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Step2247 [10]
3 years ago
6

What is the difference written in scientific notation?

Mathematics
1 answer:
lara31 [8.8K]3 years ago
4 0

Answer:6.2 x 10 to the 5 power

Step-by-step explanation:

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Can someone help me with my home work and add explanation if u can ill give up to 120. points if there right, this is one of the
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Answer:

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Step-by-step explanation:

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ricardo purchased a bunk bed for his children fora sale price of $276.25. The sale price was 65% of the original price. Calculat
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Use the formula S=P-PD

S is sale price, original price is P, and PD is discounted percent,

276.25=P-P(.65), I turned the percent into a decimal.

276.25=P(1-.65)

276.25=P(1-.65)

276.25=P(.35)

Now cross multiply.

\frac{276.26}{.35}=\frac{P(.35)}{.35}\\\\P=\frac{276.26}{.35}=789.31

Hope this helps, now you know the answer and how to do it. Stay healthy and safe and HAVE A BLESSED AND WONDERFUL DAY! :-)  

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If I have 9 employees and only 5 of them worked. What is my percentage
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What is the slope intercept form of the equation of 6x+5y=-15
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Slope intercept form is \sf y=mx+b, where 'm' is the slope and 'b' is the y-intercept.

\sf 6x+5y=-15

Subtract 6x to both sides:

\sf 5y=-6x-15

Divide 5 to both sides:

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tensor-on-tensor regression: riemannian optimization, over-parameterization, statistical-computational gap and their interplay
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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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2 years ago
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