Question 3Verstappen Motor Holdings is preparing its budget for the year ended 31 March 2022. Thefollowing information for the last six months has been prepared:MonthAprilMayJuneJulyAugustSeptemberCars Sold204025344542Warranty claim expensesR12 00015 80012 60014 40016 00020 000REQUIRED:Q.3.1 Estimate the fixed and variable components of the warranty expenses, using theabove data, by applying the least squares regression method. Round to the nearestwhole number. The regression equations to be used are as follows:Σy = na + bExExy = ax + b[x²

Given: n = 6 Formula Used: b = n∑XY-∑X∑Yn∑X2-∑X2 a = ∑Y-b∑Xn

Answered: Question 3 Verstappen Motor Holdings is…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

The following is the data for Patient Days (the cost driver) and Maintenance Costs for a local hospital: Month Patient Days (Cost driver) Maintenance Costs January 3,300 $4,450 February 4,050 $4,750 March 3,000 $4,200 April 3, 750 $ 4,600 May 4,150 $5,050 June 4,500 $5,400 July 2,600 $4,400 Using regression analysis to analyze the hospital's cost structure, the estimated fixed maintenance cost for the hospital is: $5,468 $0.793 $2,734 $0.541
The Pilot Pen Company has decided to use 15 test markets to examine the sensitivity of demand for its new product to various prices, as shown in the following table. Advertising effort was identical in each market. Each market had approximately the same level of business activity and population. Complete the following worksheet and then estimate the demand function for Pilot's new pen using a linear regression model. Test Market i 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Total O 1.577 Regression Parameters Estimations Slope (B) Intercept (a) O 1.761 O 105.432 O 0.028 Price Charged (cents) Xi 50 50 55 55 60 60 65 65 70 70 80 80 90 90 40 1.902 O 0.032 980 What is the standard error of the estimate (se)? Quantity Sold (Thousands of Pens) O Yes O No Yi 20 21 19 19.5 20.5 19 16.5 15 14 15.5 13 14 11.5 11 17 What is the estimate of the standard deviation of the estimated slope (st)? 246.5 TiYi 1,000 1,050 1,045 1,072.5 1,230 1,140 1,072.5 975 980 1,085 1,040 1,120 1,035 990 680 I₂² 2,500 2,500…
Body Fat. Where we considered the regression of percentage of body fat on nine body measurements: height, weight, hip, forearm, neck, wrist, triceps, scapula, and sup. Use the technology of your choice to do the following. a. Use the maximum- R2 criterion to obtain a regression equation for these data. b. Use the adjusted- R2 criterion to obtain a regression equation for these data. c. Use the Mallows’ Cp criterion to obtain a regression equation for these data. d. Are the regression equations obtained in parts (a), (b), and (c) the same? e. Use the analysis in parts (a), (b), and (c) to select a regression equation for these data. For the regression equation that you selected, obtain plots of residuals versus fitted values, residuals versus the included predictor variables, and a normal probability plot of the residuals. Perform a residual analysis to assess the appropriateness of the regression equation, constancy of the conditional standard deviations, and normality of the…
A chemical engineer is investigating the effect of process operating temperature on product yield. The study results in the following data: Temperature Yield 100 57.71 110 63.17 120 76.30 130 73.30 140 89.07 150 91.17 160 170 114.11 106.33 180 190 114.38 124.07 You can use Minitab to answer the following questions. However, you should be able to calculate the slope and intercept of the least squares regression model by hand, which requires only the mear and standard deviations of X and Y, and the correlation coefficient (here r = 0.9751). 1. what is the mean temperature? 155 150 130 145 Submit Answer Tries 0/2
a. Develop the least squares estimated regression equation that relates labor hours to house square footage and type of flooring. b. Use the regression equation developed in part (a) to predict labor hours when the house size is 3350 square feet and the type of flooring is wood.
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: N Video Get more help - # 3 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ=0+0x 30 A E D $ 4 R F % 5 T Month Jan Number of Accidents 25 G MacBook Air Y H U Feb 48 8 Mar 64 1 9 Apr 90 K O Clear all P
Consider the following monthly revenue data for an up-and-coming cyber security company. Month 1 2 3 4 5 6 7 8 9 Revenue (Thousands of Dollars) 315 535 533 574 628 659 697 709 789 The summary output from a regression analysis of the data is also provided. Sales Data Intercept Month Regression Statistics Multiple R R Square Adjusted R Square Standard Error Month Revenue (Thousands of Dollars) 10 819 11 827 12 843 13 855 14 849 15 858 16 870 17 901 18 913 Observations 0.933348483 0.871139392 0.863085604 60.36074188 18 ANOVA df MS SS Regression 1 394,091.071207 394,091.071207 Residual 16 58,294.706570 3643.419161 Total 17 452,385.777778 F 108.16517503 t Stat P-value Coefficients Standard Error 460.94771242 29.68313147 15.52894488 4.53893E-11 28.52012384 2.742253998 10.400248796 1.58675E-08 Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.
An article in Technometrics by S. C. Narula and J. F. Wellington (“Prediction, Linear Regression, and a Minimum Sum of Relative Errors,” Vol. 19, 1977) presents data on the selling price (y) and annual taxes (x) for 24 houses. The taxes include local, school and county taxes. The data are shown in the following table. Sale Price/1000 Taxes/1000 25.9 4.9176 29.5 5.0208 27.9 4.5429 25.9 4.5573 29.9 5.0597 29.9 3.8910 30.9 5.8980 28.9 5.6039 35.9 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 30.0 5.0500 36.9 8.2464 41.9 6.6969 40.5 7.7841 43.9 9.0384 37.5 5.9894 37.9 7.5422 44.5 8.7951 37.9 6.0831 38.9 8.3607 36.9 8.1400…
Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price $2.70 $3.40 $1.90 $4.10 $3.00 $4.00 Using simple linear regression, and given that the price per cup is $1.80, the forecasted demand for mocha latte coffees will be cups (enter your response rounded to one decimal place). Number Sold 765 510 980 245 320 480
Training Dept. of Nimrod Inc wants to develop a regression-based compensation model (compensation in $ per year, Comp) for its mid-level managers to encourage performance, loyalty, and continuing education based on three variables. ▪ Business unit-profitability (Profit per year in $). ▪ Working experiences in Nimrod Inc (Years). ▪ Whether or not a manager has a graduate degree (Grads). If a manager has a graduate degree equals 1, 0 otherwise. Table Attached Question: Which explanatory variables and interaction terms are significant and not significant at alpha = 5%? Explain your answer briefly.
An article in Technometrics by S. C. Narula and J. F. Wellington ["Prediction, Linear Regression, and a Minimum Sum of Relative Errors" (Vol. 19, 1977)] presents data on the selling price and annual taxes for 24 houses. The data are shown in the following table. Xis taxes paid. Taxes (Local, School), Price/1000 County)/1000 Sale 25.9 4.9176 29.5 5.0208 27.9 4.5429 25.9 4.5573 29.9 5.0597 29.9 3.8910 30.9 5.8980 28.9 5.6039 35.9 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 (a) Assuming that a simple linear regression model is appropriate, obtain the regression equation applicable and compute for the coefficient of determination. (b) Test the significance of the correlation coefficient. (c) Find the mean selling price given that the taxes paid is 6.111. (d) Construct a 95% Confidence Interval for the mean selling price computed in (C). Use the editor to formot your answer
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. In 1963, during the period in question, economic growth was 3.8; population growth was 1.40; and meat consumption was 152.95. Based on these values, what would be the predicted cash price of wheat at this time in 1963?

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS