Answer: 942
Step-by-step explanation: 20 x 3.14 = 62.8 62.8 x 15 = 942
A complex mathematical topic, the asymptotic behavior of sequences of random variables, or the behavior of indefinitely long sequences of random variables, has significant ramifications for the statistical analysis of data from large samples.
The asymptotic behavior of the sample estimators of the eigenvalues and eigenvectors of covariance matrices is examined in this claim. This work focuses on limited sample size scenarios where the number of accessible observations is comparable in magnitude to the observation dimension rather than usual high sample-size asymptotic .
Under the presumption that both the sample size and the observation dimension go to infinity while their quotient converges to a positive value, the asymptotic behavior of the conventional sample estimates is examined using methods from random matrix theory.
Closed form asymptotic expressions of these estimators are obtained, demonstrating the inconsistency of the conventional sample estimators in these asymptotic conditions, assuming that an asymptotic eigenvalue splitting condition is satisfied.
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Answer:
P = 20 cm
Step-by-step explanation:
The appropriate formula is P = 2W + 2L.
Here that comes to P = 2(6 cm) + 2(4 cm) = 20 cm
Answer:
Step-by-step explanation:
FIRST ONE D
SECOUND ONE IS 52
THRID 505
FORD 40
FIVE 18
<h3>
Answer: x-2</h3>
Explanation:
If x > 2, then x-2 > 0 after subtracting 2 from both sides.
Since x-2 is always positive when x > 2, this means the absolute value bars around the x-2 aren't needed. The results of |x-2| and x-2 are perfectly identical.
For example, if we tried something like x = 5, then
- x-2 = 5-2 = 3
- |x-2| = |5-2| = |3| = 3
Both outcomes are 3. I'll let you try other x inputs.
So because |x-2| and x-2 are identical, this means |x-2| = x-2 for all x > 2.
In short, we just erase the absolute value bars.
