## High-Performance Statistical Queries: Dependencies Between Discrete Variables

15 Dec 2017

In my previous article, we looked at how you can calculate linear dependencies between two continuous variables with covariance and correlation. Both methods use the means of the two variables in their calculations. However, mean values and other population moments make no sense for categorical (nominal) variables.
For instance, if you denote "Clerical" as 1 and "Professional" as 2 for an occupation variable, what does the average of 1.5 signify?
## High Performance Statistical Queries –Skewness and Kurtosis

24 Jul 2017

In descriptive statistics, the first four population moments include center, spread, skewness, and kurtosis or peakedness of a distribution. In this article, I am explaining the third and fourth population moments, the skewness and the kurtosis, and how to calculate them.
Mean uses the values on the first degree in the calculation; therefore, it is the first population moment. Standard deviation uses the squared values and is therefore the second population moment.
## SQL Statistical Analysis Part 2: Calculating Centers of Distribution

8 May 2017

My previous article explained how to calculate frequencies using T-SQL queries. Frequencies are used to analyze the distribution of discrete variables. Today, we’ll continue learning about statistics and SQL. In particular, we’ll focus on calculating centers of distribution. We’ll learn e.g. how to calculate the SQL median, what functions to use to calculate the SQL mode, and how to calculate various types of mean in SQL (geometric mean, harmonic mean and, of course, arithmetic mean).
## SQL Statistical Analysis Part 1: Calculating Frequencies and Histograms

28 Mar 2017

Database and Business Intelligence (BI) developers create huge numbers of reports on a daily basis, and data analyses are an integral part of them. If you wonder whether you can perform statistical analysis in SQL, the answer is ‘yes’. Read my article to learn how to do this!
Statistics are very useful as an initial stage of a more in-depth analysis, i.e. for data overview and data quality assessment. However, SQL statistical analysis possibilities are somewhat limited as there are not many statistical functions in SQL Server.