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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Fig 15 - حسب Yingjie Hu (1579000)

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Fig 5 - حسب Yingjie Hu (1579000)

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Fig 2 - 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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Visits to the four types of alcohol outlets in each of the 16 states. حسب Yingjie Hu (1579000)

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Fig 3 - حسب Yingjie Hu (1579000)

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Model estimates and the recorded per capita sales of spirits in individual states. حسب Yingjie Hu (1579000)

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Differences in the changes of CAR based on multiple variables including circadian rhythms, sleep, stress, and physical activities. حسب Yesol Moon (15322627)

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Ranking of genotypes according to the ideotype for each trait and the percentage difference (increase or decrease) in relation to cv. M-82. حسب Herika Paula Pessoa (13254326)

Seven forecast scenarios. حسب Ryan P. Thombs (9138968)

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