If the objective function coefficient for Product_1 were decreased to a value of 3, which of the following would be TRUE?
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Refer to the following sensitivity report:
Variable Cells
Cell | Name | Final Value | Reduced Cost | Objective Coefficient | Allowable Increase |
Allowable Decrease |
$B$2 | Product_1 | 0 | −2 | 5 | 2 | 1E+30 |
$B$3 | Product_2 | 175 | 0 | 7 | 1E+30 | 1 |
$B$4 | Product_3 | 0 | −1.5 | 9 | 1.5 | 1E+30 |
Constraints
Cell | Name | Final Value | Shadow Price | Constraint R.H.Side | Allowable Increase |
Allowable Decrease |
$H$9 | Resource_A | 0 | 0 | 100 | 1E+30 | 100 |
$H$10 | Resource_B | 525 | 0 | 800 | 1E+30 | 275 |
$H$11 | Resource_C | 700 | 1.75 | 700 | 366.6666667 | 700 |
If the objective function coefficient for Product_1 were decreased to a value of 3, which of the following would be TRUE?
-
The optimal solution would change.
-
The optimal objective function value would stay the same.
-
Product_1 would enter the solution (the final value of the variable Product_1 would be greater than zero).
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?Macomb Inc. used Excel to run a least-squares regression analysis on the production cost data resulting in the following output: Regression StatisticsMultiple R 0.9834 R Square 0.9596 Observations 30 Coefficients Standard Error T Stat P-ValueIntercept 174,980 61,580 2.84 0.021 Production units (X) 11.53 0.9265 12.44 0.000 What total cost would Macomb predict for a month in which production is 2,000 units? Multiple Choice $174,900 $63,433 $198,040 $23,037Star, Incorporated, used Excel to run a least-squares regression analysis, which resulted in the following output: Regression Statistics Multiple R R Square Observations 0.9798 0.9560 30 Coefficients Standard Error 60,902 0.9237 Intercept Production (x) How much of the variation in cost is explained by production? 174,302 11.06 T Stat 2.86 11.97 P-Value 0.021 0.000
- Calculate a regression line for the data and indicate the forecast of sales for a store with advertising spending of R240,000? and R300,000? Store Advertising Sales 1 14 6 2 11 3 3 15 5 4 16 5 5 24 15 6 28 18 7 22 17 8 21 12 9 26 15 10 43 20 11 34 14 12 9 5Q2: An economist who estimated the Keynesian money demand function in logarithmic form using data from Sri Lankan economy, obtained the graph on the left by using predicted and actual money demand. While the model was very successful in predicting money demand until 2010 (small forecast errors), it predicts a consistently higher money demand than actually occurred since then (growing forecast errors each period). 48 Ln(M) 4.75 4.7 4.65 4.6 4.55 Actual 4.5 Predicted ...... 4.45 Help this economist who has trouble explaining this situation, even if you do not know anything about the economy in question. Can you answer this question with details please? 6toz StorThe H.W. Grant Corporation used regression analysis to predict the annual cost of indirect materials. The results were as follows: Indirect Materials Cost Explained by Units Produced Constant $15,675 Standard error of Y estimate $3,500 0.7776 Number of observations 16 X coefficient(s) 10.25 Standard error of coefficient(s) 2.195 What is the cost estimation equation? O A. Y= $12,175 + $10.25X O B. Y= $3,500 + $7.97X O C. Y= $19,175 + $4.67X O D. Y= $15,675 + $10.25X
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