9 and 1/2 is a mixed number
Answer:
3.728 cm²
90
Step-by-step explanation:
To built the envelope I am using a rectangular piece of material 28 m long and 12 m wide.
Out of this material, I have to cut patches that are 1/12 the length and 2/15 the width of the whole material.
So, the length of the patches is
m and the width of the patches is
m.
Therefore, the area of the patches is (1.6 × 2.33) = 3.728 m² each. (Answer)
Now, the area of the rectangular piece of material is (28 × 12) = 336 m²
Hence,
≈ 90 such patches can be made out of the whole material. (Answer)
Answer:
First "order of operations" mistake: step 2
First arithmetic mistake: step 4
Step-by-step explanation:
As we understand Rena's work, she wants to simplify ...

for x = -1 and y = 2.
Her work seems to be ...
<u>Step 1</u>

<u>Step 2</u>

<u>Step 3</u>

<u>Step 4</u>

_____
So, the first arithmetic error is in Step 4. However, the order of operations requires exponents be evaluated first. Doing that makes step 2 look like ...

__
We expect your answer is supposed to be Step 4.
Answer:
y= -3/2x+9
Step-by-step explanation:
Well we have to know that to find a line perpendicular to a given line, you have to have opposite slopes. So that makes the equation begin as y=-3/2x because it's opposite of y=2/3x. Then we have to make sure it goes through the point (4,3). To do that, I tweaked the numbers in my graphing calculator and it worked with 9! So the equation is y= -3/2x+9
Step-by-step explanation:
To check out how efficient or accurate a model is, we use the akaike information criterion or the Bayesian. If the AIC or BIC are lower, then this model would be better. They are also used to control for model complexity
Akaike information criterion = 2k-2ln where k is the number of parameter. A higher k gives a higher AIC.
In the real world complex models are discouraged and avoided since
1. They cause data to be over fitted and can capture noise and information from this data.
2. They are complex and therefore difficult to interpret
3. They consume a lot of time and computing them has several inefficiencies.
Using these two as measure of performance, we can select optimal choice of independent variable.
With forward/backward regression, we are able to put new variables in the model or remove from it. The best is the one with lowest AIC.