This is Management Accounting class, I need help with completing the worksheet (WRKSHT) requirements below. Clarity of

formatting is critical (i.e., proper headings on each worksheet, proper tab labels, highlightingcritical values, etc.). I expect a professionally completed worksheet. Results spreadsheet for the cost estimation handout should be your basis for what "professional"spreadsheet means.

Angora Wraps of Pendleton, Oregon, makes fine sweaters out of pure angora wool. The business is seasonal, with the largest demand during the fall, the winter, and Christmas holidays. The company must increase production each summer to meet estimated demand. The company has been analyzing its costs to determine which costs are fixed and variable for planning purposes. Below are data for the company's activity and direct labor costs over the last year. The number of workdays varies from month to month due to the number of weekdays, holidays, and days of vacation in the month. The paid days include paid vacations (in July) and paid holidays (in November and December). The number of units produced in a month varies depending on demand and the number of workdays in the month. The company has eight workers who are classified as direct labor. CASE 2-27: ANGORA WRAPS CASE Read Case 2-27, p. 63 in Noreen Text complete the following requirements: Set up an Excel Spreadsheet similar to the Cost Estimation Guide results (provided on D2L) and complete the worksheet (WRKSHT) requirements below. DON'T JUST REVISE MY RESULTS. CREATE YOUR OWN. WHY? THAT'S HOW WE LEARN. Clarity of formatting is critical (i.e., proper headings on each worksheet, proper tab labels, highlighting critical values, etc.). I expect a professionally completed worksheet. My results spreadsheet for the cost estimation handout should be your basis for what I mean by \"professional\" spreadsheet. Complete the following: 1. WRKSHT 1 - Initial Data and Scattergraphs - label appropriately. Prepare your Initial Data Worksheet Create your Scattergraphs (2 of them): Format neatly and place below your data a. Direct Labor Cost vs. \"Thousands of Units Produced\" b. Direct Labor Cost vs. \"Number of Paid Days\" 2. WRKSHT 2 - High/Low Method Computations a. Determine the Cost Estimation Equations assuming, i. Independent Variable = \"Thousands of Units Produced\" ii. Independent Variable = \"Number of Paid Days\" b. Determine the Cost Estimates for the cost equations above. (SEE BELOW for \"Information for Computing Cost Estimates\" on next page) (Be sure to formulate the cells to compute these estimates. Don't just hardcode the numerical solution after solving it on a calculator.) 3. WRKSHT 3 - Simple Regression - Indep. Variable = \"Thousands of Units Produced\" a. Complete and format the regression results, then b. Document the cost estimation equation, and c. Determine the cost estimate using the equation determined in b. (SEE BELOW for \"Information for Computing Cost Estimates\" on next page) 4. WRKSHT 4 - Simple Regression - Indep. Variable = \"Number of Paid Days\" a. Complete and format the regression results, then b. Document the cost estimation equation, and c. Determine the cost estimate using the equation determined in b. (SEE BELOW for \"Information for Computing Cost Estimates\" on next page) 5. WRKSHT 5 - Multiple Regression Analysis a. Complete and format the regression results, then b. Document the cost estimation equation c. Determine the cost estimate using the equation determined (SEE BELOW for \"Information for Computing Cost Estimates\") INFORMATION FOR COMPUTING COST ESTIMATES: Assume that Angora Wraps for next January estimates that it will produce 140 thousand Units of Product and expect 23 Paid Days. To Do: Calculate the 5 cost estimates using the 5 cost estimation equations you've developed from the High/Low (2), Simple (2), and Multiple Regression (1) worksheets. Document each of these cost estimates on each of your applicable worksheets. FINAL ANALYSIS WRITE-UP: To Do: WRKSHT 6: Finally, choose the one and only one cost estimation equation (of the 5 you've created) that Angora Wraps should use to estimate its Direct Labor Costs. Explain WHY based on your analyses above. You may only select one and only one and justify. Be clear and concise. Review the Cost Estimation Handout Results - \"Final Analysis\" Worksheet for an example of what I'm looking for. Upload your finished Excel spreadsheet Shipped Units Month May June July August September October Shipped Weight (lbs) 3,000 5,000 4,000 7,000 6,000 4,500 6,200 8,000 8,100 10,000 7,000 8,000 Units Shipped Sales $ Value $ $ $ $ $ $ 100,000 110,000 80,000 114,000 140,000 160,000 Shipping Expenses $ $ $ $ $ $ 5,000 7,000 6,000 10,000 8,000 7,600 Shipping Expense by Units Shipped $12,000 $10,000 $8,000 Shipping Expenses f(x) = 1.1591836735x + 1567.3469387755 R = 0.9185495627 Shipping Expenses Linear (Shipping Expenses) $6,000 $4,000 $2,000 $2,000 4,000 6,000 8,000 Units Shipped Shipping Expenses by Weight (lbs.) $12,000 $10,000 $8,000 Shipping Expenses f(x) = 1.0634564375x - 1116.9149153234 R = 0.6186088116 Shipping Expenses Linear (Shipping Expenses) $6,000 $4,000 $2,000 $5,000 10,000 15,000 Weight Shipped (lbs.) Shipping Expenses by Sales Value of Units Shipped $12,000 $10,000 $8,000 Shipping Expenses $6,000 f(x) = 0.0269381107x + 4105.9283387622 R = 0.1989090856 $4,000 $2,000 $$50,000 $100,000 $150,000 Sales Value of Units Shipped $200,000 Shipping Expenses Linear (Shipping Expenses) Month May June July August September October Shipped Units 3,000 5,000 4,000 7,000 6,000 4,500 INITIAL DATA Shipped Units Shipped Weight (lbs) Sales $ Value 6,200 $ 100,000 8,000 $ 110,000 8,100 $ 80,000 10,000 $ 114,000 7,000 $ 140,000 8,000 $ 160,000 Shipping Expenses $ 5,000 $ 7,000 $ 6,000 $ 10,000 $ 8,000 $ 7,600 HIGH/LOW METHOD: Independent Variable = Shipped UNITS Variable Cost per "Shipped Unit" Units High Low Difference VARIABLE COST PER UNIT Shipping Expenses 7,000 $ 10,000 3,000 $ 5,000 4,000 $ 5,000 $ 1.25 Fixed Cost Using High: Using Low: Y = a + bX, thus $10,000 = a + $1.25 * 7,000 Units Y = a + bX, thus $ 5,000 = a + $1.25 * 3,000 Units Solve for a: Fixed Costs = $ 1,250.00 Cost Equation: Y = 1,250 + 1.25 X, where X = Shipped Units Cost Estimation Data: Units 6,250.00 Weight 8,500.00 lbs. Sales Value $ 125,000.00 Cost Estimate: $ 9,062.50 Simple Regresion: Units = Independent Variable Regression Statistics Multiple R 0.9584 R Square 0.9185 Adjusted R Square 0.8982 Standard Error 551.4360 Observations 6.0000 ANOVA Regression Residual Total Intercept Units Cost Equation: df 1.0000 4.0000 5.0000 SS 13,717,006.8027 1,216,326.5306 14,933,333.3333 MS 13,717,006.8027 304,081.6327 F 45.1096 Significance F 0.0026 Coefficients 1,567.3469 1.1592 Standard Error 877.9259 0.1726 t Stat 1.7853 6.7164 P-value 0.1488 0.0026 Lower 95% (870.1661) 0.6800 Y = 1,567.35 + 1.1592 X Cost Estimation Data: Units 6,250.00 Weight 8,500.00 lbs. Sales Value $ 125,000.00 Cost Estimate: $ 8,812.24 