![]() As you can see the number of floors stretch further to the right. ![]() If you look at the histogram charts you will see each of them has different shape. as you can see, most of the time I burned around 2200 to 2500 calories, also less than 5 times I burned calories less than 2000 calories. In the above picture, the first chart shows the data distribution for my calories burn during three mounths. Hist(dataset$Floors, main = “Histogram of Floors”, xlab = “Floors”) to create a histogram chart, I wrote blew R code. Below picture shows the data distribution for my Fitbit data (Floors, Calories Burned, and Steps). Histogram uses any number of bins of an identical width. ![]() The spread of the numeric variable can be check by the histogram chart. However, to see the data distribution another way is to draw a histogram or normal curve. (we have Boxplot as a custom visual in power BI see : ). Also, I have show how to draw them in Power BI, using R codes. In the Part 1 I have explained some of the main statistics measure such as Minimum, Maximum, Median, Mean, First Quantile, and Third Quantile.
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