75661.1 and the SS value is 609526983.2), The Co-efficient values illustrates that all the variables are directly related that means with the increase in Store Size the Profits will also increase, and this is the case with the Clothing Sales and the Non-clothing Sales. It can also be stated that the Clothing Sales are the least related variable among the rest of two variables and can be neglected in order to achieve more accurate and realistic results.
h) The R-Square value represents the closeness of the values to the regression line. in the result of this regression analysis the R-Square value is 0.985 which illustrates that the values are closely fitted. This also shows that the variations in any one of the variables (i.e. No-clothing Sales and Store Size) may cause a positive or negative affect of 98.5%. The value of Clothing Sales is ignored as it is not in the significant region and will not affect the overall outcome.
i) The P-values indicates the significance values that whether the variable(s) (the independent ones) affect the dependent one(s) or the overall regression analysis. In the regression analysis the P-Values of Non-clothing Sales and Store Size are significant at the 5% significance level.
The net present value is the sum of all net cash flows expected from a project over a period of projection (Needles, Powers, & Crosson, 2010). In this case three years projections have been considered for two projects X and Y.
The NPV of Project X is negative i.e. less than zero therefore this project should not accepted. On the other hand, Project Y has a positive NPV value i.e. more than zero over a three-year projection period therefore Project Y should be accepted (Moyer, McGuigan, Rao, & Kretlow, 2011).
C. Monte Carlo Simulation allows multiples values of an asset or investment based on certain sensitivity or scenario analysis.