Using the Best Buy revenue data:
a) Fit the sales data using a linear regression model with a time trend and seasonal dummy variables.
b) Using the output from part a, create a chart that shows the model predictions versus actual sales.
c) Is the time trend statistically significant? How can you tell?
d) Are the seasonal dummy variables statistically significant? How can you tell?
e) Interpret the F statistic to judge whether the regression model as a whole is statistically significant.
f) Using the model that you have created, forecast quarterly revenues for the next year. How do these results compare to those created with the HWAM, HWMM, and Forecast.ETS models?
g) According to FactSet, analysts are forecasting the following revenues for the next year. How do your numbers compare?
a) See regression results above.
b) See chart at left.
c) The time trend (Period) is statistically significant. We know because the t-stat is above 2, the p-value is about 0, and the 95% confidence interval does not include 0.
d) The seasonal coefficients (Q1, Q2, and Q3) are all statistically significant. We know because the t-stats are all above 2 (absolute value), the p-values are about 0, and the 95% confidence intervals don't include 0.
e) The F statistic indicates that the model is statistically significant. However, we don't have another regression model to compare it to, so we can't say if it is better or worse than a different regression model.
f) Forecasts are highlighted in yellow at left. They are lower in all cases than the previous models.
g) The regression model forecasts are considerably lower than analysts forecasts.
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