MAGIC SQUARE: Calculate A*B+C
[1933] MAGIC SQUARE: Calculate A*B+C - The aim is to place the some numbers from the list (10, 24, 25, 32, 33, 34, 36, 37, 42, 46, 81) into the empty squares and squares marked with A, B an C. Sum of each row and column should be equal. All the numbers of the magic square must be different. Find values for A, B, and C. Solution is A*B+C. - #brainteasers #math #magicsquare - Correct Answers: 39 - The first user who solved this task is Djordje Timotijevic
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MAGIC SQUARE: Calculate A*B+C

The aim is to place the some numbers from the list (10, 24, 25, 32, 33, 34, 36, 37, 42, 46, 81) into the empty squares and squares marked with A, B an C. Sum of each row and column should be equal. All the numbers of the magic square must be different. Find values for A, B, and C. Solution is A*B+C.
Correct answers: 39
The first user who solved this task is Djordje Timotijevic.
#brainteasers #math #magicsquare
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Water and Whiskey

A professor of chemistry wanted to teach his 5th grade class a lesson about the evils of liquor, so he produced an experiment that involved a glass of water, a glass of whiskey, and two worms.
"Now, class, closely observe the worms," said the professor while putting a worm into the water.
The worm in the water writhed about, happy as a worm in water could be. He then put the second worm into the whiskey. It curled up and writhed about painfully, then quickly sank to the bottom, dead as a doornail.
"Now, what lesson can we learn from this experiment?" the professor asked.

Johnny, who naturally sits in back, raised his hand and wisely, responded confidently, "Drink whiskey and you won't get worms."

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Thomas Bayes

Died 17 Apr 1761 (born 1702). English theologian and mathematician who was the first to use probability inductively and who established a mathematical basis for probability inference (a means of calculating, from the frequency with which an event has occurred in prior trials, the probability that it will occur in future trials). This became the basis of a statistical technique, now called Bayesian estimation, for calculating the probability of the validity of a proposition on the basis of a prior estimate of its probability and new relevant evidence. Later statisticians cite disadvantages of the method that include the different ways of assigning prior distributions of parameters and the possible sensitivity of conclusions to the choice of distributions.
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