Answer:
4x-8
Step-by-step explanation:
3/4(4x-8)+1/4(4x-8)
12/4x-24/4+4/4x-8/4
3x-6+x-2
3x+x-6-2
4x-8
Answer:
The answer is "99.82% and 86.99%".
Step-by-step explanation:
In point a:
In point b:

The purpose of the tensor-on-tensor regression, which we examine, is to relate tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without being aware of its intrinsic rank beforehand.
By examining the impact of rank over-parameterization, we suggest the Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN) methods to address the problem of unknown rank. By demonstrating that RGD and RGN, respectively, converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized scenarios, we offer the first convergence guarantee for the generic tensor-on-tensor regression. According to our theory, Riemannian optimization techniques automatically adjust to over-parameterization without requiring implementation changes.
Learn more about tensor-on-tensor here
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Using SohCahToa, we can find the height of the tree. Let the tree height be h. 8m is adjacent to the 30° angle.

The answer is A) 4.6 m