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Learn about time series ARIMA models in SPSS with data from the USDA Feed Grains Database (1876-2015) / the Odum Institute.

Av: Materialtyp: TextUtgivningsuppgift: London : SAGE Publications, Ltd., 2017Beskrivning: 1 online resource : illustrationsInnehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9781473995598 (online resource) :
Ämnen: DDK-klassifikation:
  • 519.55
Onlineresurser: This dataset example introduces researchers to autoregressive integrated moving average (ARIMA) models for a single time series variable. An ARIMA model is a statistical model used to estimate the temporal dynamics of an individual times series. ARIMA models are frequently used for forecasting future values of the time series in question. This example uses a subset of data from the United States Department of Agriculture (USDA) Database. It examines the temporal dynamics in annual oats yield per acre in bushels from 1876 to 2015. Understanding temporal dynamics in grain yields could help policy makers, farmers, and economists make better forecasts of future yields. The sample dataset used for this example has been cleaned and organized to make this example easier to follow. Interested readers should read the full documentation for the dataset before using it for research (https://www.ers.usda.gov/data-products/feed-grains-database.aspx).Direct Prerequisites: Time Series ACFs and PACFs
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This dataset example introduces researchers to autoregressive integrated moving average (ARIMA) models for a single time series variable. An ARIMA model is a statistical model used to estimate the temporal dynamics of an individual times series. ARIMA models are frequently used for forecasting future values of the time series in question. This example uses a subset of data from the United States Department of Agriculture (USDA) Database. It examines the temporal dynamics in annual oats yield per acre in bushels from 1876 to 2015. Understanding temporal dynamics in grain yields could help policy makers, farmers, and economists make better forecasts of future yields. The sample dataset used for this example has been cleaned and organized to make this example easier to follow. Interested readers should read the full documentation for the dataset before using it for research (https://www.ers.usda.gov/data-products/feed-grains-database.aspx).Direct Prerequisites: Time Series ACFs and PACFs

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