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- The table to the right contains price-demand and total cost data for the production of projectors, where p is the wholesale price (in dollars) of a projector for an annual demand of x projectors and C is the total cost (in dollars) of producing x projectors. Answer the following questions (A) - (D). (A) Find a quadratic regression equation for the price-demand data, using x as the independent variable. X 270 360 520 780 The fixed costs are $. (Round to the nearest dollar as needed.) ITTI y = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) Use the linear regression equation found in the previous step to estimate the fixed costs and variable costs per projector. The variable costs are $ per projector. (Round to the nearest dollar as needed.) (C) Find the break even points. The break even points are (Type ordered pairs. Use a comma to separate answers as needed. Round to the nearest integer as…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. Production Volume (units) Total Cost ($) 400 4,000 450 5,000 550 5,400 600 5,900 700 6,400 750 7,000 a. Use these data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations. Compute b1 and bo (to 1 decimal). bị bo Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations b. What is the variable cost per unit produced (to 2 decimal)? Do not round intermediate calculations 2$ c. Compute the coefficient of determination (to 3…SoCal Edison reported the following data for operating revenue and net income for 2001 through 2005. Year Operating Revenue (Millions), X Net Income (Millions), Y 2001 2270 96.9 2002 1482 89.1 2003 2138 103.9 2004 2260 81.6 2005 2600 78.1 Determine the least-squares regression line and interpret its slope. Estimate the net income if the operating revenue figure is $2500 million.
- Q4. The Omantel firm has estimate the Sales of fibre internet connections in Oman with the related to advertising expenditure made by the company over the past 26 months. Following is the firm estimated results of the regression equation. DEPENDENT VARIABLE: Y R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 26 0.85121212 8.747 0.0187 PARAMETER STANDARD VARIABLE ESTIMATE ERROR T-RATIO P-VALUE INTERCEPT 7.6 6.33232 1.200 0.2643969 3.53 0.52228 ? 0.0001428 a. What is the dependent and independent variables in the above regression equation of Omantel firm? b. Calculate the estimated t-ratio. Test the slope estimates for statistical significance at the 10 percent significance level. d. Interpret the coefficient of determination.Luke used regression analysis to fit quadratic relations to monthly revenue, TR, and total cost, TC, data with the following results, where Q is quantity. TR = -0.008Q² + 32Q TC = 0.005Q² +2.2Q + 10 The range of profitable demand is from Blank 1 units (round up) to Blank 2 units (round down). Note on rounding to whole number: The rounded values of 6,543.71 are 6,544 (round off or simply round), 6,543 (round down), and 6,544 (round up). Blank 1 Blank 2 Add your answer Add your answerQUESTION 3 When estimating a cubic short-run production function Q =AL³. + BL² using linear regression analysis, you must suppress the intercept term (regress through the origin). transform the equation into linear form by defining L3 and L2 as 1³ and 1², respectively. convert the right-hand-side variables to logarithms. both a and b both b and c
- A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.3-32 ** High-low method, regression analysis OBJECTIVES 4, 5 Rockhampton College has recently opened a restaurant as part of its hospitality major. For the first 10 weeks the manager did not estimate any costs but instead hoped revenues would cover costs. One of the new waiters, who happens to be taking a cost accounting class, suggests that the manager take the past known weekly costs and try to determine a cost equation by relating the cost to the number of customers served. The cost and customer data are as follows: Week Number of customers per week Weekly total costs of restaurant 751 $16800 745 16597 3 810 17 800 4 833 18 600 825 876 17900 19600 7 855 18900 8. 897 18500 925 20305 10 910 20000 The manager gives this information to the waiter, who runs a regression and gets the following equation: Weekly total restaurant costs = $2453 + ($19.04 × Number of customers per week) Required 1 Plot the relationship between number of customers per week and weekly total restaurant costs. 2…This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years State Effects? Time effects? (1) 0.41* (0.056) 1982-88 no no (2) (3) (4) -0.62** -0.76*** -0.42 (0.39) (0.33) (0.38) 0.023 (0.078) -0.014 (0.084) -0.023 -0.075 (0.053) (0.064) 0.034 -0.109*** (0.058) (0.058) no yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables Time effects=0 (5) -0.76** (0.36) 0.041 0.083 (0.111) (0.115) 0.006 0.015 (0.005) (0.011) -0.068* (0.016) 1.66* (0.66) 1982-88 1982-88 1982-88 1982-88 yes yes yes yes yes yes (6) -0.46 (0.39) -0.004 (0.022) 0.043 (0.101) 0.007 (0.005) -0.064*…
- As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…Interpret the slope (m) and R2 values given in the equation of linear regression for both costs and benefits.A linear regression model for the revenue data for a company is R=27.1t+203 where R is total annual revenue and t is time since 1/31/02 in years. 12 months 12 months 12 months Billions of Dollars Revenue Gross Profit 12 months 12 months ending 1/31/02ending 1/31/03ending 1/31/04 ending 1/31/05 ending 1/31/06 500- 201 49 236 54 255 60 500- 277 65 (A) Draw a scatter plot of the data and a graph of the model on the same axes. OA. B. O.C. KICB Q 2 316 72 500- oo D. 500- Q G