Question

# SUMMARY OUTPUT Regression Statistics Multiple R 0.993709623 R Square 0.987458816 Adjusted R Square 0.987378251 Standard Error...

 SUMMARY OUTPUT Regression Statistics Multiple R 0.993709623 R Square 0.987458816 Adjusted R Square 0.987378251 Standard Error 514.2440271 Observations 471 ANOVA df SS MS F Significance F Regression 3 9723795745 3241265248 12256.7707 0 Residual 467 123496711.4 264446.9194 Total 470 9847292456 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -267.1127974 42.01832073 -6.357055513 4.8988E-10 -349.68118 -184.54441 -349.68118 -184.54441 Fuel cost (000,000) 0.449917223 0.098292092 4.577349137 6.0451E-06 0.25676768 0.64306676 0.25676768 0.64306676 Salary (000,000) -0.327915884 0.188252958 -1.741889678 0.08218614 -0.6978436 0.04201186 -0.6978436 0.04201186 Average seat miles (000,000) 0.140064895 0.004458029 31.41857134 2.942E-117 0.13130462 0.14882517 0.13130462 0.14882517

write the equation line for all observation and interpret the result in terms of the positive/negative influence of factors on revenue.

Regression Equation Therefore,

Revenue is impacted positively by fuel cost and average seat miles i.e. when fuel cost/ average seat miles increase, revenue also increases. Revenue is impacted negatively by the salary. When salary increases, revenue reduces.

Statistical significance

The p-value for the intercept, the coefficient of fuel cost, and the coefficient of average seat miles are less than 0.05. So, their values are statistically significant at 95% confidence level. The same is not true for the coefficient of salary because the p-value is more than 0.05.

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