Format(Left([FirstName],1),">") & Format(Right([FirstName]),Len([FirstName])-1),"") & ". ","") & Format(Left([LastName],1),">") & Format(Right([LastName],Len([LastName])-1),"<")
I teach excel, access, etc etc for a living and i have my access students build this formula one step at a time from the inside out to show how formulas can be made even if it looks complicated. Yes I know I could just do IsNull([MI]) and reverse the order in the Iif() function but the point here is to nest as many functions as possible one by one (also I illustrate how it will fail without the Not() as it is)
Extract the month from a date
The easiest formula for this is =MONTH(a1)
It will return a 1 for January, 2 for February etc.
if in a column we write the value of total person for eg. 10 if we spent 1.33 paise each person then how we get total amount in next column and the result will in round form plzzzzz solve my problem sir................... thank u
@Anjali
If the value 10 is in B2 and 1.33 paise is in C2 the formula in D2 could be =B2*C2
If the values are a column of values you can copy the formula down by copy/paste or drag the small black handle at the bottom right corner of cell D2
kindly share with me new forumulas.
How to convert a figure like 870.70 into 870 but 871.70 into 880 using excel formula ? Please help.