Sage, Incorporated, used Excel to run a least-squares regression analysis, which resulted in the following output:Regression StatisticsMultiple RR SquareObservationsInterceptProduction0.98000.960420Coefficients21,2393.29a. Total Fixed Costb. Variable Costc. Total CostStandardError6,9220.5309(X)Required:a. What is Sage's total fixed cost?b. What is Sage's variable cost per unit?Note: Round your answer to 2 decimal places.c. What total cost would Sage predict for a month in which it sold 12,000 units?Note: Round your intermediate calculations to 2 decimal places.T Stat P-Value3.07 0.0076.20 0.000per unit

The provided output of the least square regression analysis:Regression statistics:Multiple…

Answered: Sage, Incorporated, used Excel to run a…

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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Mike's Stables has two different possible cost drivers (trainer hours and number of horses) for budgeting the cost of horses training. The most recent results of the two regression analysis are as follows: Trainer's hours: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.56 Coefficients t Stat Intercept 1,004.65 4.61 X Variable 1 $22.99 7.11 Number of horses: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.63 Coefficients t Stat Intercept $5,240.95 4.44 X Variable 1 $951.48 3.50 What is the estimated total training horses cost for the coming year if 15,000 trainer hours are incurred and the stable has 350 horses to be trained, based upon the best cost driver?
Laetisaric acid is a compound that holds promise for control of fungus diseases in crop plants. Below is the least-squares regression equation to predict fungus growth (mm) from laetisaric acid concentration (µG/ml): ŷ =31.8 -0.712x Which of the following statements is correct? A. Above-average values of laetisaric acid concentration tend to accompany above-average values of fungus growth. B. From the given regression equation, we know the correlation is negative and we can say what the exact value of that correlation is. C. When fungus growth increases by 1 mm, the laetisaric acid concentration decreases by 0.712 µG/ml. D. None of the above.
What is the maximum and mininumber of the range regarding the explanatory variable for which the regression model can make valid predictions?
Suppose Wesley is a marine biologist who is interested in the relationship between the age and the size of male Dungeness crabs. Wesley collects data on 1,000 crabs and uses the data to develop the following least-squares regression line where X is the age of the crab in months and Y is the predicted value of Y, the size of the male crab in cm. Y = 8.2052 + 0.5693X What is the value of Ý when a male crab is 21.7865 months old? Provide your answer with precision to two decimal places. Interpret the value of Ý. The value of Ý is the probability that a crab will be 21.7865 months old. the predicted number of crabs out of the 1,000 crabs collected that will be 21.7865 months old. the predicted incremental increase in size for every increase in age by 21.7865 months. the predicted size of a crab when it is 21.7865 months old.
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
Is there a way to compute using Excel?
In the graph shown below consider the horizontal line to represent the average y value for the data set and the slant line to represent the linear regression equation. use only the graph of the data to do the following:

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