Y= 3x-4 Is the rule for table to check .
Take first one
7,17
17= 3(7)-4
17=21-4
17=17 it checks
Answer:
When X=Y every number on either side is the same, for example 1(X)=1(Y)
Step-by-step explanation:
This is because when X=Y the X value isn't altered in any way to equal Y, so they are the same number, hope this helped!
Hello!
As you can see, we have a radius of 6. If we divide, this means that this is 2.5 radians. To convert radians to degrees we use the formula below.

First of all we divide 180 by pi.
180/

≈57.3
Now we multiply by 2.5
57.3(2.5)=143.25°
Note that the angle we see is obtuse, or greater than 90°.
Therefore, ∠<span>θ</span>≈143.25°
Now we need to convert this back into radians. This can be represented by the equation below.

First we divide pi by 180 then multiply by our angle.

/180(143.25)≈2.5
Therefore, our angle theta is about
2.5 radians.
I hope this helps!
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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Answer:
Choose every tenth student entering the school and ask them if they like the dress code or not.
Step-by-step explanation:
This is a question of sampling. We need to make non-biased predictions using non-biased data. We can sample from every tenth student to stay proportional. Sampling many students can collect more data and can help keep the data non-biased.
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