All articles with 'charting' Tag

Imagine you run an office furniture company. You want to stop reordering two brands of furniture – Relaxer (a type of chair) and Boca Top (a type of table). You currently have 20,000 Relaxer chairs and 5,000 Boca Tops. These are valued at $200,000 and $100,000 respectively. When sold, they will yield $100,000 and $25,000 gross profit. You are hoping to sell them off in 2 or 3 years. You forecast that we can sell off these as per some yearly schedule.

You need to analyze this and prepare a cash flow model.

Let’s learn how to answer such open ended questions using various analysis techniques in Excel.

Few days ago, I saw a beautiful homemade science experiment on Sand Pendulums on Bruce Yeany‘s YouTube channel. Go ahead and check it out. It is a cool project to do with your kids.

I will try this experiment with kids during school term holidays around Easter. But first, I wanted to try the simulation in Excel.

Simulating sand pendulum pattern in Excel

Take a look at the final simulation above. This is what we will create in Excel.

In this amazing guest post, the winner of our 2016 dashboard contest – Chandeep – Explains how he constructed the jaw dropping beauty (shown above) using Excel, creativity, love and sweat. Grab a full cup of coffee (or whatever liquid fancies you) and read on. Take lots of notes and play with the ideas in Excel while reading to maximize your learning.

Thanks Chandeep.

Let’s say you work in super hero factory as floor manager. You are looking at the recent time sheet data submitted by your underlings and want to know who works more. So you did what any self respecting floor manager does. You made yourself a large cup of hot chocolate, whipped open Excel and created a column chart.

But now, you want to add a line to it at 6:00 PM (or some other arbitrary  point) so you can clearly see which superheros are over working.

So how do you go about it?

It is election time in USA, and that means there is a whole lot of drama, discussions and of course data analysis. There are tons of cool visualizations published on all the data. Previously, we talked about “How Trump happened” chart.

Today let’s take a look at the beautiful decision tree chart by NY Times explaining what would happen if each of the 10 swing states vote for Democrats or Republicans. Go ahead and look at that chart. And when you are done playing with it, come back.

My first thought after looking at the chart is: Wow, that is cool. I wonder how we can recreate that experience in Excel?

But as you can guess, making a dynamic tree visualization in Excel is pretty hard. You can create a bubble chart mixed with XY chart to show all the nodes of the decision tree, but as this tree has 2^10 nodes at the bottom level (and 2^11-1 total nodes) our chart would look very clumsy and busy.

So, instead of replicating NY Times chart, why not make our own version that explains the data? You can reuse this idea when visualizing outcomes of several what-if scenarios.

Over the years, we have discussed a whole heap of techniques to visualize budget vs. actual charts. Today let’s take a ride on this slope again and learn another fun, silly & awesome way to depict target vs. actual progress.

Introducing biker on a hill chart

Tada!!!

Biker on a hill!?! Don’t worry, I didn’t fall down on a descent and lose my brain. I am talking about an Excel chart to visualize target vs. actual progress on a time line with biker on a hill analogy. See the above chart, you will know.

Looks interesting? Read on to learn how to create this in Excel.

Lets take last weeks Stacked Bar/Column Chart and add some high-performance steroids.

Learn how to develop a Stacked Bar chart with Indicator Arrow in this Tutorial

Here is a trap that is easy to fall in to. Confusing correlation as causation. As analysts, it is our job to see the data as it is rather than imply causation that doesn’t exist.

Let’s sample a chart, recently featured in Economist’s graphic detail under the title Measuring well-being.

One of the coolest features of Excel 2016 is forecasting. Today, let’s understand how it works with a sample data set.

Watch below video to understand forecasting in Excel 2016.

Our newest contest is inspired from a question asked by Kaushik, one of our forum members, interesting problem.

Need to quickly visualize 3 variables ( Company, years, Financials) in a single […] chart.

Create a chart to understand multiple variable data and you could win $100 Amazon gift card. Do send your charts before 4th of July to qualify for the prizes.

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In the 54th session of Chandoo.org podcast, let’s make you awesome in Pivot Tables.

What is in this session?

In this podcast,

• Quick updates
• Top 10 pivot table tricks
  • Adding same value field twice
  • Tabular layouts
  • GETPIVOTDATA & 2 bonus tricks
  • Relationships & data model
  • One slicer to rule them all
  • Show only top x values
  • Relative performance
  • Show unique count
  • Spruce up with conditional formats
  • Not so ugly pivot charts
• Resources & Show notes for you

It is Easter time again. This year, we drove to my brother’s house in Hyderabad (700 km away from my home) to spend a weekend doing absolutely nothing (we will eat copious amount of food, share family memories, laugh and laze). It is Chandoo.org tradition to share few puzzles during Easter time, a la an Excel themed virtual Easter egg hunt. This year, I have prepared an amazing challenge for you.

This is the final part of our series on how to analyze half a million customer complaints. Click below links to read part 1 & 2.

1. Complaint reason analysis – Part 1
2. Regional trends & analysis – Part 2

Customer satisfaction scorecard

In the previous parts of this case study, we understood what kind of complaints were made and where they came from (states). For the customer satisfaction scorecard, let’s focus on individual companies.

This is part two of our three part series on how to analyze half a million customer complaints. Read part 1 here.

Analyzing Regional Trends

As introduced in part 1, our complaints dataset has geographical information too. We know the state & zip code for each complaint. Please note that zip codes are partial or missing for a 10% of the data.

In this article, let’s explore three ways to analyze regional trends.

1. Regional trends by state, product & issue
2. Complaints per million by state
3. Complaints by zip code