Answer:
(3,4)
I'm assuming this is how u wanted it to be solved if not give me more info on the problem and I'll redo it
Step-by-step explanation:
x= 3,y = 4
Answer:
16%
Step-by-step explanation:
20/125 = 0.16
0.16 x 100 = 16% (multiply by 100 to get a percentage)
Some periodic motions, also known as armonic motions, with which much of us are familiar with are:
- The motion of the swinger when a kid is balancing forward and backward
- The motion of a pendulum
- The motion of the needles of a watch.
- The motion of a satelite around a planet.
- The spinning of the wheel of a stationary bycicle.
- A rocking chair
All the motions in which the object passes once and other times through the same point are periodic motions.
Both scientists and businesses are interested in tracking periodic motions using equations because they appear in many situations in nature and in daily life. The cycles are examples of periodic motion. By tracking this type of motion you can make models that permit you to explain the phenomena and predict cycles. This is predict facts that repeast with a certain period.
I think I know what you are thinking and you are halfway on the right track. : ) You are probably looking at the x-axis right in between the pentagon and its reflection. This <em />is the right line, but it is a y-line and not x. When we say a line is at x = 0, we mean that every point on that line has an x-value of 0, such as (0,1), (0,2), (0, 3) etc. Any line in the form x = 0 (or any other number) is a vertical line. Y-lines are the same way but they run horizontal like the line of reflection in this problem. : )
Answer:
1/3
Step-by-step explanation:
Let A be the event that you grab the fair coin and B be the event that you toss a tail.
P(A) is the probability that you grab the fair coin, which is 1/3
P(B) is the probability that you toss a tail, which is 1/2
P(B|A) is the probability that you toss a tail, given that you grab a fair coin, which is 1/2
P(A|B) is the probability that you grab the fair coin, given that you toss a tail, which we are looking for.
Using Bayes probability theorem we have:
