Step-by-step explanation:
x^2+x-6006=0
x^2+78x-77x-6006=0
x(x+78)-77(x+78)=0
(x-77)(x+78)=0
=>x=77 or x=78
Answer:
The dimension of the plot is 30 yd by 20 yd
Step-by-step explanation:
Given;
Area = 600 yd^2
Length = width + 10
l = w + 10 ......1
Area of a rectangular plot is;
Area A = length × width
A = l × w
Substituting equation 1;
A = (w+10) × w
A = w^2 + 10w
600 = w^2 + 10w
w^2 +10w -600 = 0
Solving the quadratic equation;
w = -30 or 20
cannot be negative
w = 20 yd
l = w+10 = 20+10 = 30yd
The dimension of the plot is 30 yd by 20 yd
Hello!
First of all, we can subtract the stretching time. This gives us 20. If we divide by the four laps we get 5 minutes per lap.
Now, one lap is 400 meters (most tracks are), which is equal to 15,748.03 inches. This means it takes her five minutes to walk 15,748.03 inches. This is also equal to 300 seconds, so it takes her 300 minutes per 15,748.03 inches.
But if we round our big inches number to the nearest ten thousandth, we get 16,000, so in a simpler form her pace is 300/16,000. But we need to find in per second. Therefore we will divide by 300 to find how many inches she walks per second. This means she walks about 53.33 inches per second.
I hope this helps!
Answer:
(fg)(-2) = 96
Step-by-step explanation:
Hope this helps
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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