Question

In: Statistics and Probability

Use the following multiple regression table to answer the following questions: Daily Demand High Temperature Price...

Use the following multiple regression table to answer the following questions:

Daily Demand High Temperature Price Day
144 73 $     1.00 1
90 64 $     1.00 0 0 = Weekend
108 73 $     1.00 1 1 = Weekday
120 82 $     1.00 1
54 45 $     1.20 0
69 54 $     1.20 1
126 86 $     1.20 1
99 70 $     1.20 0
48 73 $     1.50 0
33 66 $     1.50 0
90 75 $     1.50 1
81 61 $     1.50 1

1. What is the Y-intercept for the output of the data?

2. What is the R^2 of the data?

3. What is the adjusted R^2?

4. What is the high temperature coefficient of the output?

5. What is the price coefficient?

6. What is the Day Coefficient of the output, if the day is a weekday?

7. Using the data output, Calculate demand for burritos on a day that is 75 degrees, the price is $1.10...and it is a weekend.

8. Using the data output, Calculate demand for burritos on a day that is 75 degrees, the price is $1.10...and it is a weekday.

9. Using the data output, Calculate demand for burritos on a day that is 85 degrees, the price is $1.05...and it is a weekend.

10. Using the data output, Calculate demand for burritos on a day that is 85 degrees, the price is $1.05...and it is a weekday.

Solutions

Expert Solution

1. What is the Y-intercept for the output of the data?

Ans: The Y-intercept for the output of the data is 98.72.

2. What is the R^2 of the data?

Ans: The R^2 of the data = 0.83= 83.0%

3. What is the adjusted R^2?

Ans: The adjusted R^2= 0.766= 76.6%

4. What is the high-temperature coefficient of the output?

Ans:  The high-temperature coefficient of the output 1.1641.

5. What is the price coefficient?

Ans: The price coefficient is - 84.34.

6. What is the Day Coefficient of the output, if the day is a weekday?

Ans: The Day Coefficient of the output, if the day is a weekday is  24.10.

7. Using the data output, Calculate demand for burritos on a day that is 75 degrees, the price is $1.10...and it is a weekend.

Ans: The calculated demand for burritos on a day that is 75 degrees, the price is $1.10...and it is a weekend,

Daily Demand = 98.72 + 1.1641 *75 - 84.34 *1.10 + 24.10 *0 = 93.2535

8. Using the data output, Calculate demand for burritos on a day that is 75 degrees, the price is $1.10...and it is a weekday.

Ans: The calculated demand for burritos on a day that is 75 degrees, the price is $1.10...and it is a weekday,

Daily Demand = 98.72 + 1.1641 *75 - 84.34 *1.10 + 24.10 *1 = 117.3535

9. Using the data output, Calculate demand for burritos on a day that is 85 degrees, the price is $1.05...and it is a weekend.

Ans: The calculated demand for burritos on a day that is 85 degrees, the price is $1.05...and it is a weekend,

Daily Demand = 98.72 + 1.1641 *85 - 84.34 *1.05 + 24.10 *0 =109.1115

10. Using the data output, Calculate demand for burritos on a day that is 85 degrees, the price is $1.05...and it is a weekday

Ans: Calculate demand for burritos on a day that is 85 degrees, the price is $1.05...and it is a weekday,

Daily Demand = 98.72 + 1.1641 *85 - 84.34 *1.05 + 24.10 *1 = 133.2115.

