Next, proceed to run two new regressions with intercepts (DO NOT check "the constant is zero" box in Excel). The first regression will use the new scaled coal consumption variable (from a above) as the dependent variable and the time trend variable and three quarterly dummy variables (from b and c) as independent variables. The second regression will use the new scaled kWh consumption variable as the dependent variable and the time trend variable and quarterly dummy variables as independent variables. Place the output of the regressions on new sheets (titled "Coal Time Trend" and "Electricity Time Trend" respectively). The intercept in each will essentially capture the average consumption (per square foot) of the resource for quarter 4 while the three dummy variables will reflect how much this shifts in each other quarter. The coefficient for the time trend variable will capture how much resource consumption per square foot is changing over time. The p-values for all give insight into statistical significance for each. Create scatterplots to better visualize the data with the time trend variable as the x-axis, again checking the residuals box to more clearly identify the differences between actual and predicted values (Delete the scatterplots for each quarter, only keep the time trend scatterplot on the Excel sheet). Name each independent variable in both regression outputs. A time trend would appear as a consistent increase or decrease between the actual and predicted values in the scatterplot. You will be looking for whether the time trend independent variable is significant in the regression analysis (p-value