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AutoSumectly and tage Noup Math Lod M Name de In One Manager – Catai 2 -123456731 C D G M 2 sales 14 13 49.5 1072 1745 2145 Forecast 1 4 9 10 25 30 43.64 SN 01 10 1 Year Sales Revenue in Millions 5) 2 1 14 2 6.9 3 16,5 4 26.11 5 34.9 Y 6 101 7 21 1250 TO SUMMARY OUTPUT 11 19 Mogression Statistics 13 Multiple 0.993435800 14 Square 0.086920667 15 Adjusted Square 0.9365014 16 Standard Enor 1.945591111 17 Observations 6 18 19 ANOVA 20 1 21 Regression Sheet1 Sheet + 55 MS Sync 1142.512 101 8250951 64432005 1142.512 PI 3.14 sp01 Type here to search D E 1 . Regression Statistics 3 Multiple R 1 Square 5 Adjusted R Square 5 Standard Error 7 Observations 1 0.993438800 0.986920667 0.983650834 1.945593311 6 ANOVA 1 Regression 2 Residual 3 Total 2 4 SS MS F Significance 1142.512 1142.512 301.8259951 6.44326E-05 15.14133333 3.783333333 1157.653333 5 1 Intercept 7 Year Coefficients 7.346666667 8.08 Standard Error Star P-value Lower 95 Upper 95 Lower 95.0 Upper SON 1811248802 4.056133348 0.015397672 -12.37549954-2317833796 12.37549954 2.317333296 0.465085758 17.373140056.44326E-05 6.788714925 9.371285075678714925 9.371285075 0 2. Referring to above problem, we wonder if there is a better way to forecast SolarZ sales revenue for year 7. Assume we collected data on All Industry sales revenue over the past 6 years (all companies producing solar batteries). The results are shown below. 1 Year 2 3 4 5 6 1.4 6.9 16.5 26.8 34.9 39.1 Sales Revenue (in Million $) All Industry Sales (in Billion $) 5.6 9.6 12.7 14.8 17.5 20.4 a. Perform a regression analysis between the annual sales of SolarZ and all industry sales (simple regression). Show the scatter diagram and find the forecast for next year’s sales of SolarZ if the estimate of next year’s all industry sales is $22.9 billion. b. Which forecast– the time series forecast from problem 1, or the forecast from this problem — seems to be “better”? Explain why it is better.