Hi, could someone solve this parametric system?
1 answer:
9514 1404 393
Answer:
(x, y, z) = ((7 -2k)/(-31 +7k), (-29 +7k)/(-31 +7k), 1/(31 -7k))
Step-by-step explanation:
The system can be solved by any of your favorite methods. Treat k as you would any number.
My row reduction software says the solution is ...
(x, y, z) = ((7 -2k)/(-31 +7k), (-29 +7k)/(-31 +7k), 1/(31 -7k))
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Clearly, k ≠ 31/7.
The augmented coefficient matrix is attached.
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We have a 45-45-90 right triangle with the hypotenuse being the length from one corner to the opposite corner.
Multiply 30 ft with the square root of 2 and use a calculator to help give you numerical value to get your answer.
Your answer is the second choice.
Question 2:
The equilateral triangle can be divided into 2 30-60-90 triangles with the longer leg being the altitude. To get half of the side length of the right triangle, divide the altitude by the square root of 3.
The full side length is double of that, which is
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Multiply that by 3 to get the full perimeter.
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We have a 45-45-90 right triangle with the hypotenuse being the length from one corner to the opposite corner.
Multiply 30 ft with the square root of 2 and use a calculator to help give you numerical value to get your answer.
Your answer is the second choice.
Question 2:
The equilateral triangle can be divided into 2 30-60-90 triangles with the longer leg being the altitude. To get half of the side length of the right triangle, divide the altitude by the square root of 3.
The full side length is double of that, which is
Multiply that by 3 to get the full perimeter.
Your answer is the third choice. Hope this helps! :)
Answer:
There is approximately 17% chance of a person not having a disease if he or she has tested positive.
Step-by-step explanation:
Denote the events as follows:
<em>D</em> = a person has contracted the disease.
+ = a person tests positive
- = a person tests negative
The information provided is:
Compute the missing probabilities as follows:
The Bayes' theorem states that the conditional probability of an event, say <em>A</em> provided that another event <em>B</em> has already occurred is:
Compute the probability that a random selected person does not have the infection if he or she has tested positive as follows:
So, there is approximately 17% chance of a person not having a disease if he or she has tested positive.
As the false negative rate of the test is 1%, this probability is not unusual considering the huge number of test done.