On the final Friday of each month, set an Excel for you to puzzle over for the weekend. On the Monday, we publish a solution. If you think there is an alternative answer, feel free to email us. We’ll feel free to ignore you.

On the final Friday of each month, we’re going to set Excel for you to puzzle over so that you can get your “Excel fix”. Challenge your office colleagues to see who can solve the puzzle quickest. There’s no prizes at this stage, you’re playing for bragging rights only!

When transforming data with Power Query, the most important skill is knowing where to start, and this week I look at one such scenario.

In probability theory and statistics, the chi-squared distribution (also chi-square or ?2-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.

We haven’t seen the VBA blog in a little while. So let’s take a look at things we can’t see. If something is hidden, it’s hard to see. Therefore, if something is very hidden, it must be very hard to see, right?

Workbooks which are built using multiple queries can become complex, and the Power Query Dependencies Viewer is a tool which allows an overview of how each query has been built.

In probability theory and statistics, the chi-squared distribution (also chi-square or ?2-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.

Our Sydney Director, Tim Heng, has just come back from Seattle where he was attending Microsoft’s Data Insights Summit!

For those following the series, my past blogs have often dipped into the expense accounts of my (fictional) salespeople. For today’s scenario, I will extract the start and end date of my employees’ expense period to show how easy it is when I use the grouping functionality in Power Query.

In probability theory and statistics, the chi-squared distribution (also chi-square or ?2-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.