“Demetra” is a herbicide manufacturing company, based in Athens, Greece. A year ago “Demetra” branded a...
“Demetra” is a herbicide manufacturing company, based in Athens, Greece. A year ago “Demetra” branded a new herbicide call “Super-H” in 30 regional markets following the success of a pre-test. You are the chief economist of “Demetra”. The president of the company wants you to indicate how the demand for the new product is affected by a number of factors. Using the data set on the excel file answer the following question:
Regression equation-
Demand in cases= 1398.151 - 5.1518 Price per case+3.450833 Competitor Price+0.266443 Advertising+ 0.000139 household
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.819133 | |||||||
| R Square | 0.670979 | |||||||
| Adjusted R Square | 0.618336 | |||||||
| Standard Error | 64.74587 | |||||||
| Observations | 30 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 4 | 213722.1 | 53430.53 | 12.74575 | 8.67E-06 | |||
| Residual | 25 | 104800.7 | 4192.028 | |||||
| Total | 29 | 318522.8 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 1398.151 | 216.7901 | 6.44933 | 9.4E-07 | 951.6636 | 1844.639 | 951.6636 | 1844.639 |
| price per case | -5.1518 | 0.85531 | -6.02331 | 2.72E-06 | -6.91335 | -3.39026 | -6.91335 | -3.39026 |
| competitor price | 3.450833 | 1.921955 | 1.795481 | 0.084676 | -0.50751 | 7.409174 | -0.50751 | 7.409174 |
| Advertising | 0.266443 | 0.19839 | 1.343024 | 0.191331 | -0.14215 | 0.675035 | -0.14215 | 0.675035 |
| household | 0.000139 | 0.000143 | 0.967651 | 0.342488 | -0.00016 | 0.000434 | -0.00016 | 0.000434 |
1.Assuming a significance level (α level) of 1% which explanatory variables are statistically significant?
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“Demetra” is a herbicide manufacturing company, based in Athens,
Greece. A year ago “Demetra” branded a...
“Demetra” is a herbicide manufacturing company, based in Athens, Greece. A year ago “Demetra” branded a...
“Demetra” is a herbicide manufacturing company, based in Athens, Greece. A year ago “Demetra” branded a new herbicide call “Super-H” in 30 regional markets following the success of a pre-test. You are the chief economist of “Demetra”. The president of the company wants you to indicate how the demand for the new product is affected by a number of factors. Using the data set on the excel file answer the following questions: Regression equation- Demand in cases= 1398.151 - 5.1518 Price...
From the regression example discussed in class and based on the information below: Regression Statistics Multiple...
From the regression example discussed in class and based on the information below: Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.925 0.856 0.846 0.059 45 ANOVA P dfss SMS 3 0 .85 0.14 440.99 Significance F 0.00 Regression Residual Total 0.28 0.00 81.46 Intercept PRICE INCOME WEATHER Coefficients 13.040 -0.200 1.500 0.124 Standard Error 0.758 0.063 0.079 0.065 Stat P-value 17.1940 .000 -7.904 0.000 13.162 0.000 1.909 0.063 L ower 95% 11.508 -0.627 0.883 -0.007...
SUMMARY OUTPUT Regression Statistics Multiple R 0.633614748 R Square 0.401467649 Adjusted R Square 0.388732918 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.633614748 R Square 0.401467649 Adjusted R Square 0.388732918 Standard Error 7373785408 Observations ANOVA SS SS F Significance F 1 17141221.72 17141222 31.52541 1.02553E-06 4725555174.28 543727.1 48 4 2696396 1 17141221.72 17141222 3152541 Siewicowe Regression Residual Total Coefficients Standard Error Star P-value 2194.707265 332.0870736 6.608831 3.21E-08 40.870917 7279205668 5.61475 1.03E-06 Coefficients Standard Porn Photo Intercept Lower 95% Upper 95% Lower 95.096 Upper 95.0% 1526,634245 2862.780285 1526.634245 2862.780285 26.22704404 55.51478995 26.22704404 55.51478995 54 SUMMARY OUTPUT Regression...
What percent of the variance in well production is explained by knowing well depth and well...
What percent of the variance in well production is explained by knowing well depth and well age? SUMMARY OUTPUT Regression Statistics Multiple R 0.98711 R Square 0.974387 Adjusted R Square 0.965849 Standard Error 47.4523 Observations 9 ANOVA df SS MS F Significance F Regression 2 513960.7 256980.4 114.1262 1.68E-05 Residual 6 13510.32 2251.72 Total 8 527471.1 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -100.805 48.43281 -2.08133 0.082583 -219.316 17.70612 -219.316 17.70612 Well...
1.Based on the table above, how to intepret this regression analysis? 2. When we need to...
1.Based on the table above, how to intepret this regression
analysis?
2. When we need to look at the adjusted r2 and why?
3. How to conduct the hypothesis test?
0 Regression Statistics 1 Multiple R 2 R Square 3 Adjusted RS 0.853658537 0,97530483 0.951219512 4 Standard Err 0.191273014 5 Observation 6 7 ANOVA Significance F 0.220863052 df SS MS 0.713414634 0.356707 9 Regression 0 Residual 1 Total 2. 9.75 1 0.036585366 0.036585 0.75 2 Lower 95 % 3 Coefficients...
