With the information provided in the problem, we can create a right triangle with the ramp as its hypotenuse, and the vertical rise as its opposite side from its angle of elevation.
Let
be the angle of elevation from the car to the end of the ramp. We now know that the hypotenuse of our triangle measures 445 feet, and the opposite side measures 80 feet, so we need a trig function that relates our angle of elevation with the hypotenuse and the opposite side. That function is sine:
We can conclude that the angle of elevation from the car to the end of the ramp is 10.36°.
Answer:
Step-by-step explanation:
Given
Required
Evaluate when n = 12
Substitute 12 for n in
Simplify the fraction
If there are multiple operations at the same level on the order of operations off from left to right and you work like this first noticed that there are no parentheses or exponents so we moved to multiplication and division with any sense of parentheses
It’s add positive 2 every time I believe
if I’m wrong I’m so sorry and please tell me I’m wrong for other that have to use this.
If I’m right ya!! XD
Hope this helps you!
-Pam Pam
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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