Weighted Average In Power bi | Average vs Weighted Average

Asan Tutorials
Asan Tutorials
6 هزار بار بازدید - 3 ساعت پیش - Welcome to our video on
Welcome to our video on using variables, summarize, and addcolumns functions to calculate weighted averages in Power BI! First, let's define what a weighted average is. A weighted average is a type of average that takes into account the importance or weight of each value. This is in contrast to a simple average, which treats all values equally. To calculate a weighted average in Power BI, you'll need a column of values and a column of weights. The values column represents the data you want to average, and the weights column represents the importance of each value. To perform a weighted average in Power BI using variables, summarize, and addcolumns functions, you'll need to first create a variable that represents the sum of the weights. You can do this by using the summarize function to sum the weights column and then using the addcolumns function to create a new column with the name "Total Weight" and the value of the sum of the weights. Next, you'll need to use the summarize function again to calculate the sum of the product of the values and weights columns. You can do this by specifying the values column and the weights column as the two arguments for the summarize function and setting the operation to "product". Finally, you can use the divide function to divide the sum of the product of the values and weights columns by the total weight. This will give you the weighted average of the values. Once you've calculated the weighted average using these functions, you can use it in your visualizations just like any other measure. You can also use it in calculations with other measures and dimensions to create more complex analysis. That's it for our video on using variables, summarize, and addcolumns functions to calculate weighted averages in Power BI. We hope you found it helpful and are now able to use these functions in your own Power BI reports and dashboards. Thanks for watching!
3 ساعت پیش در تاریخ 1403/07/17 منتشر شده است.
6,018 بـار بازدید شده
... بیشتر