The far right would be the correct answer.
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.
Answer:
the inverse of the quadratic function 
is 
Step-by-step explanation:
We need to find inverse of the quadratic function
Let
Replace x and y
Now, we will solve for y
Adding 3 on both sides
Taking square root on both sides
Now replace y with 
So, the inverse of the quadratic function is
The answer is 43 when you add 29 back to 57 then divide by 2
