1 1 I 4.6% 1 Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Revenue $75.9 $152.8 $270.4 $500.6 $682.2 $996.7 $1,205.3 $1,364.7 $1,670.3 $2,162.6 $3,204.6 $3,609.3 $4,374.6 $5,504.7 $6,779.5 $8,830.7 $11,692.7 $15,794.3 $20,156.4 $24.996.1 GDP 10581.8214 10936.41905 11458.24388 12213.72915 13036.64023 13814.61141 14451.85866 14712.84408 14448.93303 14992.05273 15542.5811 16197.00735 16784.8492 17527.1637 18238.30057 18745.07569 19542.97918 20611.86093 21433.2247 20936.6 % Change Revenue 111.5% 101.3% 77.0% 85.1% 36.3% 46.1% 20.9% 13.2% 22.4% 29.5% 48.2% 12.6% 21.2% 25.8% 23.2% 30.3% 32.4% 35.1% 27.6% 24.0% 1 1 Lag % Change Revenue % Change in GDP Naive Forecast Model A Forecast % Diff Naive % Diff Model A 617.0% 3.2% 111.5% 3.4% 101.3% 4.8% 77.0% 6.6% 85.1% 6.7% 36.3% 6.0% 46.1% 1 20.9% 1.8% 1 13.2% -1.8% 22.4% 3.8% 29.5% 3.7% 48.2% 4.2% 12.6% 3.6% 21.2% 4.4% 1 25.8% 4.1% 1 23.2% 2.8% 1 30.3% 4.3% 32.4% 5.5% 35,1% 4.0% 27.6% -2.3% Average St. Dev. 1 1 1 I 1 1 Above is cenual revenue data for Netflix and GDP for the US. You decide to test two regression forecasting models for the change in revenue for Netflix The Mode A forests the disege in revenue as a function of the percent diege in revenue in the previous year, Model B forecasts the disege in recue as la function of the percent change in revenue in the previous yee and the percent change in GDP. i Which ode(A Bither ble models w2 Your colleague suggeste model, forcing revenue (not the percent chege in revenue) function of GDP (not the change in GDP. Pulspotential with the Test 2 Use these tipproached the repression model ipproach using Model A to fore the percent chutege is revenue for Nedit. Dette which of the two models is the most count within sepiest Model A SUMMARY OUTPUT Regression Statistics Multiple R 0.700573805 R Square 0.490803656 Adjusted R Square 0.46251497 Standard Error 0.212977369 Observations 20 ANOVA dr MS F 17.34982174 Significance 0.000581346 Regression Residual Total 1 18 19 0.786976806 0.04535936 SS 0.786976806 0.816468475 1.603445281 Coefficients Intercept 0.30239721 Lag % Change Rever 0.154486992 Standard Emor 0.054388818 0.037088939 Star 5.559915055 4.165311722 P-value 2.80977E-05 0.000581346 Lower 95% 0.188130543 0.076566023 Upper 959 0.416663877 0.232407961 Lower 95.0% Upper 25.09 0.188130543 0.416663877 0.076566023 0.232407961 Model B SUMMARY OUTPUT Regression Statistics Multiple R 0.731225966 R Square 0.534691413 Adjusted R Square 0.479949226 Standard Error 0.20949475 Observations 20 ANOVA df MS F 9.767447096 Significance F 0.001498991 Regression Residual Total 2 17 19 SS 0.857348422 0.746096858 1.603445281 0.428674211 0.04388805 Intercept Lag % Change Rever % Change in GDP Coefficients 0.206891443 0.152346701 2.651489504 Standard Emor 0.092470669 0.036521592 2.093940286 1 Stat 2.237373708 4.171414593 1.266267965 P-value 0.038944682 0.00063992 0.222494138 Lower 95% 0.011795386 0.075292877 -1.76633833 Upper 95% 0.4019875 0.229400524 7.069317338 Lower 95.0% Upper 95.0% 0.011795386 0.4019875 0.075292877 0.229400524 -1.76633833 7.069317338 