Bowling Corporation wants to know how closely its current cost driver is correlated with its monthly operating costs. The managerial accountant runs a regression analysis using a statistical software program andproduces the following data:Intercept Coefficient = 12,200,567X Variable 1 Coefficient = 95.65R-square= 0.8574What is the Bowling Corporation's monthly cost equation?OA. y $95.65x + $12,200,567OB. y $95.65x + $8.574O c. y = $0.8574x + $12,200,567O D. y = $12,200,567x + $8,574---

Given that We have to find the Bowling Corporation 's monthly cost equation

Answered: Bowling Corporation wants to know how…

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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Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of company speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 1 2 3 4 5 6 7 B 9 10 11 12 UNIT SALES (THOUSANDS) 398 698 898 y bar 1,304 1,163 1,195 898 1,200 982 1,235 875 811 PRICE $ PER UNIT 283 209 213 220 209 200 220 209 230 213 216 243 ADVERTISING ($000) 621 821 1,200 1,405 1,210 1,304 875 1,200 691 875 691 691 a. Perform a regression analysis based on these data using Excel. Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places. Price advertising
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month Machine Hours Electricity Costs January 2,000 $ 18,950 February 2,400 $ 22,100 March 1,400 $ 14,050 April 2,600 $ 24,100 May 3,300 $ 28,800 June 2,800 $ 23,100 July 3,600 $ 25,300 August 3,000 $ 23,300 September 1,500 $ 16,600 October 3,200 $ 27,100 November 4,200 $ 32,100 December 3,700 $ 28,300 Summary Output Regression Statistics Multiple R 0.960 R Square 0.921 Adjusted R2 0.913 Standard Error 1,545.17 Observations 12.00 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 7,465.99 1,566.61 4.77 0.00 3,975.37 10,956.62 Machine Hours 5.76 0.53 10.78 0.00 4.57 6.95 If the controller uses regression analysis to estimate costs, the estimate…
The number of megapixels in a digital camera is one of the most important factors in determining picture quality. But, do digital cameras with more megapixels cost more? The following data show the number of megapixels and the price ($) for 10 digital cameras(Consumer Reports, March 2009). Use these data to develop an estimated regression equation that can be used to predict the price of a digital camera given the number of megapixels. Brand and Model Megapixels Price (S) Canon PowerShot SD1100 IS Casio Exilim Card EX-510 8 180 200 230 10 Sony Cyber-shot DSC-T70 Pentax Optio M50 Canon PowerShot G10 120 470 15 8 Canon PowerShot A590 IS Canon PowerShot El 140 180 10 12 Fujifilm FinePix FOOFD Sony Cyber-shot DSC-W170 Canon PowerShot A470 310 10 250 110
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 23.462 + 0.315x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 74 B 150 73 C 95 59 D 70 58 E 70 42 F 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST=SSR=SSE= (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line.The least squares line provided a good fit as a large proportion of…
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results: Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 What is the slope of the linear equation? Multiple Choice -12.201 -1.860 12.505
A park has kept the number of visitors since its opening in January. For the first six months of the year, the following numbers were recorded: Month Number of visitors Month Number of visitors January 133 April 640 February 183 May 1,879 March 285 June 2,550 Estimate the intercept and slope of the linear regression equation for forecasting the numbers of visitors. What are the forecasts for July through December of the year using the regression equation? The numbers of visitors to the park were 2,150 in July, and 2,660. Use Holt’s method with α=0.15 and β= 0.10 to find the one-step-ahead and two-step-ahead forecasts for September and October, respectively.
answer both please and explain well. Anna company sells coffee products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. The CFO determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales. Questions 1. Define the coefficient of determination and explain what it means in this scenario. 2. Express the relationship between total costs and variable costs for Anna Company using a regression equation. Explain each element of the equation.
The following data show the brand, price (S), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.522 + 0.335x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 70 58 E 70 38 35 26 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination rt. (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a small proportion of the variability in y…
Regression may not be an appropriate tool if a. there are two variables that are measured on an interval or ratio scale. b. multicollinearity is present c. you want to make predictions for one variable based on information about another variable d. the pattern of data points forms a reasonably straight line
what is the answer?
2. The table below lists the annual land-line phone cost per costumer: Year 2012 2013 2014 2015 Cost ($) a. 692 610 Find a linear regression model for this data b. Interpret the slope of the model 580 C. Predict the annual land-line phone cost per customer in 2022 495 2016 434
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…

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