Year READ THIS -----> Sales 2019 $ 56,745 2018 $ 51,407 2017 $ 43,396 2016 $ 42,401 2015 $ 39,244 Cost of Sales 25,920 23,343 20,438 20,005 19,831 DO NOT MODIFY THIS 0 01) Summary Ouput 012) Analysis of Results 03)a Chart 03/b Chart has Trend Line 0|4) Recalculate COGS Run a regression analysis to determine next year's Cost of Sales (Cost of Goods Sold) where Sales is the Independent Variable. Set the output to cell A9. Then use the results to calculate the cost of Sales. Enter your formula for this below. SUMMARY OUTPUT Next provide 3 reasons why this is or is not a good regression (that the correlation is relevant). Regression Statistics Multiple R 0.985864517 R Square 0.971928845 Adjusted R Square 0.962571794 Standard Error 514.1514642 Observations 5 Graph the Sales and Cost of Sales as an X-Y Scatter Chart and include the Trendline and formula on the graph. ANOVA dl Regression Residual Total 1 3 4 SS 27458550.02 793055,1845 28251605.2 MS F Significance F 27458550.02 103.8712711 0.002013153 264351.7282 2020 Sales Cost Of Sales $59,841.00 $26,703.96 Intercept Sales Coefficients 4963.140378 0.363309783 Standard Error 1678.375664 0.035647524 Stat P-value Lower 95% Upper 95% 2.957109355 0.059682431 -378.200054 10304.4808 10.19172562 0.002013153 0.249863452 0.47675611 Lower 95.0% Upper 95.0% -378.200053510304,4808 0.249863452 0.47675611 RESIDUAL OUTPUT PROBABILITY OUTPUT Sales Line Fit Plot Observation 30,000 flicted Cost of S 1 25579.15399 2 23639.80637 3 20729.3317 4 20367.83847 519220.86948 Residuals 340.8460141 -296.8063669 -291.3316992 -362.8384656 610.1305177 Percentie 10 30 50 70 90 Cost of Sales 19831 20005 20438 23343 25920 25,000 y = 0.3633x + 49631 20,000 $15,000 Answer Yes -Cost of Sales +Predicted Cost of Sales Linear (Predicted Cost of Sales) 3 reasons why this is or is not a good regression (that the correlation is relevant). Reason 1 R square>95% Reason 2 t stat>2 Reason 3 P value