I'm guessing on the make up of the matrices.
First off let's look at [C][F].
[C]=
[F]=
[C][F]=
where each element of [C][F] comes from multiplying a row of [C] with a column of [F].
Example: First element is product of first row and first column.
.
.
.
Now that we have [C][F], we can subtract it from [B], element by element,
[B]-[C][F]=
[B]-[C][F]=
.
.
.
If this is not how the matrices look,please re-state the problem and be more specific about the make up of the matrices (rows x columns).
Here's an example.
[A] is a 2x2 matrix. A=[1,2,3,4].
The assumption is that [A] looks like this,
[A]=
[B] is a 3x2 matrix. B=[5,6,7,8,9,10]
[B]=
<span>This is the term used to describe economic systems in which the basic economic questions are answered based on a socially, pre-established way.</span>
Reliable causal inference based on observational studies is seriously threatened by unmeasured confounding.
What is unmeasured cofounding?
- By definition, an unmeasured confounder is a variable that is connected to both the exposed and the result and could explain the apparent observed link.
- The validity of interpretation in observational studies is threatened by unmeasured confounding. The use of negative control group to reduce unmeasured confounding has grown in acceptance and popularity in recent years.
Although they've been utilised mostly for bias detection, negative controls have a long history in laboratory sciences and epidemiology of ruling out non-causal causes. A pair of negative control exposure and outcome variables can be utilised to non-parametrically determine the average treatment effect (ATE) from observational data that is vulnerable to uncontrolled confounding, according to a recent study by Miao and colleagues.
Reliable causal inference based on observational studies is seriously threatened by unmeasured confounding.
Learn more about unmeasured confounding here:
brainly.com/question/10863424
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12/15
Divide 12 by 15 and then you should get 0.8
0.8 is obviously equal to 8/10ths or 80%
Hope this helps :)
Answer:

Step-by-step explanation:



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