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aleksley [76]
3 years ago
11

PLEASE HELP ME OUT WITH THIS: *13 POINTS*

Mathematics
2 answers:
Anika [276]3 years ago
8 0

Answer:b

Step-by-step explanation:

almond37 [142]3 years ago
6 0

Answer:

Step-by-step explanation:

B

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Does anyone know what the √2.25 is when rewritten as a fraction?
Zinaida [17]

Answer:  \sqrt{\frac{9}{4} }

Step-by-step explanation:

This is what it is as a mixed number

\sqrt{2.25}

= \sqrt{2\frac{1}{4} }

= \sqrt{\frac{9}{4} }

If you want it written in exponential form it would look like this:

(\frac{9}{4}) ^{\frac{1}{2} }

5 0
2 years ago
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A 208 yard long road divided into 16 parts of equal legnth. Mr. Ward paints a 4 yard long strip of each part of the road?
balu736 [363]
The answer is 4 hope this helps
7 0
3 years ago
The ratio table below shows the relationship between the number of packages of gum and the total pieces of gum.
tangare [24]

The complete question is

"The ratio table below shows the relationship between the number of packages of gum and the total pieces of gum.

Gum Packages of gum Pieces of gum

1

15

2

30

3

45

4

?

How many pieces of gum are in 4 packages of gum?"

Using a proportional function, there are 60 pieces of gum in 4 packages.

<h3>What is a proportional relationship?</h3>

Two values x  and  y are said to be in a proportional relationship if x=ky, where x and y are variables and k is a constant.

The constant k is called constant of proportionality.

The constant is given by:

k = 15/1

k = 30/2

k  = 45/3

k  = 15.

Therefore, the number of pieces of gums in x packages is given by:

y = 15x.

In 4 packages:

y = 15 x 4

y = 60 pieces of gum.

Using a proportional function, there are 60 pieces of gum in 4 packages.

More can be learned about proportional functions at brainly.com/question/10424180

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4 0
2 years ago
tensor-on-tensor regression: riemannian optimization, over-parameterization, statistical-computational gap and their interplay
Contact [7]

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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7 0
2 years ago
What is the domain and range of this graph?
Mama L [17]
Domain: (-1, 5)
Range: (-1, 3)

Explanation:
The domain covers the lowest and highest x value. You take the lowest number for the x and the highest number and that’s your domain. The range is covering y values, so you look at the lowest and highest point on your graph
6 0
3 years ago
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