We investigate crop yields in a developing country. We model the crop yield (yie
ID: 3126762 • Letter: W
Question
We investigate crop yields in a developing country. We model the crop yield (yield) of family owned farms. Some of the farms receive money from a program that teaches sustainable argriculture. • The dummy variable money takes the value 1 for farms that receive money and the value 0 for farms that do not receive money. • The dummy variable nomoney takes the value 1 for farms that do not receive money and the value 1 for farms that receive money. Farms grow cash crops (crops that are sold for profit) and subsistence crops (crops that are grown for own consumption). Some farms grow both. There are no farms that grow neither.Explanation / Answer
Many times time series data shows seasonility. Inorder to check whether seasonility is present or not first one has to remove the trend factor from data by simple differeniation which is most common method to remove trend.
Then the seasonility is tested. By seasonality, we mean periodic fluctuations that is whether the output variable shows some ups and downs at certian period and it repeats after some period. Seasonality can repeat on a weekly, monthly or quarterly basis, these periods of time are structured and occur in a length of time less than a year.
For example, retail sales tend to peak for the Christmas season and then decline after the holidays. So time series of retail sales will typically show increasing sales from September through December and declining sales in January and February.
The seasonility can be detected Run sequence plot. That is by plotting the output Y variable along with Y axis and taking index as x axis. If the plot shows some pattern like ups and downs and repeatinon of the patterns for certain period then we can say the seasonality is present in the data.
Another graphical method is Seasonal subseries plot used to detect seasonality.
Seasonal subseries plots are formed by
Also multiple Box plot can be used to detect the seasonality in the data.
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