dChan
Anonymous ID: d7cc22 Sept. 15, 2020, 2:12 p.m. No.10659890   🗄️.is 🔗kun   >>9985

>>10659825

TYB!

o7

 

At this Point, what difference does a Body Double Make….?

 

MUTHA FUCKING KEKS SQuARED!

 

REEEEE-ZULTS MATTER

 

From our earlier discussion about standard deviation, we recall that the spread or dispersion of a set of data about the mean will be small if the standard deviation is small, and it will be large if the standard deviation is large. If we are dealing with a symmetric, bell-shaped distribution, then we can make very definite statements about the proportion of the data that must lie within a certain number of standard deviations on either side of the mean. This will be discussed in detail in Chapter 7 when we talk about normal distributions.

 

However, the concept of data spread about the mean can be expressed quite generally for all data distributions (skewed, symmetric, or other shapes) by using the remarkable theorem of Chebyshev.>>10659825

Anonymous ID: d7cc22 Sept. 15, 2020, 2:33 p.m. No.10660090   🗄️.is 🔗kun

>>10660060

It is important to realize that statistics and probability do not deal in the realm of certainty. If there is any realm of human knowledge where genuine certainty exists, you may be sure that our statistical methods are not needed there. In most human endeavors, and in almost all of the natural world around us, the element of chance happenings cannot be avoided. When we cannot expect something with true certainty, we must rely on probability to be our guide. In this chapter, we will study regression, correlation, and forecasting. One of the tools we use is a scatter plot. René Decartes (1596–1650) was the first mathematician to systematically use rectangular coordinate plots. For this reason, such a coordinate axis is called a Cartesian axis.