Post by account_disabled on Dec 6, 2023 10:37:15 GMT
include: mean, median, standard deviation, values for the first and third quartile. To easily interpret them, you can use a box plot (chart below) that shows some of these statistics. Data Analysis: Example of a Tableau chart on salesperson performance From the above chart we can read: the median (the border of the two gray rectangles in the middle), first and third quartiles (lower and upper outer limits of gray rectangles), quarter spacing (length of two gray rectangles), the first and third quartiles, respectively, with the value of the quarterly range subtracted and added, multiplied by . (the most extreme horizontal lines, the so-called "whiskers"; in the case of this chart.
The lower limit stops at zero because the sales value cannot be Email Marketing List negative), results of individual traders (dots). Based on the sample chart, we can see that in terms of sales value, two salespeople stand out from the rest. They are Richard Martinez and Robert Wilson. At the same time, the results they achieve are not improbable, so there is no reason to suspect an error in the data. Click and visualize your data with Tableau.
Conclusions from a cursory analysis of the data Although at this stage , charts and statistics are generated, this element should not be treated as a full data analysis. This stage is only intended to introduce the researcher to the characteristics of the data set and allow him to make possible transformations before the actual analysis. At the same time, at this stage we should decide on the approach to outliers, e.g. for the mentioned case of an invoice payment delay of days. In the literature, you can find an approach that suggests the use of the "three sigma" rule. This term means discarding all observations that are more.
The lower limit stops at zero because the sales value cannot be Email Marketing List negative), results of individual traders (dots). Based on the sample chart, we can see that in terms of sales value, two salespeople stand out from the rest. They are Richard Martinez and Robert Wilson. At the same time, the results they achieve are not improbable, so there is no reason to suspect an error in the data. Click and visualize your data with Tableau.
Conclusions from a cursory analysis of the data Although at this stage , charts and statistics are generated, this element should not be treated as a full data analysis. This stage is only intended to introduce the researcher to the characteristics of the data set and allow him to make possible transformations before the actual analysis. At the same time, at this stage we should decide on the approach to outliers, e.g. for the mentioned case of an invoice payment delay of days. In the literature, you can find an approach that suggests the use of the "three sigma" rule. This term means discarding all observations that are more.