SUMMARY OUTPUT Regression Statistics Multiple R 0.985689515 R Square 0.97158382 Adjusted R Square 0.968940454 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.985689515 R Square 0.97158382 Adjusted R Square 0.968940454 Standard Error 754.6653051 Observations 48 ANOVA df SS MS F Significance F Regression 4 837320651.9 209330163 367.555599 1.23563E-32 Residual 43 24489348.08 569519.723 Total 47 861810000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -979.9824986 2587.408411 -0.3787506 0.70673679 -6197.988856 4238.02386 -6197.988856 4238.023859 Price (cents) -39.65930534 3.380682944 -11.731152 5.4685E-15 -46.47710226 -32.841508 -46.47710226 -32.84150842 Competitors Price (cents) 39.71320378 3.717321495 10.6832847 1.1179E-13 32.21651052 47.209897...
Regression Statistics Multiple R 0.896755 R Square 0.80417 Adjusted R Square 0.767452 Standard Error 51.04855 Observations...
Regression Statistics Multiple R 0.896755 R Square 0.80417 Adjusted R Square 0.767452 Standard Error 51.04855 Observations 20 ANOVA df SS MS F Significance F Regression 3 171220.5 57073.49 21.90118 6.56E-06 Residual 16 41695.28 2605.955 Total 19 212915.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 427.1938 59.60143 7.167509 2.24E-06 300.8444 553.5432 300.8444 553.5432 Temp (deg) -4.58266 0.772319 -5.93364 2.1E-05 -6.21991 -2.94542 -6.21991 -2.94542 Insulation (ins.) -14.8309 4.754412 -3.11939 0.006606 -24.9098 -4.75196 -24.9098 -4.75196...
3. United Park City Properties real estate investment firm took a random sample of five condominium...
3. United Park City Properties real estate investment firm took a random sample of five condominium units that recently sold in the city. The sales prices Y (in thousands of dollars) and the areas X (in hundreds of square feet) for each unit are as follows Y= Sales Price ( * $1000) 36 80 44 55 35 X = Area (square feet) (*100) 9 15 10 11 10 The owner wants to forecast sales on the basis of the...
Calculate the following statistics given the existing information (1 point per calculation): Regression Statistics Multiple R...
Calculate the following statistics given the existing information (1 point per calculation): Regression Statistics Multiple R R Square Adjusted R Square 0.559058 Standard Error Observations 30 ANOVA df SS MS F Significance F Regression 2 3609132796 19.38411515 6.02827E-06 Residual 27 2513568062 Total 29 6122700857 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -15800.8 57294.51554 -0.27578 0.784814722 CARAT 12266.83 1999.250369 6.135715 1.48071E-06 DEPTH 156.686 928.9461882 0.168671 0.867312915 Additionally interpret your results. Be sure to comment on Accuracy, significance...
ine college of Business at Northeast college is accumulating data as a first step in the...
ine college of Business at Northeast college is accumulating data as a first step in the preparation of next years Duaget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below. Month July August September October November December January February March April May Administrative Costs $ 129,801 83,013 225,880 216,594 258, 363 184,949 219,537 245, 380...
From the regression example discussed in class and based on the information below: Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.925 0.856 0.846 0.059 45 ANOVA P dfss SMS 3 0 .85 0.14 440.99 Significance F 0.00 Regression Residual Total 0.28 0.00 81.46 Intercept PRICE INCOME WEATHER Coefficients 13.040 -0.200 1.500 0.124 Standard Error 0.758 0.063 0.079 0.065 Stat P-value 17.1940 .000 -7.904 0.000 13.162 0.000 1.909 0.063 L ower 95% 11.508 -0.627 0.883 -0.007...
SUMMARY OUTPUT Regression Statistics Multiple R 0.633614748 R Square 0.401467649 Adjusted R Square 0.388732918 Standard Error 7373785408 Observations ANOVA SS SS F Significance F 1 17141221.72 17141222 31.52541 1.02553E-06 4725555174.28 543727.1 48 4 2696396 1 17141221.72 17141222 3152541 Siewicowe Regression Residual Total Coefficients Standard Error Star P-value 2194.707265 332.0870736 6.608831 3.21E-08 40.870917 7279205668 5.61475 1.03E-06 Coefficients Standard Porn Photo Intercept Lower 95% Upper 95% Lower 95.096 Upper 95.0% 1526,634245 2862.780285 1526.634245 2862.780285 26.22704404 55.51478995 26.22704404 55.51478995 54 SUMMARY OUTPUT Regression...
1.Based on the table above, how to intepret this regression
analysis?
2. When we need to look at the adjusted r2 and why?
3. How to conduct the hypothesis test?
0 Regression Statistics 1 Multiple R 2 R Square 3 Adjusted RS 0.853658537 0,97530483 0.951219512 4 Standard Err 0.191273014 5 Observation 6 7 ANOVA Significance F 0.220863052 df SS MS 0.713414634 0.356707 9 Regression 0 Residual 1 Total 2. 9.75 1 0.036585366 0.036585 0.75 2 Lower 95 % 3 Coefficients...
ine college of Business at Northeast college is accumulating data as a first step in the preparation of next years Duaget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below. Month July August September October November December January February March April May Administrative Costs $ 129,801 83,013 225,880 216,594 258, 363 184,949 219,537 245, 380...
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