Consider the following Multiple liner regression output to predict demand (in 100,000 units) for a product given variables Period, ADV ($10,000), DIF ($) and AIP ($) SUMMARY OUTPUT Alegression States Multiple R R Square Adjusted R Square Standard Error Observations 0.9572672 0.91636048 0.9044 1198 0.38946319 33 ANOVA MS 46.5313572 Significance 11338AEAA 76.6924985 Regression Residual Total 11.6328393 0.15168158 28 4.24708422 50.7784414 Coefficients t Stat P.value 18.14 0.11 Intercept PERIOD ADV DIFF AIP 0.088 0.87 -0.77 Standard Error 3.24594179 0.01245496 0.12266881 0.37605127 0.50108484 5.58894132 8.6242017 0.72914335 2.13772628 -1.54243023 5.5623E-06 2.2696E-09 0.47196954 0.04140952 0.13419656 0 3023 0.13419656 A-Construct the multiple linear regression equation to forecast demand based on given four variables B Using the above equation, forecast demand for the 40" period if ADV=11.50, DIFF = 0.50, and AIP - $7.00. C- Is this model is good for prediction purposes? Why? D-What percentage of variation in demand is explained by these variables in the model? Explain. E-Rank the independent variables based on their degree of contribution to the model (highest contribution to lowes F. Find the incremental change in demand if ADV is increased by $2 ($20,000, use only $2 in formula)