In: Economics
| San Francisco Bread Company | ||||
| Demand (Q) | Price (P) | Competitor Price (Px) | Advertising (Ad) | Income (I) |
| 599,201 | $6.66 | $5.96 | $206,647.00 | $52,955.00 |
| 572,258 | $8.01 | $5.30 | $207,025.00 | $54,391.00 |
| 558,142 | $7.53 | $6.16 | $207,422.00 | $48,491.00 |
| 627,973 | $6.51 | $7.56 | $216,224.00 | $51,219.00 |
| 593,024 | $6.20 | $7.15 | $217,954.00 | $48,685.00 |
| 565,004 | $7.28 | $6.97 | $220,139.00 | $47,219.00 |
| 596,254 | $5.95 | $5.52 | $220,215.00 | $49,775.00 |
| 652,880 | $6.42 | $6.27 | $220,728.00 | $54,932.00 |
| 596,784 | $5.94 | $5.66 | $226,603.00 | $48,092.00 |
| 657,468 | $6.47 | $7.68 | $228,620.00 | $54,929.00 |
| 519,866 | $6.99 | $5.10 | $230,241.00 | $46,057.00 |
| 612,941 | $7.72 | $5.38 | $232,777.00 | $55,239.00 |
| 621,707 | $6.46 | $6.20 | $237,300.00 | $53,976.00 |
| 597,215 | $7.31 | $7.43 | $238,765.00 | $49,576.00 |
| 617,427 | $7.36 | $5.28 | $241,957.00 | $55,454.00 |
| 572,320 | $6.19 | $6.12 | $251,317.00 | $48,480.00 |
| 602,400 | $7.95 | $6.38 | $254,393.00 | $53,249.00 |
| 575,004 | $6.34 | $5.67 | $255,699.00 | $49,696.00 |
| 667,581 | $5.54 | $7.08 | $262,270.00 | $52,600.00 |
| 569,880 | $7.89 | $5.10 | $275,588.00 | $50,472.00 |
| 644,684 | $6.76 | $7.22 | $277,667.00 | $53,409.00 |
| 605,468 | $6.39 | $5.21 | $277,816.00 | $52,660.00 |
| 599,213 | $6.42 | $6.00 | $279,031.00 | $50,464.00 |
| 610,735 | $6.82 | $6.97 | $279,934.00 | $49,525.00 |
| 603,830 | $7.10 | $5.30 | $287,921.00 | $49,489.00 |
| 617,803 | $7.77 | $6.96 | $289,358.00 | $49,375.00 |
| 529,009 | $8.07 | $5.76 | $294,787.00 | $48,254.00 |
| 573,211 | $6.91 | $5.96 | $296,246.00 | $46,017.00 |
Create a descriptive statistics table for our variables in word for submission (simply export from excel and cleaning it up a bit)
Create a table showing the variable names, coefficients, and p-values, indicating which variables are statistically significant for submission (export from excel and clean up a bit)
Write out the regression equation for sales here based on excel output:
Interpret the coefficient on our own price here:
If a manager increases our price by $2 then what is the predicted impact on sales?
a.
|
Variable |
Obs |
Mean |
Std |
Min |
Max |
|
Demand (Q) |
28.00 |
598545.79 |
35021.89 |
519866.00 |
667581.00 |
|
Price (P) |
28.00 |
6.89 |
0.70 |
5.54 |
8.07 |
|
Competitor Price (Px) |
28.00 |
6.19 |
0.82 |
5.10 |
7.68 |
|
Advertising (Ad) |
28.00 |
247665.86 |
29503.52 |
206647.00 |
296246.00 |
|
Income (I) |
28.00 |
50881.43 |
2849.10 |
46017.00 |
55454.00 |
b.
|
SUMMARY OUTPUT |
||||||
|
Regression Statistics |
||||||
|
Multiple R |
0.9308 |
|||||
|
R Square |
0.8664 |
|||||
|
Adjusted R Square |
0.8432 |
|||||
|
Standard Error |
13868.2389 |
|||||
|
Observations |
28 |
|||||
|
ANOVA |
||||||
|
df |
SS |
MS |
F |
Significance F |
||
|
Regression |
4.000 |
28692838225.528 |
7173209556.382 |
37.297 |
0.000 |
|
|
Residual |
23.000 |
4423545125.186 |
192328048.921 |
|||
|
Total |
27.000 |
33116383350.714 |
||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|
|
Intercept |
93833.8877 |
66959.6608 |
1.4013 |
0.1745 |
-44682.7242 |
232350.4995 |
|
Price (P) |
-19432.5811 |
3931.9650 |
-4.9422 |
0.0001 |
-27566.4704 |
-11298.6919 |
|
Competitor Price (Px) |
17711.9806 |
3359.2803 |
5.2726 |
0.0000 |
10762.7798 |
24661.1814 |
|
Advertising (Ad) |
0.2869 |
0.0940 |
3.0510 |
0.0057 |
0.0924 |
0.4815 |
|
Income (I) |
8.9996 |
0.9625 |
9.3505 |
0.0000 |
7.0086 |
10.9906 |
All of the variables are significant as the P-value is less than 0.05
c. Q= 93833.89-19432.58P+17711.98*Px+0.2869*Ad+8.99*I
D. Own price is negatively related with Qd . With one unit increase in price, the Qd decreases by 19433 units
E. For $2 increase in the predicted impact on sales Qd would decrease by -19432.58*2 = 38865 units