1. [21 points] An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Sample data on production volumes and total cost data for a manufacturing operation was gathered and the following analysis results were obtained. a) Complete the above ANOVA table. [2 points] b) Based on the scatter diagram developed, do you expect a relationship between Total Cost ($) and Production Volume (Units)? Indicate the expected type of relationship if any. [1 point] c) What is the sample size? [1 point] d) Write down what the estimated regression equation is that relates Total Cost with Production Volume. [1 point] e) State the Null and Alternate Hypotheses used to test statistical significance of regression between Total Cost and Production Volume [2 points] f) Read and state what the p-value is from the report. Test the significance of the regression at a .01 level using p-value approach. What is the test decision? [3 points] g) Determine Critical-value for the test. Test the significance of the regression at a .01 level using Critical-value approach. What is the test decision? [4 points] h) Write down the 99% confidence interval on the \"Slope Coefficient\" based on the report. Based on the CI would you Reject or Not Reject the Null Hypothesis? Why or Why not? [2 points] i) What conclusion you arrive at based on the Hypothesis tests with respect to the relationship between Total Cost and Production Volume? [1 point] j) State why the estimated regression equation is \"good\" or \"no good.\" [1 point] k) What is the value of sample correlation coefficient? [1 point] l) Why is R Square value equal to 95.87%? [1 point] 1 m) Interpret what the value of slope coefficient = 7.6 means. [1 point] 1. [33 points] The U.S. Department of Energy's Fuel Economy Guide provides fuel efficiency data for cars and trucks. A portion of the sample data for 311 compact, midsize, and large cars follows. The column labeled Class identifies the size of the car; Compact, Midsize, or Large. The column labeled Displacement shows the engine's displacement in liters. The column labeled Fuel Type shows whether the car uses premium (P) or regular (R) fuel, and the column labeled Hwy MPG shows the fuel efficiency rating for highway driving in terms of miles per gallon. A partial report of regression analysis is provided below. Answer the questions based on the report. Regression Analysis: Hwy MPG versus Displacement, ClassMidsize, ... Predictor Constant Displacement ClassMidsize ClassLarge FuelPremium S = 1.64596 Coef 29.7624 -1.6347 3.9634 1.6450 -1.1210 SE Coef 0.5521 0.1169 0.3193 0.2940 0.2090 R-Sq = 83.4% T 53.91 -13.98 P 0.000 0.000 0.000 0.000 0.000 R-Sq(adj) = 83.2% Analysis of Variance Source Regression Residual Error Total DF SS MS F P 0.000 829.0 4989.3 Predicted Values for New Observations New Obs Fit SE Fit 99% CI 1 25.3822 0.2233 (24.8033, 25.9610) 99% PI (21.0767, 29.6876) Values of Predictors for New Observations New Obs Displacement ClassMidsize ClassLarge 1 3.00 0.000000 1.00 FuelPremium 1.00 a) What is the sample size used for the analysis? (1 point) b) What type of variables are \"Class\" and \"Fuel Type\"? Show how they are represented in the regression analysis. (3 points) c) Identify the Dependent Variable and the Independent Variables in the model. (1 point) 2 d) Calculate 't' test statistic values. Complete the ANOVA Table. (3 points) e) Write down Hypothesis Statements. Conduct both p-value and critical-value based hypothesis tests at 0.01 level of significance. Is the Multiple Regression between \"Hwy MPG\