The Vintage Restaurant, on Captiva Island near Fort Myers, Florida, is owned and operated by Karen Payne. The restaurant just completed its third year of operation. Since opening her restaurant, Karen has sought to establish a reputation for the Vintage as a high-quality dining establishment that specializes in fresh seafood. Through the efforts of Karen and her staff, her restaurant has become one of the best and fastest growing restaurants on the island. To better plan for future growth of the restaurant, Karen needs to develop a system that will enable her to forecast food and beverage sales by month for up to one year in advance. Table 17.25 shows the value of food and beverage sales ($1000s) for the first three years of operation. Managerial Report Perform an analysis of the sales data for the Vintage Restaurant. Prepare a report for Karen that summarizes your findings, forecasts, and recommendations. Include the following: A time series plot. Comment on the underlying pattern in the time series. An analysis of the seasonality of the data. Indicate the seasonal indexes for each month, and comment on the high and low seasonal sales months. Do the seasonal indexes make intuitive sense? Discuss. Deseasonalize the time series. Does there appear to be any trend in the deseasonalized time series? Using the time series decomposition method, forecast sales for January through December of the fourth year. Using the dummy variable regression approach, forecast sales for January through December of the fourth year. Provide summary tables of your calculations and any graphs in the appendix of your report. Assume that January sales for the fourth year turn out to be $295,000. What was your forecast error? If this error is large, Karen may be puzzled about the difference between your forecast and the actual sales value. What can you do to resolve her uncertainty in the forecasting procedure?

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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The Vintage Restaurant, on Captiva Island near Fort Myers, Florida, is owned and operated by Karen Payne. The restaurant just completed its third year of operation. Since opening her restaurant, Karen has sought to establish a reputation for the Vintage as a high-quality dining establishment that specializes in fresh seafood. Through the efforts of Karen and her staff, her restaurant has become one of the best and fastest growing restaurants on the island.

To better plan for future growth of the restaurant, Karen needs to develop a system that will enable her to forecast food and beverage sales by month for up to one year in advance. Table 17.25 shows the value of food and beverage sales ($1000s) for the first three years of operation.

Managerial Report

Perform an analysis of the sales data for the Vintage Restaurant. 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. An analysis of the seasonality of the data. Indicate the seasonal indexes for each month, and comment on the high and low seasonal sales months. Do the seasonal indexes make intuitive sense? Discuss.

  3. Deseasonalize the time series. Does there appear to be any trend in the deseasonalized time series?

  4. Using the time series decomposition method, forecast sales for January through December of the fourth year.

  5. Using the dummy variable regression approach, forecast sales for January through December of the fourth year.

  6. Provide summary tables of your calculations and any graphs in the appendix of your report.

Assume that January sales for the fourth year turn out to be $295,000. What was your forecast error? If this error is large, Karen may be puzzled about the difference between your forecast and the actual sales value. What can you do to resolve her uncertainty in the forecasting procedure?

Month
First Year Second Year Third Year
January
242
263
282
February
235
238
255
March
232
247
265
April
178
193
205
Мay
184
193
210
June
140
149
160
July
145
157
166
August
152
161
174
September
110
122
126
October
130
130
148
November
152
167
173
December
206
230
235
Transcribed Image Text:Month First Year Second Year Third Year January 242 263 282 February 235 238 255 March 232 247 265 April 178 193 205 Мay 184 193 210 June 140 149 160 July 145 157 166 August 152 161 174 September 110 122 126 October 130 130 148 November 152 167 173 December 206 230 235
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