Answer:
V=96cm³
Step-by-step explanation:
If it is a Right rectangular pyramid there is your answer.
One angle has 60 degrees, the other angle has 30 degrees.
60 divided by 30 = 2.
and 60 + 30 = 90.
<span>18/37 if you're in Europe, 9/19 if you're in America, assuming the wheel isn't rigged.
If the roulette wheel is a fair wheel, the odds on any given spin of the wheel is unaffected by any previous spins of the wheel. So no matter how many times the wheel has consecutively come up red, the next spin can still come up red, black, or green based upon the number of each color is present on the wheel. The difference between the odds for Europe vs America has to do with a quirk about roulette. In Europe, the roulette wheel has 37 slots, 18 red, 18 black, and 1 green. In America, the roulette wheel has 38 slots, 18 red, 18 black, and 2 green.
Although in this case, I'd be wary about assuming that the wheel isn't rigged. The odds of 270 consecutive red on a fair wheel is only 1 in 3.09663x10^84 in Europe, or 1 in 4.14949x10^87 in America.</span>
Answer:
for problem 1: -4 x -2 = -6
for problem 2: -8x +3 = -11x
Step-by-step explanation:
I hope it helped
brainlest please
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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