Answer:
12300 militers
Step-by-step explanation:
1 milileter = .001 liter
12.3 liters = 100 x 12.3
Answer: D) 99 Points
Step-by-step explanation: Theresa earned scores of 88 points, 84 points, 88 points, and 91 points on four tests
Therefore the totals points are 351
351 is her total score for 4 tests, if she gets an average score of 90 on her fifth test then all we have to do is:
5 x 90 = 450 (The 5 being her fifth test)
450-351 = 99
Option D
Answer:
5
Step-by-step explanation: 5/13 8 48
graph the function, center the center of the circle find the difference between the center and the circle
center at (-7, 7)
sorry I don't know how to find it mathematically. I'll try to figure out later, if you need an mathematically solution.
It should be noted that the relationship is not linear based on the information illustrated as the difference between the numbers aren't the same.
<h3>How to illustrate the information?</h3>
It should be noted that an equation is simply used to illustrate and express the relationship between the variables.
It should be noted that for the relationship to be linear, it's essential that they've the same relationship between them.
When x = 1, y = 4.5. The
When x = 2, y = 6.5
When x = 3, y = 8.5
When x = 4 y = 11.5
It should be noted that there's a difference of 2 at first but between 3 and 4, there is. a difference of 3.
Therefore, the relationship is not linear.
Learn more about equations on:
brainly.com/question/2972832
#SPJ1
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.
