The digital marketing analytics team has started with an initial model where four independent variables X1, X2, X3, and X4 are considered and the following regression output has been generated: Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept X1 X2 X3 X4 0.768046623 0.589895615 0.589145538 1388.563709 df 2192 SS MS F 6065423910 1516355978 786.4471562 4216774766 1928109.175 4 2187 2191 10282198676 0.900833531 Coefficients Standard Error t Stat P-value -934.6460736 171.9024485 -5.437072491 6.01974E-08 0.152866821 5.892930377 4.38256E-09 1.719781812 0.225223192 7.635900183 3.3284E-14 8.986935152 0.319291996 28.14644673 5.1539E-149 0.203210824 0.101572792 2.000642298 0.145554359 Significance F 0 Lower 95% Upper 95% Lower 95.0% Upper 95.0% -1271.755248 -597.5368991 -1271.755248 -597.5368991 0.601054161 1.200612901 0.601054161 1.200612901 1.278108032 2.161455593 1.278108032 2.161455593 8.36078781 9.613082493 8.36078781 9.613082493 0.004021572 0.402400075 0.004021572 0.402400075 Answer the following questions based on the provided output: B) Do you think this model is reliable and final for marketing strategists application? If there are issues with the model, explain in detail what these are and what steps you would take to resolve them and finalise the model. (Word count: 200 words) C) Assume everything is fine with the output and the team decides to go ahead with this. Develop an equation that can be used to predict the target variable based on the dependent variables X1, X2, X3 and X4. D) Interpret the coefficient of X2 in the output table. What does this value mean in the model? (Word count: 50 words) E) Use the model developed in part C to predict Y for when X1, X2, X3 and X4 are as follows: X1 = 800, X2 = 500, X3 = 400, and X4 = 1500. F) Use the model developed in part C to calculate the residuals of data in rows 1 to 4 of the excerpt above (i.e., for dates 01/01/2015, 02/01/2015, 03/01/2015 and 04/01/2015). Comment if any of these may be considered an outlier based on the provided output and why. (Word count: 50 words) G) Explain what we mean by seasonality in data and describe in detail how you would alter your multiple regression analysis here to include seasonality. (Word count: 100 words)