Upper 95% 4,004.8600 1.6384 Simple Regresion: Weight = Independent Variable Regression Statistics Multiple R 0.7865 R Square 0.6186 Adjusted R Square 0.5233 Standard Error 1,193.2562 Observations 6.0000 ANOVA df Regression Residual Total Intercept Weight (lbs) Cost Equation: ### 1,423,860.4366 Coefficients (1,116.9149) 1.0635 Standard Error 3,327.2263 0.4175 t Stat (0.3357) 2.5471 Y = (1,116.91) + 1.0635 X Cost Estimation Data: Units Weight Sales Value $ Cost Estimate: 1.0000 4.0000 5.0000 SS 9,237,891.5868 5,695,441.7466 14,933,333.3333 $ 6,250.00 8,500.00 lbs. 125,000.00 7,922.46 MS F 6.4879 Significance F 0.0635 P-value 0.7540 0.0635 Lower 95% (10,354.7762) (0.0957) Upper 95% 8,120.9463 2.2227 Simple Regresion: Sales $ Value = Independent Variable Regression Statistics Multiple R 0.4460 R Square 0.1989 Adjusted R Square (0.0014) Standard Error 1,729.3754 Observations 6.0000 ANOVA Regression Residual Total Intercept Sales $ Value Cost Equation: df 1.0000 4.0000 5.0000 SS 2,970,375.6786 11,962,957.6547 14,933,333.3333 ### 2,990,739.4137 Coefficients 4,105.9283 0.0269 Standard Error 3,249.1865 0.0270 t Stat 1.2637 0.9966 Y = 4,105.93 + 0.0269 X Cost Estimation Data: Units 6,250.00 Weight 8,500.00 lbs. Sales Value $ 125,000.00 Cost Estimate: $ 7,473.19 MS F 0.9932 Significance F 0.3754 P-value 0.2750 0.3754 Lower 95% (4,915.2595) (0.0481) Upper 95% 13,127.1162 0.1020 Multiple Regression: All 3 variables included Regression Statistics Multiple R 0.9937 R Square 0.9874 Adjusted R Square 0.9684 Standard Error 307.1756 Observations 6.0000 ANOVA df Regression Residual Total Intercept Units Weight (lbs) Sales $ Value Cost Equation: MS 4,914,873.1970 94,356.8711 F 52.0881 Significance F 0.0189 Coefficients (1,824.0441) 0.7797 0.4519 0.0144 Standard Error 1,146.9736 0.1537 0.1624 0.0054 t Stat (1.5903) 5.0728 2.7817 2.6550 P-value 0.2527 0.0367 0.1086 0.1174 Lower 95% (6,759.0730) 0.1184 (0.2471) (0.0090) Y = -1824.04 + .78 per unit + .45 per lb. + .01 per Sales $ Value Cost Estimation Data: Units Weight Sales Value $ Cost Estimate: 3.0000 2.0000 5.0000 SS 14,744,619.5911 188,713.7422 14,933,333.3333 $ 6,250.00 8,500.00 lbs. 125,000.00 8,695.68 Upper 95% 3,110.9849 1.4410 1.1508 0.0379 FINAL ANALYSIS AND RECOMMENDATIONS The purpose of this analysis was to determine an appropriate cost estimation equation to estimate shipping expenses. The analysis considered the high/low method, simple regression, and multiple regression. Based on this analysis, it appears that the company should use the equation derived from using Simple Regression, using "Units Shipped" as the independent variable (cost driver). This method and independent variable is best for a number of reasons. First, "units shipped" explains 91.85% of the variability is shipping expenses as documented by the R-square. Second, the coefficient of the independent variable, units shipped, is statistically significant and reliable (p-value = .0026). Although the fixed cost coefficient is not statistically reliable, its result is reasonably close to signfiicance and will be used as the fixed cost estimate; albeit with caution. Regarding the other methods, because the high/low method only uses two (most extreme) data points, it does not provide as reliable an estimate for estimating shipping expenses, and therefore, should only be used as a quick guide. The other simple regresssion results and the multiple regression results do not provide results that are as strong as the method and estimate recommended