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Quarterly Sales of a Department Store for the Last Seven

question 40

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Quarterly sales of a department store for the last seven years are given in the following table. Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β0 + β1Qtr1 + β2Qtr2 + β3Qtr3 + β4t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? t = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? t = Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? t × Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? t with Quarterly sales of a department store for the last seven years are given in the following table. The scatterplot shows that the quarterly sales have an increasing trend and seasonality. A linear regression model given by Sales = β<sub>0</sub> + β<sub>1</sub>Qtr1 + β<sub>2</sub>Qtr2 + β<sub>3</sub>Qtr3 + β<sub>4</sub>t + ε, where t is the time period (t = 1, ..., 28) and Qtr1, Qtr2, and Qtr3 are quarter dummies, is estimated and then used to make forecasts. For the regression model, the following partial output is available. Using MSE and MAD, compare the linear trend equation with seasonal dummy variables, <sub>t</sub> = 31,9261 - 7.855Qtr1 - 4.7362Qtr2 - 7.1656Qtr3 + 1.0749t, and the decomposition method equation <sub>t</sub><sub> </sub>= <sub>t </sub>× <sub>t</sub><sub> </sub>with t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended? t = 26.8819 + 1.0780t and the quarterly seasonal indices: 0.9322, 1.0066, 0.9441, and 1.1171. Which of the two corresponding forecasting models is recommended?

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