Answer:I'll explain this to you, It's a chart with lined down margin math equation.
Step-by-step explanation:
So the first up/down line margin is X*X, the second is X numbers, third is x^x numbers, fourth is explain the left to right's math solutions into words. Just imply your following numbers into X's and onto your chart. Hopefully this helps.
The answer is D because the whole circle is 369 degrees the big arc is 202 so 360- 202 is 158 then you have an inscribed angle of 35 multiply it by two to find the arc of that so it would be 70 so subtract 158-70=88 and you can see that that section of 88 is part of an inscribed angle so divide by two to get the answer of 44
Problem 7: Correct
Problem 8: Correct
Problem 9: Correct
The steps are below if you are curious
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Problem 7
S = 180*(n-2)
2340 = 180*(n-2)
2340/180 = n-2
13 = n-2
n-2 = 13
n = 13+2
n = 15
I'm using n in place of lowercase s, but the idea is the same. If anything, it is better to use n for the number of sides since S already stands for the sum of the interior angles. I'm not sure why your teacher decided to swap things like that.
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Problem 8
First find y
y+116 = 180
y+116-116 = 180-116
y = 64
which is then used to find x. The quadrilateral angles add up to 180*(n-2) = 180*(4-2) = 360 degrees
Add up the 4 angles, set the sum equal to 360, solve for x
x+y+125+72 = 360
x+64+125+72 = 360 ... substitution (plug in y = 64)
x+261 = 360
x+261-261 = 360-261
x = 99
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Problem 9
With any polygon, the sum of the exterior angles is always 360 degrees
The first two exterior angles add to 264. The missing exterior angle is x
x+264 = 360
x+264-264 = 360-264
x = 96
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
brainly.com/question/16382372
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