Use multi step equation method
add five by both sides
then divide negative 5 and you get 2
the answer is two
Answer:
5
Step-by-step explanation:
distribute the negative
5*3 + 5(-2)
15-10
5
Answer:
35w^3 + 44w
<em><u>Combine Like Terms:</u></em>
= 35w + 9w + 35w^3 + 25w^3 - 25w^3
= (35w^3 + 25w^3 - 25w^3) +(35w + 9w)
= 35w^3 + 44w
Hope this is right ♡
This is an equilateral triangle, which is a triangle that has 3 congruent/equal sides and 3 congruent angles.
To find "x", you can set the sides equal to each other because they are suppose to be the same length (you can just do two sides because all of the sides are the same)
[Side AB = Side BC]
4x - 10 = 3x + 2 Subtract 3x on both sides
x - 10 = 2 Add 10 on both sides
x = 12
[proof]
Side AB:
4x - 10 Plug in 12 for x
4(12) - 10 = 48 - 10 = 38
Side BC:
3x + 2 Plug in 12 for x
3(12) + 2 = 36 + 2 = 38
Side AC:
5x - 22 Plug in 12 for x
5(12) - 22 = 60 - 22 = 38
This is also an equilateral triangle (the tick marks show that the sides are the same)
A triangle is 180°. So the three angles add up to 180°.
Since this is an equilateral triangle, all the angles should be the same.
Each angle is 60°
[60° + 60° + 60° = 180° or you could have divided 180 by 3 = 60]
Now that you know each angle is 60°, you can do:
(2x - 4)° = 60°
2x - 4 = 60 Add 4 on both sides
2x = 64 Divide 2 on both sides
x = 32
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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