Prepare a report for Karen that summarizes your findings, forecasts, and recommendations. Include the following: 1. A time series plot. Comment on the underlying pattern in the time series. 2. Using the dummy variable approach, forecast sales for January

The study's primary focus is on the company's sales performance over the last three years. There is a predictable pattern when it comes to sales. The highest and lowest points are usually in January and September, respectively. People purchased less food and beverages in the first three months of the year; things began to improve in the fourth quarter. People spent more money in the first three months than they did in the previous three months. In January, sales are higher than in September. This makes sense because tourism in Florida is more common in the winter and less common in September when most schools are closed. According to Nadeau (2020), even though the sales value fluctuates over time, the underlying trend remains consistent over the years under consideration. They are used because they show horizontal and seasonal trends for three years. As a result, sales trends can be used to forecast how things will go in the future. According to Moreo (2021), the dummy variable can be considered an indicator variable, and accurate forecasting entails comparing each month to the entire year. This regression model assists in isolating each month to forecast food and beverage sales at the Vintage Restaurant. The forecasting outcome is adjusted using the sales regression equation, equalling the dummy variable approach. The forecast is valid in this equation and can solve Karen's uncertainty. Sales were expected to be slightly less than $270,000, but they ended up being $295,000. With a forecasted error rate as low as 0.49 percent, Karen is looking at months where actual sales could balance out errors of being too high or too long in the forecasted sales. While the difference may perplex her, the error is minor enough that she won't have to worry about it for the time being. Karen can use this model to forecast food and beverage sales month by month for up to a year ahead of time, and the forecast error is low enough that she can be more confident.