Function: SLOPE, INTERCEPT; Cell ReferencingUsing Excel to Computer Variable and Fixed CostElements Using RegressionData for Stiever Corporation's maintenance costs is shownbelow.JulyAugustSeptemberOctoberPROBLEMNovemberDecemberUnitsProduced Total Cost18,000 $32,00032,00048,00036,00055,00022,00038,00040,00066,10038,00062,000Student Work AreaRequired: Provide input into cells shaded in yellow in this template. Use cellreferences to the Problem area to identify the high and low points. Use theSLOPE and the INTERCEPT functions with cell referencing to the Problemarea.Compute the variable- and fixed-cost elements using regression analysisusing Excel's SLOPE and INTERCEPT functions.per unitVariable costTotal fixed costsPresent your solution in the form of a cost equation.Cost equationTotal cost =+per unit produced

The question is about regression analysis.The data on the Stiever Corporation maintenance costs is…

Answered: Function: SLOPE, INTERCEPT; Cell…

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

Yaster Breakfast Supplies is planning to manufacture and market a new toaster. After conducting extensive market surveys, the research department provides the following estimates: • a weekly demand of 302 at a price of $14 per toaster • a weekly demand of 196 at a price of $17 per toaster The financial department estimates that weekly fixed costs will be $1,438 and variable costs (cost per unit) will be $5. Assume: • the relationship between price and demand is linear • the cost function in linear Use your models to predict the marginal profit when Yaster is producing and selling 265 toasters per week. Round to the nearest cent. $ per toaster
3. The chief economist for Argus Corporation, a large appliance manufacturer, estimated the firm's short-run cost function for vacuum cleaners using an average variable cost function of the form AVC = a + bQ + cQ² where AVC = dollars per vacuum cleaner and Q = number of vacuum cleaners pro- duced each month. Total fixed cost each month is $180,000. The following results were obtained: DEPENDENT VARIABLE: AVC R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 19 VARIABLE INTERCEPT Q Q2 Show Transcribed Text PARAMETER ESTIMATE 191.93 -0.0305 0.0000024 Ĵ 0.7360 STANDARD ERROR 54.65 39.428 T-RATIO 3.512 0.00789 23.866 0.00000098 2.449 0.0001 P-VALUE 0.0029 0.0014 0.0262 a. Are the estimates â, b, and ĉ statistically significant at the 2 percent level of significance? b. Do the results indicate that the average variable cost curve is U-shaped? How do you know? c. If Argus Corporation produces 8,000 vacuum cleaners per month, what is the estimated average variable cost? Marginal cost? Total…
The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…
$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…
Make a scatterplot of the untransformed data and tell which kind ofrelationship the points seem to follow. Also name the best type oftransformation needed to "straighten" the plot.Note: Part of the transformation shouId involve subtracting 1900 from the year soyou're working with more manageable numbers.Now try some transformations to get the data as close to linear as possible.(Use your calculator to transform the data, and try scatterplots of the differenttransformations). Then find the regression line, r, and r2.Tell which transformation worked the best and back it up by showing:• A scatterplot of the transformed data with the least-squares regression equation andline, r, and r-squared.• A plot of the original data with the regression equation converted to a non-linearequation
A small business can produce a maximum of 46 of its product per month and sell them for $7 each. The variable costs are $2 per unit and fixed costs are $175 per month. d) Calculate the revenue at the break-even (round off to the nearest cent). $ e) Plot the revenue and cost functions. 400 300 200 100 Revenue/Cost($) 5 10 15 20 25 30 35 40 Quantity 45 50
Yaster Breakfast Supplies is planning to manufacture and market a new toaster. After conducting extensive market surveys, the research department provides the following estimates: • a weekly demand of 318 at a price of $13 per toaster • a weekly demand of 201 at a price of $16 per toaster The financial department estimates that weekly fixed costs will be $1,399 and variable costs (cost per unit) will be $3. Assume: • the relationship between price and demand is linear • the cost function in linear Use your models to predict the marginal profit when Yaster is producing and selling 271 toasters per week. Round to the nearest cent. $ per toaster
An automotive sales manager wishes to examine the relationship between age (years) and sales price ($) for used automobiles of a certain brand. The accompanying table contains data for a sample of a certain make and model that were listed for sale at a car shopping website. b. Fit a quadratic regression model to predict price and state the quadratic regression equation.

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