I- Demand for a product is estimated to be Q=960 - 1.2P + 1.4Y +.003A where, Q and P are the quantity and price of the product respectively, Y is income, and A is the advertising expenditures. All the variables are in the natural logarithmic form and all the estimated coefficients are statistically significant. The average annual sale and the average price of the product are 60000 units and $8000 respectively. income elasticity of demand is advertising elasticity of demand A. Price elasticity of demand is is B. The optimum level of advertising spending for the firm is

I- Demand for a product is estimated to be Q=960 - 1.2P + 1.4Y +.003A where, Q and P are the quantity and price of the product respectively, Y is income, and A is the advertising expenditures. All the variables are in the natural logarithmic form and all the estimated coefficients are statistically significant. The average annual sale and the average price of the product are 60000 units and $8000 respectively. income elasticity of demand is advertising elasticity of demand A. Price elasticity of demand is is B. The optimum level of advertising spending for the firm is

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Using Excel, Big Poppa's estimates the weekly demand function for its BBQ sandwiches to be QD = 1,385.63 - 22.30P. In reference to the estimated slope coefficient for price, which of the following standard errors gives you the most confidence (i.e., largest t-statistics) that the estimated slope coefficient is close to the true slope coefficient? 37) _____ A) -22.0 B) 2.15 C) 1.12 D) 22.0 Could you put some explanations please.
2. Consider the following regression model and the result of estimation: distance Bo + B,angle + u %3D Where: distance = distance (in feet) traveled by a baseball, angle = the angle (in degrees) the baseball was hit, %3! u= regression error. Dependent Variable: DISTANCE Method: Least Squares Sample: 1 13 Included observations: 13 Variable Coefficient Std. Error t-Statistic Prob. C. ANGLE 32.93084 0.785542 5.146819 1.981191 0.0003 0.0731 169.4891 1.556309 a) Breusch-Godfrey test has been performed that produced the following result. Discuss the test result. Breusch-Godfrey Serial Correlation LM Test: Null hypothesis: No serial correlation at up to 2 lags F-statistic 6.534685 Prob. F(2,9) 7.698535 Prob. Chi-Square(2) 0.0177 0.0213 Obs R-squared b) RESET test has been performed that produced the following result. Discuss the test result. Ramsey RESET Test Equation: EQ01 Specification: DISTANCE C ANGLE Omitted Variables: Powers of fitted values from 2 to 3 Value 475.8260 60.71504 df…
Question 22 A computer depreciates 32% per year. A computer is bought for $1200. What is an equation to model this where V is the value of the computer and t is the years that passs since it is bought. Ov= 1200(-0.32)t OV= 1200(0.32) OV = 1200(1.32) OV = 1200(0.68) Question 23 A computer depreciates 32% per year. A computer is bought for $1200. What is an equation for the year since the comupter is bought as a function of the computer's worth? Ot=10g0.68 (1200)
You estimated a regression with the following output. Source | SS df MS Number of obs = 335 -------------+---------------------------------- F(1, 333) = 69555.83 Model | 211169628 1 211169628 Prob > F = 0.0000 Residual | 1010979.01 333 3035.97301 R-squared = 0.9952 -------------+---------------------------------- Adj R-squared = 0.9952 Total | 212180607 334 635271.28 Root MSE = 55.1 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.15183 .1674102 263.73 0.000 43.82251 44.48114 _cons | 31.63715 16.49849 1.92 0.056 -.8172452 64.09155…
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Agnes, a General Manager in XXX Company, estimated a multiplicative demand function of the form: using a cross-section data collected in the company sales on 30th June, 2019. The estimation results are as follows: (SEE IMAGE) Write down the estimated demand equation Interpret the coefficients and R2 value Describe any three managerial decisions that can be applied by the manager from the estimated demand function
The following table was generated from the sample data of 10 college students regarding the number of parking tickets the student receives in a semester, the student's age, and the student's GPA. The dependent variable is the student's GPA, the first independent variable (x1) is the number of parking tickets, and the second independent variable (x2) is the student's age. Intercept Coefficients Standard Error 15.034003 1.302739 1-Stat p-value 11.540305 0.000025 Number of Parking Tickets -0.026275 0.030254 Student's Age -0.638331 0.062922 -0.868470 0.418515 -10.144838 0.000053 Copy Data Step 1 of 2: Write the multiple regression equation for the computer output given. Round your answers to three decimal places. Answer How to enter your answer (opens in new window) + x + x2 Tables Keypad Keyboard Shortcuts
(e) Suppose you have been given the following ordinary least squares (OLS) regression result. Estimated Long Run Coefficients using the ARDL Approach ARDL (1,2,2,2,0,2) selected based on Akaike Information Criterion Dependent variable is LY 33 observations used for estimation from 1987 to 2019 T-Ratio [Prob.] 4.6671[0.000] 4.6678[0.051] 7.9897[0.043] -4.802[0.009] 2.3898[0.028] 1.0498[0.308] Regressor Coefficient Standard Error 0.36068 0.45447 LK 0.077280 LM 0.097363 0.48751 -0.41208 0.19057 0.52521 LE 0.061017 LF 0.085800 LT 0.079744 C 0.500320 where, Y = Economic growth K = Capital M = Employment E = Electricity consumption F = Foreign direct investment T= Technology (i) Write the regression equation. Interpret the estimated coefficients. (ii) Which explanatory variables are significant at the 1%, 5% and 10% level? Which variables are insignificant? Briefly explain.
You are given the following data: The regression equation is: A. -0.66 B. -1.20 (X'X)*¹ C. 1.12 O D. 2.06 = 1.3 2.1 0.8 -1.4 1.9 2.1 -1.4 s² = 0.86. T = 103 The correlation between ₁ and 3 (i.e., corr(Â₁, Â3)) is: -1.6] 1.9 (X'y) = 2.9 3.4 0.8 Yt = B₁ + B₂X2+ + B3X3t + Ut.
The following question refers to this regression equation (standard errors for each of the estimated coefficients are in parenthesis). Q=8,400-8" P+5" A+ 4** Px +0.05**1, (1,732) (2.29) (1.36) (1.75) (0.15) Q = Quantity demanded P = Price 1,100 Advertising expenditures, in thousands = 20 P = price of competitor's good = 600/= average monthly income 10,000 What is the advertising elasticity of demand? Round your answer to two decimal places. Your Answer: The t-statistic is computed by dividing the regression coefficient by the standard error of the coefficient. dividing the regression coefficient by the standard error of the estimate. dividing the standard error of the coefficient by the regression coefficient. dividing the R2 by the F-statistic. none of the specified answers are correct.
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.