ExRaay TOIRAn 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 estimatedregression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumesand total cost data for a manufacturing operation.AProduction Volume (units)Total Cost ($)4005,0004506,0005506,4006006,9007,4007007508,000a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations.Compute bi and bo (to 1 decimal). Do not round intermediate calculations.b₁*boComplete the estimated regression equation (to 1 decimal). Do not round intermediate calculations.y=1247.0*+ 7.6xb. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations.$7.60c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report r² between 0 and 1.7².959What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? Do not round intermediate calculations.DDDIIF9F8DOD000F7F6F10IF110x+CF12 6,4006006,9007007,400750a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations.8,000Compute bi and bo (to 1 decimal). Do not round intermediate calculations.b₁boComplete the estimated regression equation (to 1 decimal). Do not round intermediate calculations.ŷ 1247.0=+7.6xb. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations.$ 7.60c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report r² between 0 and 1.7² = .959What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? Do not round intermediate calculations.95.9 %d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? Do not roundintermediate calculations.$Hide FeedbackDIIJAF9AFBDODF7000F6F480F5*(F10aF11

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Answered: that could be used to predict the total…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Use the cost model screenshot attached to answer the question. Thanks for the help. Use the weekly cost model to estimate the total weekly cost when the weekly demand is 147. Round to the nearest dollar. $
Make a scatter plot of the data and determine which type of model best fits the data. linear quadratic exponential cubic
C) if a country increases its life expectancy, the happiness index will increase or decrease d) if the life expectancy is increased by 4.5 years in a certain country .how much will the happiness index change? Round two decimal places e) use the regression line to predict that happiness index of a country with a life expectancy of 89 years . Round to two decimal places use the space below to type your answers
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Graph the scatterplot and find the equation of the regression line GDP. CO2 emissions (in millions metric tons) 1.7. 552.6 1.2. 462.3 2.5. 475.4 2.8. 374.3 3.6. 748.5 2.2. 400.9 0.8. 253.0 1.5. 318.6 2.4. 496.8 5.9. 1180.6
Use Excel to develop a regression model to predict Annual Food Spending by Annual Household Income for those living in the Metro area only. Suppose a household in the metro area has an annual income of $60,000. Predict how much they spend on food per year. Write your answer as a number and round to 2 decimal places. Annual Food Spending ($) Annual Household Income ($) Non mortgage household debt ($) Region: 1 = NE 2 = MW 3 = S 4 = W Location: 1 = Metro 2 = Outside Metro 8909 56697 23180 1 1 5684 35945 7052 1 1 10706 52687 16149 1 1 14112 74041 21839 1 1 13855 63182 18866 1 1 15619 79064 21899 1 1 2694 25981 8774 1 1 9127 57424 15766 1 1 13514 72045 27685 1 1 6314 38046 8545 1 1 7622 52408 28057 1 1 4322 41405 6998 1 1 3805 29684 4806 1 1 6674 49246 13592 1 1 7347 41491 4088 1 1 2911 26703 15876 1 1 8026 48753 16714 1 1 8567 55555 16783 1 1 10345 71483 21407 1 1 8694 50980 19114 1 1 8821 46403 7817 1 1…
Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. The table shows the shoe size and heights (in.) for 6 men. Shoe size, x Height, y Find the regression equation. ŷ=x+ (Round to three decimal places as needed.) Choose the correct graph below. O A. Height (in.) 75+ 65- 8 Shoe size N O B. Height (in.) 75+ 65+ 8.5 65.0 Shoe size 9.5 66.0 o 11.0 67.0 O C. Height (in.) 75 65 8 Shoe size 12.0 73.0 Q 13.0 71.0 D. Height (in.) 75- 65 13.5 73.0 Shoe size Q o
Solve it correctly and clear
2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?
Construct and interpret a 95% prediction interval for the proceeds when the number of issues is 602.

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