Regression Statistics Multiple R R Square Adjusted R Square Standard Error Obser

Question

Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.769489638 0.592114303 0.56073848 793089.0209 15 ANOVA F Significance F 1 1.18701E+13 1.19E+13 18.87167 0.000795544 MS df Regression Residual Total 13 8.17687E+12 6.29E+11 14 2.0047E+13 Coefficients Standard Emor tStat P-value Lower 95% Upper 95% Lower 95.0 % Upper 95.0% 4833.942857 430931.3949 0.011217 0.99122 926136.7357 935804.6214 -926136.7357 935804.6214 205896.1071 47396.13437 4.344154 0.000796 103502.984 308289.2303 103502.984 308289,.2303 Intercept X Variable 1 4 mit, Thosand

Explanation / Answer

5.

The X variable here denotes the year and the Y variable represents the Free Cash Flow.

6.

The slope of the equation is 205896. (in thousands)

It implies that for a unit change in the X variable the value of Y variable will increase by 205896 (in thousands).

7.

The P value of 0.000796 indicates that the slope of the equation is not zero. The p value for each term (coefficient and intercept) tests the null hypothesis of coefficient being equal to zero. A sufficiently low P value (<0.05) implies that null hypothesis can be rejected.

8.

Y’ = 205896.1071 * X + 4833.942857

For X = 16

Y’ = 205896.1071 * 16 + 4833.942857 = 3299171.656

Predicted cash flow (95% confidence interval) = (Y’ – 1.645*SE, Y’ + 1.645*SE)

Predicted cash flow (95% confidence interval) = (3299171.656 – 1.645*793089.0209, 3299171.656 +1.645*793089.0209)

Predicted cash flow (95% confidence interval) = (1994540.217 ,4603803.095)

9.

Y = 205896.1071 * X + 4833.942857

10.

We need the formula to predict future cash flows for subsequent years and also to explain the relationship of how the cash flow has grown year on year.

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