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.

Next week our director Tim Heng from the Sydney office will be in Seattle attending Microsoft’s Data Insights Summit!

Whilst I was creating data to use for my training and blogs on Power Query, I spent time manipulating my data in a Microsoft Access database. It is however possible to do much of this transforming in Power Query and today I will look at a simple example.

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.