The 100th customer would be the first to receive both.
20×5=100
50×2=100
Converse (switch p and q)
If an angle is obtuse, then it measures 128°
This is false (a 127° angle is obtuse, but it does not measure 128°)
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Inverse (negations of p and q)
If an angle does not measure 128°, then it is not obtuse
This is false (a 127° angle does not measure 128°, but it is obtuse)
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Contrapositive (negations of p and q, then switch their places)
If an angle is not obtuse, it does not measure 128°
This is true (any 128° is obtuse; no exceptions)
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.
