6 squared is 36 and 5 squared is 25 so if you add them together you get 61.
A proportional equation does not have a Y-intercept greater than 0.
Answer:
Height: 3/2 inches
Length: 12 inches
Width: 4 inches
Step-by-step explanation:
Let x is the side length of the square
The height of the box by cutting squares off :x
- The new length of the cardboard = 15 -2x (because we cut from 4 corners)
- The new width of the cardboard = 7 -2x (because we cut from 4 corners)
The new volume of it is:
V = (15 -2x) (7 -2x) x
<=> V = 
To maximum volume, we use the first derivative of the volume
<=> 
<=> 
<=> 2x -3 = 0 or 6x -35 = 0
<=> x = 3/2 or x = 35/6
To determine which value of x gives a maximum, we evaluate
= 24x -88
= 24(3/2) -88 = -52
= 24(35/6) -88 = 52
We choose x = 3/2 to have the maximum volume because the value of x that gives a negative value is maximum.
So the dimensions (in inches) of the box is:
Height: 3/2 inches
Length: 15-2(3/2) = 12 inches
Width: 7 - 2(3/2) = 4 inches
The answer is 8/17 because you have to change the fractions to improper fractions then change the division sign to a multiplication sign. Then change the second fraction by flipping the two numbers then multiply and reduce.
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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