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

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Please solve below question using regression analysis The Connors Company has assembled the following data pertaining to certain costs that cannot be easily identified as either fixed or variable. Connors Company has heard about a method of measuring cost functions called the high-low method and has decided to use it in this situation. Cost Hours $23,000 5,000 $26,000 6,450 $34,600 7,650 $42,000 10,000 $38,000 9,350 What is the estimated total cost at an operating level of 9,000 hours?
ABC Corporation has two cost drivers to estimate cost. Data are as follows:
Urgent and important question 3rd
Situation: The Ipod Touch has been out for two years now and a lot of data has been collected.Relevant Relationship:There is a functional relationship between Price of an IPod Touch,� and Weekly Demand,�.Below is a table of data that have been collected Price,�,($) Weekly Demand,�,(1,000s) 150 207 170 199 190 198 210 185 230 181 250 169 A.. Find the linear model that best fits this data using regression and enter the model below(for entry round the linear parameter value to nearest 0.01 and constant parameter to nearest 1)�=�(�)= B. The squared correlation coefficient �2 was 0.95(note: values less than 0.95 MAY mean the model is not appropriate for making predictions)Now answer these two questions using the UNROUNDED model parametersC. What does the model predict will be the weekly demand if the price of an ipod touch is $184 ? (nearest 100)D. According to the model at what should the price be set in order to have a weekly demand of 176,200 ipod Touches? $…
$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…
Situation: The Ipod Touch has been out for two years now and a lot of data has been collected.Relevant Relationship:There is a functional relationship between Price of an IPod Touch,p and Weekly Demand,s.Below is a table of data that have been collected Price,p ,($) Weekly Demand,s ,(1,000s) 150 209 170 205 190 200 210 192 230 177 250 175 A.. Find the linear model that best fits this data using regression and enter the model below(for entry round the linear parameter value to nearest 0.01 and constant parameter to nearest 1)s=T(p)= C. What does the model predict will be the weekly demand if the price of an ipod touch is $172 ? (nearest 100) can you answer these two questions
Make a scatterplot of the untransformed data and tell which kind ofrelationship the points seem to follow. Also name the best type oftransformation needed to "straighten" the plot.Note: Part of the transformation shouId involve subtracting 1900 from the year soyou're working with more manageable numbers.Now try some transformations to get the data as close to linear as possible.(Use your calculator to transform the data, and try scatterplots of the differenttransformations). Then find the regression line, r, and r2.Tell which transformation worked the best and back it up by showing:• A scatterplot of the transformed data with the least-squares regression equation andline, r, and r-squared.• A plot of the original data with the regression equation converted to a non-linearequation
Situation: The Ipod Touch has been out for two years now and a lot of data has been collected.Relevant Relationship:There is a functional relationship between Price of an IPod Touch,� and Weekly Demand,�.Below is a table of data that have been collected Price,�,($) Weekly Demand,�,(1,000s) 150 217 170 206 190 191 210 185 230 182 250 170 A.. Find the linear model that best fits this data using regression and enter the model below(for entry round the linear parameter value to nearest 0.01 and constant parameter to nearest 1)�=�(�)= B. The squared correlation coefficient �2 was 0.95(note: values less than 0.95 MAY mean the model is not appropriate for making predictions)Now answer these two questions using the UNROUNDED model parametersC. What does the model predict will be the weekly demand if the price of an ipod touch is $219 ? (nearest 100)D. According to the model at what should the price be set in order to have a weekly demand of 171,300 ipod Touches? $…
A biscuit production manager wants to investigate the linear relationship between the number of items produced and the production cost. To pursue his/her objective, the manager recorded the data on the number of items produced per day and the production cost per day (in thousands of Malaysian Ringgit) as shown in Table 3 Table 3 Days 1 2 4 6. 7 8 10 11 Number of items, (x) 26 67 86 17 10 44 53 40 77 80 20 20 Production cost, (y) 21 60 69 47 50 | 98 39 55 85 104 37 20 a) Name the TWo (2) most popular correlation coefficients. OPearson Oz-table OT-table Ochi-square Ospearman b) Calculate the correlation coefficient, r. ху x2 y2 26 21 676 44 60 2640 1936 3600 53 69 3657 2809 4761 47 1880 2209 77 50 3850 5929 2500 80 98 7840 6400 9604 20 39 780 400 1521 20 55 1100 400 3025 67 5695 4489 7225 86 104 8944 7396 10816 17 37 629 289 1369 10 20 200 100 400 E y² = Σχ ΣΥ- Σχy- Ex? = || || **Answer should be up to 4 decimal place (dp) r = c) Based on the correlation coefficient, robtained, make a…
(a) State the null hypothesis H0 and the alternative hypothesis H1 . H0: H1: (b) Determine the type of test statistic to use. ▼(Choose one) (c) Find the value of the test statistic. (Round to three or more decimal places.) (d) Find the p-value. (Round to three or more decimal places.) (e) Can we support the claim that the mean completion time of Fundash is not equal to the mean completion time of Coolsprint? Yes No
I need expert advice please especially with the graph. The table below shows monthly data collected on production costs and on the number of units produced over a twelve month period. Month Total Production Costs Level of Activity(Units Produced) July $230,000 3,500August 250,000 3,750September 260,000 3,800October 220,000 3,400November 340,000 5,800December 330,000 5,500January 200,000 2,900February 210,000 3,300March 240,000…
An automotive sales manager wishes to examine the relationship between age (years) and sales price ($) 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.

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