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1. DETAILS ASWSBE13 14.E.021. MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER An important application of regression analysis in accounting is in the estimation of cost.
1. DETAILS ASWSBE13 14.E.021. MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER 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. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume Total Cost (units) ($) 400 4,000 450 5,000 550 5,500 600 5,900 700 6,500 750 6,900 (a) Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. (Round your numerical values to two decimal places.) D = (b) What is the variable cost (in dollars) per unit produced? $ (c) Compute the coefficient of determination. (Round your answer to three decimal places.) What percentage of the variation in total cost can be explained by production volume? (Round your answer to one decimal place.) |% (d) The company's production schedule shows 650 units must be produced next month. Predict the total cost (in dollars) for this operation. (Round your answer to the nearest cent.) Need Help? Read It2. DETAILS ASWSBE13 14.E.027.ALT. MY NOTES ASK YOUR TEACHER DATAfile: SalaryStress You may need to use the appropriate technology to answer this question. To identify high-paying jobs for people who do not like stress, the following data were collected showing the average annual salary ($1,000s) and the stress tolerance for a variety of occupations.+ Job Average Annual Salary ($1,000s) Stress Tolerance Art directors 81 69.0 Astronomers 96 62.0 Audiologists 70 67.5 Dental hygienists 70 71.3 Economists 92 63.3 Engineers 92 69.5 Law teachers 100 62.8 Optometrists 98 65.5 Political scientists 102 60.1 Urban and regional 65 69.0 planners The stress tolerance for each job is rated on a scale from 0 to 100, where a lower rating indicates less stress. (a) Develop a scatter diagram for these data with average annual salary as the independent variable 75 75 75 75 70 70 70 70 65 65 65- 65 Stress Tolerance Stress Tolerance Stress Tolerance 60 60 60 60 55- 2 55 3 55- 55 50 60 70 80 90 100 110 50 60 70 80 90 100 110 50 60 70 80 90 100 110 50 60 70 80 90 100 110 O Average Annual Salary ($1,000s) Average Annual Salary ($1,000s) Average Annual Salary ($1,000s) Average Annual Salary ($1,000s) What does the scatter diagram indicate about the relationship between the two variables? O There appears to be no noticeable relationship between average annual salary ($1,000s) and stress tolerance. There appears to be a positive linear relationship between average annual salary ($1,000s) and stress tolerance. O There appears to be a negative linear relationship between average annual salary ($1,000s) and stress tolerance. (b) Use these data to develop an estimated regression equation that can be used to predict stress tolerance given the average annual salary (in $1,000s). (Round your numerical values to three decimal places).What does the scatter diagram indicate about the relationship between the two variables? O There appears to be no noticeable relationship between average annual salary ($1,000s) and stress tolerance. O There appears to be a positive linear relationship between average annual salary ($1,000s) and stress tolerance. O There appears to be a negative linear relationship between average annual salary ($1,000s) and stress tolerance. (b) Use these data to develop an estimated regression equation that can be used to predict stress tolerance given the average annual salary (in $1,000s). (Round your numerical values to three decimal places). (c) At the 0.05 level of significance, does there appear to be a significant statistical relationship between the two variables? (Use the F test.) State the null and alternative hypotheses. O Ho: Bo = o Ha: Bo = 0 O Ho: B, = 0 O Ho: Bo = 0 OHO: B 1 = 0 Ha! P1 = 0 OH: B1 20 Ha : B ,
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