Generate and explain the derivation of insights using regression analysis and its associated visualization. Differentiate between the signals identified by business analytics and the noise that is inherent in the system.

SUMMARY OUTPUT Regression Statistics Multiple R 0.248625 R Square 0.061815 Adjusted R Sque -0.032 Standard Error 3.812356 Observations 12 ANOVA df SS MS F Significance F Regression 1 9.576115842 9.576116 0.658874333 0.435852855 Residual 10 145.3405508 14.53406 Total 11 154.9166667 Coefficient: Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.961044 4.596238954 1.514509 0.160845195 -3.28001492 17.20210225 -3.28001492 17.20210225 Flight Time 2.726943 3.359501282 0.811711 0.435852855 -4.758492044 10.21237862 -4.758492044 10.21237862Airplanes Folds Flight Time 1 7 1.2 Flight Time 2 11 1.63 '5 3 13 1.16 4 5 0.75 2 ' 5 9 1.18 a . ' E u - 6 10 2.01 :5 ............................................................... 12.9.92133..+..1.-.35.._ 7 12 143 E - . . - \"=39\" - g. l. . 8 8 1.73 a 9 14 1.23 M 10 15 1.31 11 6 0-96 u . . . . . . 12 l 7 l _3 2 6 8 ll) 12 Axis Title . Fight True ......... linear {Flight Time]