1. The following cost and activity data were taken from factory records. Cost incurred (y) ($) 112 124 160 144 176 188 Activity (x) (Units) 8 10 14 14 18 20 a. Use the Least Squares Method of Linear Regression to calculate the fixed and variable cost. b. Calculate total cost if activity is 30 units.

$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…

The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated.          Team  Revenue  Value  Baltimore  245  1000  Boston  370  2100  Chicago White Sox  227  975  Cleveland  207  825  Detroit  254  1125  Houston  175  800  Kansas City  231  700  Los Angeles Angels  304  1300  Minnesota  223  895  New York Yankees  508  3200  Oakland  202  725  Seattle  250  1100  Tampa Bay  188  625  Texas  266  1220  Toronto  226  870  Arizona  211  840  Atlanta  267  1150  Chicago Cubs  302  1800  Cincinnati  227  885  Colorado…

The table below show data that has been collected from different fields from various farms in a certain valley. The table contains the grams of Raspberries tested and the amount of their Vitamin C content in mg. Find a linear model that express Vitamin C content as a function of the weight of the Raspberries. grams 50 60 70 80 90 100 110 Vitamin C content in mg 9.3 14.3 18.9 23.5 27.5 31.7 36.9 A) Find the regression equation: y = places x + Round your answers to 3 decimal B) Answer the following questions using your un-rounded regression equation. If we test 140 grams of raspberries what is the expected Vitamin C content? nearest tenth) mg (round to the

An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Total Cost ($) 3900 4700 5300 5700 6400 7100 The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation =-490.00 + 10.60x, a. The company's production schedule shows that 450 units must be produced next month. Predict the total cost for next month. ŷ* = * (to 2 decimals) b. Develop a 99% prediction interval for the total cost for next month. (to 2 decimals) 8 t- value decimals) (to 3 * (to 2 Spred decimals) Prediction Interval for an individual Value next month Ⓡ Production Volume…

The following table shows the approximate amount of trash produced in an industrialized country from 1980 to 2000. Let x represent the year after 1980 (1980 is year 0) and y represent the amount of trash (millions of tons). Year Million Tons 1980 150 1990 202 2000 220 (a) Draw the graph for this data. (b) Find the equation of the regression line for the data. (Round your answers to one decimal place.)y =   x +  (c) Use the equation to predict the amount of trash y that will be produced in 2010 and 2015. (Round your answers to one decimal place.)   2010:    _______million tons 2015:    _______ million tons

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.