#### New

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#### How to Round in R

How to Round in R This is a good source https://www.geeksforgeeks.org/rounding-off-values-in-r-language-round-function/

#### Chebyshev’s Theorem Excel Calculator

Chebyshev’s Excel Calculator Chebyshev’s Theorem can be used for any type of distribution, but if the problem says the distribution is “bell shaped,” use the Empirical Rule unless the problem specifically says to use Chebyshev’s. Note: an alternative spelling is Chebychev. Chebyshev’s only applies to problems where the K is > 1. In our MSL

#### Baby Steps

Image by Laura Retyi from Pixabay I watched dumbfounded as 82- ear-old Wally Funk moved quickly leading her fellow soon-to-be astronauts up the 8 flights of stairs on the Blue Origin launch pad. As one 75-year-old who gets tired carrying my few bags of groceries up my one flight of stairs, I wondered how she

#### Bomber Mafia

Summer Reading The Bomber Mafia by Malcolm Gladwell One of the first names I had to memorize as part of my Doolie Knowledge at the Air Force Academy was that of Curtis E. LeMay, the then Air Force Chief of Staff but better known for his WWII exploits and later “father” of the Strategic Air

#### What is Deep Learning?

What is Deep Learning? This article provides some insight into the future of Deep Learning and explains the differences between it, supervised learning, and reinforcement learning. Well worth a read.

#### AI and Deep learning

What is Deep Learning? This article provides some insight into the future of Deep Learning and explains the differences between it, supervised learning, and reinforcement learning. Well worth a read.

#### sMAPE

How to calculate sMAPE Subtract the actual value from the absolute forcast Square the result Find the square root of the previous result (step #2) sMAPE = Square Root ( F – A)^2 In general when comparing forecast methods, the smaller the sMAPE the better. <h2>How to calculate sMAPE in r</h2> <h3>Use the Metrics Package.</h3><p>Assume

#### Why is 2-tail p-value twice the 1-tail p-value?

Why is the two-tail p-value always twice the one-tail p-value in a t-test? In the t-test output below, notice that the two-tail p-value is 0.66852 while the one-tail p-value is 0.33426. The two-tail p-value is twice the one-tail p-value. Why is this true? The two-tail p-value is defined as P > |t|. That is, the

#### 1 Test post

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