We assumed our data fit the normal distribution. We were not taught to check this before computing a mean and a standard distribution. The first thing it talked about was how to know if your data was normal. The answer is between the bread, aka the outliers.Ī long while back, I picked up a book on data analysis. ![]() The terminology and notation may vary, but in the mathematics sense, you have a sandwich. Notice that I added the terms upper and lower fence to the figure, as that is another way of referring to the whiskers. Don’t just assume the distribution is normal or fits under a standard normal. Taking the mean and the standard deviation takes more work. Any collection of data, any series of data has a median, a minimum, a maximum, and quartiles. Going further, keep in mind that a box plot is about a series of numbers. Be warned here that the median may not be the mean and when it isn’t, the real distribution would be skewed and non-normal. The diagram also shows Q2 as the median and correlates Q2 with the mean of a standard distribution. The IRQ includes the quartiles between Q1 and Q3. Here the outliers are shown going out to +/- 1.5 IRQs beyond the Q1 and Q3 quartiles. Now, we’ll take one more look at the box plot. Each step would also have its own long tail for functionality use frequencies. Each such step would have its own content marketing relative to referral bases. Here the sticks are arbitrary, but could be laid left to right in order of their pragmatism step. Each stick represents a nominal distribution in a collective normal distribution. Which customer? Are they really prospects, aka potentially a new customer, or the customer, as in the retained customer? Are they historic customers, or customers in the present technology adoption lifecycle phase? Are they on the current pragmatism step or the ones a few steps ahead or behind? Do you have a box and whisker chart for each of those populations, like the one below? We really can’t work in the blur we call talking to the customer. When sales, through the randomizing processes they use brings us demand for functionality beyond the outliers of our notations, just say no. We do it by defining another population and constructing a box and whiskers plot for that population. Even when we are stepping over to an adjacent step on the pragmatism scale, we don’t do it by stepping outside our outliers. The real point here is that the customer we talk about listening to is somewhere in this notation. Notice that there are other data points beyond the outliers. Where the figure on the left says the outliers are more than 3/2’s upper quartile, or less than 3/2’s the lower quartile. Outliers might be included in the whisker parts of the notation or beyond the reach of the whiskers. Notice that outliers appear in the figure on the left, but not on the right. Just ignore it and stick with the maximum and minimum, as shown on the left. Notice the 5th and 95th percentiles appear in the figure on the right, but not the left. We usually see them in a vertical orientation, and not a horizontal one. The five-number summary consists of the minimum, the maximum, the median, the first quartile, and the third quartile.īoxplots are also known as box and whisker charts. Statistics can take any series of numbers and summarize them into the five-number summary. In the beginning, yeah, I came down that particular path, the one starting with the five-number summary. “Dude, your prospect isn’t even an outlier!” Tying box plots back to product management, it gives us a simple tool for saying no to sales. They can also tell us what we don’t know. I also wrestled with some geometry relative to triangles and hyperbolas. Along with the stuff I know, came some new stuff. ![]() Last weekend, I watched some statistics videos.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |