Predicted and observed wine sales for individual states. حسب Yingjie Hu (1579000)

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Feature importance of the numeric input variables in the optimized RF models for the estimation of spirits, wine, and beer sales. حسب Yingjie Hu (1579000)

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The architecture of the DNN model for estimating alcohol sales. حسب Yingjie Hu (1579000)

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Fig 7 - حسب Yingjie Hu (1579000)

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RMSE values of including and not including grocery store visits for alcohol sales prediction based on the RF model. حسب Yingjie Hu (1579000)

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Four categories of input features for the machine learning models. حسب Yingjie Hu (1579000)

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VIF values of the variables in three MLR models. حسب Yingjie Hu (1579000)

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Geographic differences for how the per capita visits to liquor stores changed in response to COVID-19. حسب Yingjie Hu (1579000)

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Change in per capita visits to drinking places in each state from March to June in 2020. حسب Yingjie Hu (1579000)

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Fig 15 - حسب Yingjie Hu (1579000)

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Fig 5 - حسب Yingjie Hu (1579000)

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Change in per capita visits to wineries in each state from March to June in 2020. حسب Yingjie Hu (1579000)

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Fig 2 - حسب Yingjie Hu (1579000)

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Geographic differences for how the per capita sales of spirits changed during the pandemic. حسب Yingjie Hu (1579000)

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Wine sales in each of the 13 states. حسب Yingjie Hu (1579000)

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Visitation behavior to the four types of alcohol outlets in the U.S. during the first months of COVID-19. حسب Yingjie Hu (1579000)

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Performances of the three machine learning models based on ten-fold cross-validation and walk-forward validation: Multiple linear regression (MLR), random forest (RF), and a deep n... حسب Yingjie Hu (1579000)

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Predicted and observed beer sales for individual states. حسب Yingjie Hu (1579000)

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Beer sales in each of the 11 states. حسب Yingjie Hu (1579000)

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Geographic differences for how the per capita sales of beer changed during the pandemic. حسب Yingjie Hu (1579000)

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