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To represent the number 1,230,400 in normalized scientific notation, the decimal separator would be moved 6 digits to the left and × 10 6 appended, resulting in 1.2304 × 10 6. The number −0.004 0321 would have its decimal separator shifted 3 digits to the right instead of the left and yield −4.0321 × 10 −3 as a result.
[contradictory] For example, the number 4 000 000 has a logarithm (in base 10) of 6.602; its order of magnitude is 6. When truncating, a number of this order of magnitude is between 10 6 and 10 7. In a similar example, with the phrase "seven-figure income", the order of magnitude is the number of figures minus one, so it is very easily ...
[6] [7] [a] The parentheses can be omitted if the input is a single numerical variable or constant, [2] as in the case of sin x = sin(x) and sin π = sin(π). [a] Traditionally this convention extends to monomials; thus, sin 3x = sin(3x) and even sin 1 / 2 xy = sin(xy/2), but sin x + y = sin(x) + y, because x + y is not a monomial ...
For each integer n > 2, the function n x is defined and increasing for x ≥ 1, and n 1 = 1, so that the n th super-root of x, , exists for x ≥ 1. However, if the linear approximation above is used, then y x = y + 1 {\displaystyle ^{y}x=y+1} if −1 < y ≤ 0 , so y y + 1 s {\displaystyle ^{y}{\sqrt {y+1}}_{s}} cannot exist.
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The Normal PDF does not describe this derived data particularly well, especially at the low end. Substituting the known mean (10) and variance (4) of the x values in this simulation, or in the expressions above, it is seen that the approximate (1600) and exact (1632) variances only differ slightly (2%).
Seismic moment is measured in newton-meters (N m or N⋅m) in the SI, or dyne-centimeters (dyn⋅cm; 1 dyn⋅cm = 10 −7 N⋅m) in the older CGS system. In the simplest case the moment can be calculated knowing only the amount of slip, the area of the surface ruptured or slipped, and a factor for the resistance or friction encountered.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]