Answer:
Step-by-step explanation:
Remark
The measurement of the arc is 3Pi
that represents 108 / 360 of the circle.
Equation
108/350 2 * pi * R = 3* pi
Solution
Pi is on both sides of the equation. They both cancel.
108/360 = 0.3
0.3 * 2 * R = 3
0.6 * R = 3
R = 3/0.6
R = 5
Let us assume the larger number = x
Let us assume the smaller number = y
Then
x + y = 3 3/4
x + y = 15/4
And
x/3 = (2y/3) + 1/2
x = [3 * (2y/3)] + (3/2)
= 2y + (3/2)
Now putting the value of x from the second equation to the first , we get
x + y = 15/4
2y + (3/2) + y = 15/4
3y = (15/4) - (3/2)
3y = (15 - 6)/4
3y * 4 = 9
12y = 9
y = 9/12
= 3/4
Now putting the value of y in the first equation, we get
x + y = 15/4
x + (3/4) = (15/4)
x = (15/4) - (3/4)
= (15 - 3)/4
= 12/4
= 3
So the value of x or the larger number is 3 and the value of y or the smaller number is 3/4.
Answer:
You take turns plugging in each number for your answers. I’ll list them below.
6(0) - (0)2 = 0
6(1) - (1)2 = 4
6(2) - (2)2 = 8
6(3) - (3)2 = 12
6(4) - (4)2 = 16
6(5) - (5)2 = 20
6(6) - (6)2 = 24
The pattern I’ve noticed is every time you increase the value from the previous meaning for x, the solution increases by 4. I hope this helps and let me know if I’m right.
Answer: C. there is still not enough evidence to conclude that the time series is stationary.
Step-by-step explanation: First thing to note for a time series plot is that it is required to select a suitable forecast method for the data set being considered.
A stationary time series means that the process generating the data set has a constant mean and the variations are constant over time. This means all evidence is present leading to the conclusion that the entire time series is stationary. A stationary time series thus exhibits an horizontal pattern which enables an appropriate forecast method to be selected for this type of pattern.
A horizontal pattern of a time series plot indicates that a data set fluctuates around a constant mean for a period of time. This period of time may however not be the entire time of the time series or take the entire data set into consideration and might just be a reflection of a portion of the time series hence why it can not be explicitly considered to be stationary. This means that a horizontal pattern can change into a seasonal or trending pattern if more variables/data are added over time.
For instance, a manufacturer sells a certain amount of products over a 10 week period and the resulting pattern of a time series plot is horizontal, then from the 11th week to the 15th week he gets a sharp and continuous increase in sales. This change in level will therefore change the time series plot from horizontal to trending making it more difficult to select a suitable forecast method.
