Luke used regression analysis to fit quadratic relations to monthly revenue, TR, and total cost, TC, data with the following 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).
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- The following information was gathered by the managerial accountant to estimate the 2nd quarter's utility cost: Months Utility Cost Direct labor hours January ₱8,520 1809 February ₱7,628 1421 March ₱5,712 912 April ₱6,746 1102 Summation of X 5,244 Summation of Y 28,606 Summation of XY 38,895,504 Summation of X-squared 7,337,870 Using the regression analysis, what is the estimated cost-function for the 2nd quarter?Least Squaes Regression Below is the production cost of Company B. In the future the company management intends to produce the following number of units: A. 56,500 B. 75,000 Determine the Total Cost of producing A and B based on the historical data below.Q5. Show that µY = Yµ − µY · 1. Data Mining Regression Evaluation chapter
- ABC, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. She determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales. List two advantages and two limitations of regression analysis.The regression equation to predict sales based on temperature is: Predicted sales = -2419.01+ 98.02 (temperature). A correct interpretation of the slope would be that 1. as temperature goes up by 1 degree, sales are predicted to go down by 2419.01. 2. as temperature goes down by 1 degree, sales are predicted to go up by 2419.01. 3. as temperature goes up by 1 degree, sales are predicted to go down by 98.02. 4. as temperature goes up by 1 degree, sales are predicted to go up by 98.02. 5. None of the answer choices provides a correct interpretation of the slope.Determine the mode choice (personal vehicle or bus system) for the following regression model: Utility Function: Umode – (8.333 x 10-4)*(Access time in sec) – (6.667 x 10-4)*(Wait Time in sec) – (5.00 x 10-4)*(Riding time in sec) – (1.40)*(Cost, $) PARAMETER PERSONAL VEHICLE CITY BUS SYSTEM MODE CONSTANT -0.01 -0.07 ACCESS TIME (SECS) 300 600 WAITING TIME (SECS) 0 900 RIDING TIME (SECS) 1,500 6,000 COST (DOLLARS) $1.50 $1.00
- Question 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.Given the following table, use the matrix method to derive the constant and slope parametersof the sample regression function: Module test mark = f (Weekly study hours). X and Y standfor weekly study hours and module test mark respectively.X (Weekly study hours) Y (Module test mark)1 402 403 454 605 706 75Bounds Inc. determined through regression analysis that its sales (S) are a function of the amount of advertising (measured in units) in two different media. This relationship is given by the following equation (X = newspapers, Y = magazines): S (X, Y) = 200X+100Y− 10X2− 20Y2 + 20XY Find the level of newspaper and magazine advertising that maximizes the firm’s sales. Calculate the firm’s sales at the optimal values of newspaper and magazine advertising determined in part (a).
- Given the estimated multiple regression equation ŷ = 6 + 5x1 + 4x2 + 7x3 + 8x4 what is the predicted value of Y in each case? a. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 b. x1 = 23, x2 = 18, x3 = 10, and x4 = 11 c. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 d. x1 = -10, x2 = 13, x3 = -8, and x4 = -16A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?Interpret the slope (m) and R2 values given in the equation of linear regression for both costs and benefits.