Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the followinginformation (note: only a portion of the regression analysis results is presented here)SUMMARY OUTPUTRegression StatisticsMultiple R 0.998041188R Square 0.996086212Adjusted R Square 0.994781616Standard Error 917.1714273Observations 5ANOVAdf SS MS F Significance FRegression 1 642276389.7 642276389.7 763.5208906 0.000104039Residual 3 2523610.281 841203 427Total 4 644800000Coefficients Standard Errort StatP-valueIntercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876What is variable cost per machine hour (rounded to the nearest cent)?OA. $6,679.62OB. $917.17OC. $9.80OD. $.99Lower 95% Upper 95.0%9645.19851710.92509577

The regression model shows a relationship between 1 dependent variable and 1 or more independent…

Answered: Big Time Corporation wanted to…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.2: The Least Square Line

Problem 8E

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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Water is being poured into a large cone shape cistern. The volume of water measured and centimeters cubed is reported at different time intervals measured in seconds. A regression analysis was completed and is displayed in the computer output. what is the equation of the lease squared regression line?
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Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 Using a = 0.05, Louis should ________________.
What kind of plot is useful for deciding whether finding a regression line for a set of data points is reasonable?
Home Price Sales. Where we considered the regression of sale price of a home on size, presence of a pool, lot area, age, number of baths, number of stories, number of garage stalls, presence of traffic, and type of roof based on data for 88 homes in northeast Phoenix. Describe and discuss problems that could have arisen in the collection of the data for this regression analysis.
Find the equation of the regression line for the data based on time spend forstudying and current CGPA
You want to look at an ANOVA table of a regression in which a dependent variable is predicted using an intercept and one slope coefficient. Unfortunately, as you want to look at the table, you knock over your coffee mug which smudges out some of the numbers. Here is what you still can read: • n=7 • F-ratio = 15 • Residual sum of squares (RSS) = 16 • t-score of the slope coefficient = 3.873 How big is the explained sum of squares (ESS)? a 44 b 52 c 48 d 40 How big is the total sum of squares (TSS)? a 64 b 52 c 60 d 56 How big is the explained R-squared? a 0.7 b 0.75 c 0.8 d Cannot be determined What's the p-value for the F-ratio? a 0.012 b 0.024 c 0.036 d…
The purpose of this assignment is to apply simple regression techniques to find factors that determine the car values. Open the dataset of FamilySedans (attached) and read the case entitled “Finding the Best Car Value” in its entirety at the end of Chapter 14. Use descriptive statistics to summarize the data and make comments on them. Use regression analysis to develop an estimated regression equation that could be used to predict the value score given the price of the car. Use regression analysis to develop an estimated regression equation that could be used to predict the value score given the five-year owner costs. Use regression analysis to develop an estimated regression equation that could be used to predict the value score given the road-test score. Use regression analysis to develop an estimated regression equation that could be used to predict the value score given the predicted-reliability. What conclusions can you derive from your analysis?
Sky Eyes manufactures drones for security and enforcement organizations. They would like to predict production cost using a regression model. The regression model is based on historic production values. The regression statistics from Excel are provided below. Intercept Machine Hours Labor Hours Production Output Coefficients -477.7647341 44.97381045 -0.08890915 55.06503861 Standard Error 62.63384 -7.627901659 7.518943592 0.083252 -1.067948403 5.957718 9.242639029 t Stat 5.9814 P-value 1.20603E-09 1.74367E-09 0.291239564 5.77595E-12 What can be concluded based on the output of the regression statistics? (Select all that apply.) The regression equation is y 44.97 x (machine hours) - 0.0889 x (labor hours) + 55.065 x (production output). Labor hours is significant variable in the regression model. Machine hours is a significant variable in the regression model. The regression equation is y=-477.76+44.97 x (machine hours) - 0.0889x (labor hours) + 55.065x (production output).

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