1 1 I 4.6% 1 Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Revenue $75.9 $152.8 $270.4 $500.6 $682.2 $996.7 $1,205.3 $1,364.7 $1,670.3 $2,162.6 $3,204.6 $3,609.3 $4,374.6 $5,504.7 $6,779.5 $8,830.7 $11,692.7 $15,794.3 $20,156.4 $24.996.1 GDP 10581.8214 10936.41905 11458.24388 12213.72915 13036.64023 13814.61141 14451.85866 14712.84408 14448.93303 14992.05273 15542.5811 16197.00735 16784.8492 17527.1637 18238.30057 18745.07569 19542.97918 20611.86093 21433.2247 20936.6 % Change Revenue 111.5% 101.3% 77.0% 85.1% 36.3% 46.1% 20.9% 13.2% 22.4% 29.5% 48.2% 12.6% 21.2% 25.8% 23.2% 30.3% 32.4% 35.1% 27.6% 24.0% 1 1 Lag % Change Revenue % Change in GDP Naive Forecast Model A Forecast % Diff Naive % Diff Model A 617.0% 3.2% 111.5% 3.4% 101.3% 4.8% 77.0% 6.6% 85.1% 6.7% 36.3% 6.0% 46.1% 1 20.9% 1.8% 1 13.2% -1.8% 22.4% 3.8% 29.5% 3.7% 48.2% 4.2% 12.6% 3.6% 21.2% 4.4% 1 25.8% 4.1% 1 23.2% 2.8% 1 30.3% 4.3% 32.4% 5.5% 35,1% 4.0% 27.6% -2.3% Average St. Dev. 1 1 1 I 1 1 Above is cenual revenue data for Netflix and GDP for the US. You decide to test two regression forecasting models for the change in revenue for Netflix The Mode A forests the disege in revenue as a function of the percent diege in revenue in the previous year, Model B forecasts the disege in recue as la function of the percent change in revenue in the previous yee and the percent change in GDP. i Which ode(A Bither ble models w2 Your colleague suggeste model, forcing revenue (not the percent chege in revenue) function of GDP (not the change in GDP. Pulspotential with the Test 2 Use these tipproached the repression model ipproach using Model A to fore the percent chutege is revenue for Nedit. Dette which of the two models is the most count within sepiest Model A SUMMARY OUTPUT Regression Statistics Multiple R 0.700573805 R Square 0.490803656 Adjusted R Square 0.46251497 Standard Error 0.212977369 Observations 20 ANOVA dr MS F 17.34982174 Significance 0.000581346 Regression Residual Total 1 18 19 0.786976806 0.04535936 SS 0.786976806 0.816468475 1.603445281 Coefficients Intercept 0.30239721 Lag % Change Rever 0.154486992 Standard Emor 0.054388818 0.037088939 Star 5.559915055 4.165311722 P-value 2.80977E-05 0.000581346 Lower 95% 0.188130543 0.076566023 Upper 959 0.416663877 0.232407961 Lower 95.0% Upper 25.09 0.188130543 0.416663877 0.076566023 0.232407961 Model B SUMMARY OUTPUT Regression Statistics Multiple R 0.731225966 R Square 0.534691413 Adjusted R Square 0.479949226 Standard Error 0.20949475 Observations 20 ANOVA df MS F 9.767447096 Significance F 0.001498991 Regression Residual Total 2 17 19 SS 0.857348422 0.746096858 1.603445281 0.428674211 0.04388805 Intercept Lag % Change Rever % Change in GDP Coefficients 0.206891443 0.152346701 2.651489504 Standard Emor 0.092470669 0.036521592 2.093940286 1 Stat 2.237373708 4.171414593 1.266267965 P-value 0.038944682 0.00063992 0.222494138 Lower 95% 0.011795386 0.075292877 -1.76633833 Upper 95% 0.4019875 0.229400524 7.069317338 Lower 95.0% Upper 95.0% 0.011795386 0.4019875 0.075292877 0.229400524 -1.76633833 7.069317338