267 x 0.02 = 5.34
267 + 5.34 = 272.34
267 x 0.08 = 21.36
267 - 21.36 = 245.64
267 + 272.34 + 245.64 = $784.98
Answer:
Use the equation
Step-by-step explanation:
y = mx + b
Answer:
Test statistic, 
(to 3 dp)
Step-by-step explanation:
Deviation, d = x -y
Sample mean for the deviation
Standard deviation: 
SD =2.93
Under the null hypothesis, the formula for the test statistics will be given by:
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.
