Instructions Choose the Moving Average or Exponential Smoothing method to complete a time series forecast on one of the two datasets provided and predict for the January 2023 period Complete a multiple regression model (using three independent variables from those available) on the SAME dataset as in 1, in Microsoft Excel using the data analysis pack and predict for the January 2023 period Introduction to Multivariate Regression with Microsoft Excel to see how I multiply values against the coefficients) Scroll to bottom of this page for some more optional helpful videos Evaluate the following measures of performance Forecast error (Forecast Error and Absolute Forecast Error See video for explanation) Coefficient of variation R Squared P Values F Statistic Coefficients Evaluate the performance of your regression model and compare it to the results of the time series analysis method you chose I E Use the stuff in 3 Identify what the regression model explains in a way that a business person can understand This should include an explanation of the independent variables and how they relate to the dependent variable Create a report that summarizes your findings and includes a visual representation of your results (e g , a graph or chart) Your report should be well organized and well written, using clear and concise language The report should be submitted in Microsoft Word format and should be no more than two pages in length (excluding the visual representation of your results) Submit your Microsoft Excel (xlsx) file in proper and professional working order (hide unnecessary sheets, clean up sheets that you may have 'tried' or 'experimented' with ideas, etc ) Your work will be evaluated based on the accuracy of your calculations, the quality of your analysis and interpretation of the results, and the clarity and organization of your report and Microsoft Excel file You work should be professional, honoring data ink ratio, including file name considerations Month Sales Revenue Advertising Spend Sales Employees Price Per Unit Competitor Price Temperature Selling Days January 2021 $ 48,922 00 $ 3,586 30 12 11 99 13 99 19 22 February 2021 $ 48,640 00 $ 2,131 00 11 11 99 13 99 38 23 March 2021 $ 56,023 00 $ 7,867 50 15 9 99 13 99 50 22 April 2021 $ 78,492 00 $ 9,730 40 16 9 99 13 99 66 23 May 2021 $ 36,237 00 $ 3,825 60 18 11 99 9 99 78 23 June 2021 $ 51,668 00 $ 7,879 50 8 11 99 13 99 80 20 July 2021 $ 55,954 00 $ 8,745 70 19 11 99 13 99 88 20 August 2021 $ 42,206 00 $ 3,636 40 11 11 99 13 99 85 21 September 2021 $ 66,072 00 $ 7,408 30 8 11 99 13 99 86 22 October 2021 $ 79,983 00 $ 8,116 80 8 11 99 13 99 56 23 November 2021 $ 33,283 00 $ 1,434 90 16 11 99 9 99 23 23 December 2021 $ 47,493 00 $ 7,239 60 9 11 99 13 99 13 20 January 2022 $ 47,471 00 $ 5,532 00 13 14 99 14 99 19 22 February 2022 $ 48,417 00 $ 5,705 60 20 14 99 14 99 38 23 March 2022 $ 60,371 00 $ 6,828 60 18 14 99 14 99 60 22 April 2022 $ 63,044 00 $ 7,783 70 15 14 99 14 99 70 23 May 2022 $ 72,454 00 $ 9,778 70 5 14 99 14 99 77 23 June 2022 $ 69,161 00 $ 9,595 30 15 14 99 14 99 80 20 July 2022 $ 42,950 00 $ 6,204 10 18 14 99 14 99 78 20 August 2022 $ 66,831 00 $ 8,387 20 5 14 99 14 99 75 21 September 2022 $ 42,446 00 $ 6,977 20 12 14 99 14 99 68 22 October 2022 $ 68,859 00 $ 9,064 60 8 14 99 14 99 65 23 November 2022 $ 53,489 00 $ 4,431 00 11 14 99 13 99 16 23 December 2022 $ 50,263 00 $ 4,054 70 13 14 99 13 99 23 20

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Choose the Moving Average or Exponential Smoothing method to complete a time series forecast on one of the two datasets provided and predict for the January 2023 period.
Complete a multiple regression model (using three independent variables from those available) on the SAME dataset as in #1, in Microsoft Excel using the data analysis pack and predict for the January 2023 period. Introduction to Multivariate Regression with Microsoft Excel to see how I multiply values against the coefficients) Scroll to bottom of this page for some more optional helpful videos.
Evaluate the following measures of performance:

Forecast error (Forecast Error and Absolute Forecast Error ~ See video for explanation)
Coefficient of variation
R-Squared
P-Values
F-Statistic
Coefficients

Evaluate the performance of your regression model and compare it to the results of the time series analysis method you chose. I.E. Use the stuff in #3.
Identify what the regression model explains in a way that a business person can understand. This should include an explanation of the independent variables and how they relate to the dependent variable.
Create a report that summarizes your findings and includes a visual representation of your results (e.g., a graph or chart). Your report should be well-organized and well-written, using clear and concise language.
The report should be submitted in Microsoft Word format and should be no more than two pages in length (excluding the visual representation of your results).
Submit your Microsoft Excel (xlsx) file in proper and professional working order. (hide unnecessary sheets, clean up sheets that you may have 'tried' or 'experimented' with ideas, etc...).
Your work will be evaluated based on the accuracy of your calculations, the quality of your analysis and interpretation of the results, and the clarity and organization of your report and Microsoft Excel file.
You work should be professional, honoring data-ink ratio, including file name considerations.