orchestra answered 1 year ago
Next > < Previous

Related Solutions

To answer the following questions about LMT's stock price, use the following multiple regression analysis (Show...
To answer the following questions about LMT's stock price, use the following multiple regression analysis (Show work). SUMMARY OUTPUT Regression Statistics Multiple R 0.988681823 R Square 0.977491746 Adjusted R Square 0.976307101 Standard Error 10.49117857 Observations 61 ANOVA df SS MS F Significance F Regression 3 272454.9563 90818.31876 825.1347922 6.64497E-47 Residual 57 6273.695181 110.0648277 Total 60 278728.6514 Coefficients Standard Error t Stat P-value Lower 95% Intercept -1931.41991 243.9210071 -7.918218817 9.39705E-11 -2419.863299 CPI 8.914424548 1.13956563 7.822651293 1.35489E-10 6.632483736 Nasdaq 0.020174909 0.005420724 3.721810856...
Based on the EViews result in Table 1, answer the following questions. Table 1: Estimated Regression...
Based on the EViews result in Table 1, answer the following questions. Table 1: Estimated Regression Result Dependent Variable: LGDP Method: Least Squares Sample: 2000Q1 2012Q4 Included observations: 52 Variable Coefficient Std. Error t-Statistic Prob. LM2 0.796910 0.032383 24.60868 0.0000 LREER -0.060125 0.065220 -0.921892 0.3613 LSDR 0.051745 0.074618 0.693465 0.4914 LTBR 0.029174 0.007715 3.781436 0.0004 C 2.795406 0.427355 6.541183 0.0000 R-squared 0.986591 Mean dependent var 13.28409 Adjusted R-squared 0.985450 S.D. dependent var 0.429315 S.E. of regression 0.051786 Akaike info criterion...
Based on the EViews result in Table 1, answer the following questions. Table 1: Estimated Regression...
Based on the EViews result in Table 1, answer the following questions. Table 1: Estimated Regression Result Dependent Variable: LGDP Method: Least Squares Sample: 2000Q1 2012Q4 Included observations: 52 Variable Coefficient Std. Error t-Statistic Prob.   LM2 0.796910 0.032383 24.60868 0.0000 LREER -0.060125 0.065220 -0.921892 0.3613 LSDR 0.051745 0.074618 0.693465 0.4914 LTBR 0.029174 0.007715 3.781436 0.0004 C 2.795406 0.427355 6.541183 0.0000 R-squared 0.986591     Mean dependent var 13.28409 Adjusted R-squared 0.985450     S.D. dependent var 0.429315 S.E. of regression 0.051786     Akaike info criterion...
Table 1 below shows the schedule of demand and supply in the Market for Michigan wine. Use this table to answer the following questions.
Table 1 below shows the schedule of demand and supply in the Market for Michigan wine. Use this table to answer the following questions.MarketPrice (P )QuantityDemanded (Qd )Quantity Supplied (Qs )$01500$1012550$20100100$3075150$4050200$5025250$600300Explain why a price of $40 cannot be an equilibrium price in this market.Draw a figure (call it Figure 1) representing the Market for Michigan wine (i.e. demand and supply curves). Be sure to fully label the graph. You can assume that demand and supply are continuous between points.On Figure...
Use the below table to answer the following questions. Selling Price = $33.00 Sales Volume Fixed...
Use the below table to answer the following questions. Selling Price = $33.00 Sales Volume Fixed Cost Variable Cost 1,700 2,700 3,700 4,700 5,700 Profitability $ 30,800 7 $ 13,400 $ 39,400 $ 65,400 $ 91,400 $ 117,400 30,800 8 11,700 36,700 61,700 86,700 111,700 30,800 9 10,000 34,000 58,000 82,000 106,000 40,800 7 3,400 29,400 55,400 81,400 107,400 40,800 8 1,700 26,700 51,700 76,700 101,700 40,800 9 – 24,000 48,000 72,000 96,000 50,800 7 (6,600 ) 19,400 45,400 71,400...
Given the demand data answer the following questions after estimating your regression model of demand for...
Given the demand data answer the following questions after estimating your regression model of demand for Good 1. Quantity demanded of Good 1 is given by Q1 and the price of Good 1 is given by P1. The price of Good 2 is given by P2. Use a linear-linear functional form (i.e., do not transform the variables in anyway, such as with natural logarithms). Many economists refer to this as a “lin-lin” functional form. Evaluate elasticities at the sample means...
The following table illustrates the “Multiple Regression” output for “Selling Price” regressed on two independent (predictor)...
The following table illustrates the “Multiple Regression” output for “Selling Price” regressed on two independent (predictor) variables. Given the output, evaluate the significance, if any, of the individual independent variables at the 5% significance level. Make sure that you provide your rationale for your response for each independent variable. Also, indicate what the appropriate regression equation is for estimating the selling price Regression Statistics Multiple R 0.81968 R Square 0.67188 Anova DF SS MS F Regression 2 13313936968 6.7E+09 11.2619...
Use the SAT data and create a multiple regression table but this time as input use...
Use the SAT data and create a multiple regression table but this time as input use ONLY two variables: Letters and SAT. Answer questions 1 to 4. Choose the best fitting answer. Note: numbers are truncated unless specified. 1. If an incoming student has Letters = 7 and SAT = 1000 what would his predicted College GPA be? a. 1.99 b. 2.01 c. ​​2.18 d. 2.39 2. What is the approximate error of this prediction? a. 0.58 b. ​​0.61 c....
Use the SAT data and create a multiple regression table but this time as input use...
Use the SAT data and create a multiple regression table but this time as input use ONLY two variables: Letters and SAT. Answer questions 1 to 4. Choose the best fitting answer. Note: numbers are truncated unless specified. C ollege GPA HighSchl GPA SAT Letters 2.04 2.01 1070 5 2.56 3.4 1254 6 3.75 3.68 1466 6 1.1 1.54 706 4 3 3.32 1160 5 0.05 0.33 756 3 1.38 0.36 1058 2 1.5 1.97 1008 7 1.38 2.03 1104...
Answer the following questions: In the multiple explanatory variable regression model, define the partial correlation coefficients,...
Answer the following questions: In the multiple explanatory variable regression model, define the partial correlation coefficients, explain how they are interpreted, and how do the interpretations differ from the coefficients of the single explanatory variable regression model? Explain the t-tests of the partial correlation coefficients.  Have they changed? Explain how the coefficient of determination has changed and why. Explain how the null hypothesis has change for the F-test as compared to a single explanatory variable regression model.
ADVERTISEMENT
Subjects
ADVERTISEMENT
Latest Questions
ADVERTISEMENT