Start circle: πd = (3.14)(19) = 59.7
Move diagonally to the circle with the radius of 6.2.
Second circle: 2πr = 2(3.14)(6.2) = 39
Move upwards to the circle with the radius of 10.5
third circle: 2πr = 2(3.14)(10.5) = 66
Move right to the circle with the diameter of 16.6
Fourth circle: πd = (3.14)(16.6) = 52.2
Move down to the circle with the diameter of 7.7
fifth circle: πd = (3.14)(7.7) = 24.2
Move down to the circle with the diameter of 50
Sixth circle: πd = (3.14)(50) = 157.1
Move left to the circle with the radius of 11.8
Seventh circle: 2πr = 2(3.14)(11.8) = 74.1
Move down to the circle with the radius of 38
Eight circle: 2πr = 2(3.14)(38) = 238.8
Move right to the circle with the diameter of 1.1
ninth circle: πd = (3.14)(1.1) = 3.5
Move right to the circle with the radius of 14.8
10th circle = 2πr = 2(3.14)(14.8) = 93
Move up to the end.
Hope this helps :)
Answer:
-2
Step-by-step explanation:
0.5a=2a+3
a=-2
PLEASE MARK BRAINLIEST
Answer:2.75
Step-by-step explanation:
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.
