Upon initial analyzation of our data, we could easily tell that there was a seasonality trend in the data provided to us. Some additional statistics about the data follow:
• September was the lowest month of sales (each year)
• December was the highest grossing month of sales (each year)
• From Sept. to January, sales would grow (each year)
• From January to Sept., sales would decline (each year)
With this in mind, I then began to dig deeper into the data to find the best forecast models possible.
Since the data showed signs of seasonality, I decided that a linear regression with monthly coefficients would provide an ideal forecasting model. I then had to figure out if there was a trend (from year to year) or no trend at all. To do this, I copied the data and created two separate models, one with a trend and one without:
Although this, in itself, does not provide much information we can begin to see that the data with a trend coefficient might provide a slightly more accurate forecast model.
To understand how excel came up with the forecast model, we can turn to the coefficients it provided.
For regression with a trend, we were provided with the following coefficients to create an equation:
According to the data above, forecasted sales (in tho...
... middle of paper ...
...es of $295,000 came in, we can look at our predicted sales for the fourth year ($286,750) and get a percentage of error. In this case, our percentage of error from actual sales and forecasted sales is 2.80% meaning that actual sales were 2.8% higher then what we forecasted in January. Karen should likely not be alarmed by this as the percentage of error is rather low in this situation. To further resolve her uncertainty however, we can show her the R-squared value of our model (99.4%) and the MSE to put her at ease. We can also state that as the business continues to grow and gains more data, we can provide more accurate models to forecast data. With the forecasted model provided, Vintage restaurant can have a pretty good idea of their best grossing months and try to provide an incentive to customers to raise profits on their lower-grossing months (June – September).
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