Answer:
94 for the first one and 130 for the second
Step-by-step explanation:
Since A=6 and B=4
For the first one
13(6)+ 4(4)
78+16=94
For the second one
(6+4)*13
(10)*13= 130
Answer:
a.) 1.38 seconds
b.) 17.59ft
Step-by-step explanation:
h(t) = -16t^2 + 22.08t + 6
if we were to graph this, the vertex of the function would be the point, which if we substituted into the function would give us the maximum height.
to find the vertex, since we are dealing with something called "quadratic form" ax^2+bx+c, we can use a formula to find the vertex
-b/2a
b=22.08
a=-16
-22.08/-16, we get 1.38 when the minuses cancel out. since our x is time, it will be 1.38 seconds
now since the vertex is 1.38, we can substitute 1.38 into the function to find the maximum height.
h(1.38)= -16(1.38)^2 + 22.08t + 6 -----> is maximum height.
approximately = 17.59ft -------> calculator used, and rounded to 2 significant figures.
for c the time can be equal to (69+sqrt(8511))/100, as the negative version would be incompatible since we are talking about time. or if you wanted a rounded decimal, approx 1.62 seconds.
Can you elaborate on ur question
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.
