Here, we just use the following x values and put them into the equation.
y = - 0.05x + 16
y = -0.5(0) + 16
y = 16
y = - 0.05x + 16
y = -0.5(160) + 16
y = -80 + 16
y = -64
y = - 0.05x + 16
y = -0.5(320) + 16
y = - 160 + 16
y = -144
Now, to set up the table, you could list the x values and the y values.
x values :- 0,160, 320
y values:- 16, -64, -144
Answer:
Step-by-step explanation:
Answer: $12,916.70
Step-by-step explanation:
Given the following:
Invoice received :
Bedroom set = 5
Cost per set = $3,000
Chain discount = 5/8/3
Freight cost = $200
If Mel pays within the discount period:
Chain discount given = 5/8/3
Therefore, net equivalent price rate equals:
(1 - 0.05) × (1 - 0.08) × (1 - 0.03) =
0.95 × 0.92 × 0.97 = 0.84778
Net price = total cost × 0.84778
($3000 × 5) × 0.84778
$15000 × 0.84778 = $12,716.7
Net equivalent price + FOB Shipping
$12,716.7 + $200 = $12,916.70
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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slope intercept form
y=mx+b
x-y =2
we need to subtract x from each side
x-y-x = -x +2
-y = -x+2
multiply each side by -1
-1*-y = -1(-x+2)
y = x -2
the slope intercept form is
y = x-2
