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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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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a: 335 b: 355 c: 365
find the equation of the regression line. Round to 3 decimal places.
a snow plower recorded inches of snow fall and the yield by measure of square acreage over the previous 5 seasons. The regression equation is determined to be y=38.4+6.3x where x represents the inches of snowfall and y the measure of yield. 1. use the best fitting line to predict yield for 6 inches of snow 2. one of the records is (6,75) what is teh residual if x is 6
Use the given data set to answer parts (a) and (b). a. Find the regression equation for the data points. b. Graph the regression equation and the data points. a. Find the regression equation for the data points. G (Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) X 5 y - 1 4 1 1 -1 3 2 2 4 Q
Consider the regression équation In =3.94+0.4X₁₁ +0.7X₂- a. Predict the value of Y when X₁ =7.4 and X₂ = 5.2. b. Interpret the meaning of the regression coefficients 100 b,, and 100-b₂. a. The predicted value of Y is (Type an integer or decimal rounded to two decimal places as needed.) b. Choose the correct answer below. OA. Holding constant the other variables, for every one unit increase in X, or X₂, the value of Y increases by b, or by percent, respectively. B. Holding constant the other variables, for every one percent increase in X, or X₂, the value of Y increases by b, or b₂ percent, respectively. C. Holding constant the other variables, for every one unit increase in X, or X₂, the value of Y increases by 100 b, or 100-b₂ percent, respectively. OD. Holding constant the other variables, for every one percent increase in X, or X₂, the value of Y increases by b, or b₂ units, respectively. OE. For every decrease of one unit of In Y, holding bo constant, the estimated change in X, or…
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Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. A horizontal table lists x-values in the first row and y-values in the second row. The x-values are as follows from left to right: 6, 8, 20, 28, and 36. The y-values are as follows from left to right: 2, 4, 13, 20, and 30. A. y with caret= −2.79 + 0.897x B. y with caret = −3.79 + 0.801x C. y with caret= −2.79 + 0.950x D. y with caret = −3.79 + 0.897x
Use the data shown in the graph to write a quadratic regression equation. Then predict the box office revenue in 2013. Let x represent the number of years since 2000. Millions of dollars 32- 189 31+ 30- Box office revenue 28.9 29.4 28.7 30.8 29- 28- 27- 2004 2005 2006 2007 2008 2009 Year
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
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
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?

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