Uncovering relationships in data

In addition to understanding data distributions, exploring relationships between variables is important for gaining insights into how different factors interact and affect each other. By understanding how different variables are related to each other, you can gain insights into your data that would not be possible otherwise. You will be ready to write the queries that will unlock insights from your data.

There are several ways to uncover relationships in data. One common approach is to use correlation analysis. Correlation analysis measures the strength and direction of the relationship between two variables. A correlation coefficient of 1 indicates a positive relationship, a correlation coefficient of -1 indicates a perfect negative relationship, and a correlation coefficient of 0 indicates no relationship. For example, if you had a table of customer data that includes the customer’s age, gender, and income, you could use correlation analysis...