Produce ten scatter plot graphs with the number of pizzas sold as the predictor variable. The scatter plot graphs for the nine expenses and total costs should show the actual data points. 2. Add a trend line, regression output, and R-square statistic for each scatter plot graph. [The number of pizzas is the independent variable (X) and the cost data is the dependent variable (Y).] 3. Prepare a summary table by using selected data from the regression output: the constant (a), the X coefficient (b), and the R-square statistic for each cost item. A check row should be used to total the a and b coefficients for the nine expenses. These totals should be equal to the regression output for the total expense line. 4. Time series scatter plot graphs for the cost items. (Several cost items could be combined on one graph). 5. The time series plots may reveal information about cost behavior patterns that are more useful for predictive purposes than the regression data based on volume. For each cost equations, please explain whether the regression equation or the time series graph is a better predictor of costs. 1. Ten scatter plot graphs 2. For each graph, provide a trend line, fixed cost, variable cost, and R-square statistic. 3. Nine time series lines 4. Summary table and explanation of how this information might be used to predict costs

Acct 350 Month 8 Pizzas sold XX Ingredients os Sa & Weges on Advertsing Depreciation Press Esc to exit full screen Uties Supplies 9000 Taxes OBS Insurance $1,241 Miscellaneous ala * + wooz - Q Acct 350 Month 8 Pizzas sold XX Ingredients os Sa & Weges on Advertsing Depreciation Press Esc to exit full screen Uties Supplies 9000 Taxes OBS Insurance $1,241 Miscellaneous ala * + wooz