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1st regression analysis

SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.8744

2nd regression analysis

Y UNITS SOLD X1-PRICE X2-TIME SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.973747634 0.948184454 djusted R Suar

1. Analyze the two regression analysis's above and make a recommendation on if the organization should increase, decrease, or retain their pricing and why?

2. What happens to the dependent variable Y if the price X1 decreases in the second regression analysis?

SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.874493978 0.764739718 0.756026374 159.2178137 29 quare ANOVA df MS Significance F 1 2224908.261 2224908.26187.76650338 5.64792E-10 Regression Residual Total 27684458.4291 25350.31219 28 2909366.69 P-yalue Coefficients Standard Error 306.9514925 112.51655382.728056293 0.011064093537.8163911 109.037313411.63886804 9.368377842 5.64792E-10 85.15632881 t Stat Lower 95% Intercept Price
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