Answer:
48 ft³
Step-by-step explanation:
To find the height, you can use the slant height and the base.
Using the Pythagorean Theorem and 3 as the triangle's base,
a²+b²=c²
3²+b²=5²
9+b²=25
b²=16
b=4
The height of the pyramid is 4.
Volume formula for the pyramid is 
Inserting our measurements:
Answer:
Step-by-step explanation:
Surface area = lateral area + 2(area of base)
Lateral area = perimeter of base * height.
Because it is a isosceles right triangle, both sides are equal.
= 72
2
= 72. Divide both sides by 2
= 36. Square both sides.
x = 6.
So the perimeter of the base = 6 + 6 +
= 20.485281374239
Lateral area = 20.485281374239 * 7 = 143.397 
Area of base is (1/2)base * height.
(1/2)(6)(6) = 18
Using the surface area formula
surface area = 143.397 + 2(18) = 179.4 
Answer:
1.47 mm
Step-by-step explanation:
In week 1 there was 2.6 mm
In week 4 there was 4.07
You want to find the difference of week 1 and 4 meaning you have to subtract week 4's value from week 1's to find how much more rain there was in week 4 than week 1
You would do 4.07-2.6
This is equal to 1.47
THEREFORE there was 1.47 mm more rain in week 4 than week 1
Answer:
2,75in
4,25in
33in²
Step-by-step explanation:
shorter base is 2,75in
larger base is 4,25in
S=11+10,5+9+2,5=33in²
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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