a scenario during which a local gas station is looking for advice regarding their yearly sales. Sales have been good since they opened in 2012 and the owner, Sally, seems pleased with revenues. She invites you out for working lunch in the hopes of retaining your statistical analysis skill set.
a scenario during which a local gas station is looking for advice regarding their yearly sales. Sales have been good since they opened in 2012 and the owner, Sally, seems pleased with revenues. She invites you out for working lunch in the hopes of retaining your statistical analysis skill set.
You meet for the working lunch and start talking about the business. During the conversation, she slides the following across the table:
| Year | Revenue |
| 2012 | $ 100,000.00 |
| 2013 | $ 133,628.00 |
| 2014 | $ 157,548.92 |
| 2015 | $ 182,822.77 |
| 2016 | $ 213,051.96 |
| 2017 | $ 257,687.23 |
| 2018 | $ 294,797.44 |
| 2019 | $ 339,107.08 |
| 2020 | $ 371,666.08 |
| 2021 | |
| 2022 | |
| 2023 |
You are pleased to see the results of Sally’s hard work paying off—her petrol station is taking off and growing. After a moment of looking at the chart, she asks you to turn on your tablet and figure quick estimates, using
QUESTION:
So, what do you tell her after using the FORECAST.LINEAR() function? Should she expect revenues to grow, decline, or stabilize? And, what are the forecast values for 2021-2023?
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