| Average Daily Temperature |
Quantity Sold (Thousands) |
| 70 |
30 |
| 75 |
28 |
| 80 |
40 |
| 90 |
52 |
| 93 |
57 |
| 98 |
54 |
| 72 |
27 |
| 75 |
38 |
| 75 |
32 |
| 80 |
46 |
| 90 |
49 |
| 95 |
51 |
Based on the above data we run a regression with confidence
interval as 90%. The regression outsput is shown below:
| SUMMARY OUTPUT |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| Regression Statistics |
|
|
|
|
|
|
|
| Multiple R |
0.930913559 |
|
|
|
|
|
|
|
| R Square |
0.866600054 |
|
|
|
|
|
|
|
| Adjusted R Square |
0.85326006 |
|
|
|
|
|
|
|
| Standard Error |
4.164371857 |
|
|
|
|
|
|
|
| Observations |
12 |
|
|
|
|
|
|
|
|
azq |
|
|
|
|
|
|
|
| ANOVA |
|
|
|
|
|
|
|
|
|
df |
SS |
MS |
F |
Significance F |
|
|
|
| Regression |
1 |
1126.58007 |
1126.58007 |
64.96254915 |
1.10291E-05 |
|
|
|
| Residual |
10 |
173.4199297 |
17.34199297 |
|
|
|
|
|
| Total |
11 |
1300 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 90.0% |
Upper 90.0% |
| Intercept |
-43.05885111 |
10.62154192 |
-4.053917167 |
0.002309003 |
-66.72512133 |
-19.3925809 |
-62.3099829 |
-23.80771933 |
| Average Daily Temperature |
1.027901524 |
0.127532238 |
8.05993481 |
1.10291E-05 |
0.74374199 |
1.312061058 |
0.796754301 |
1.259048747 |
So the regression equation is given by
Y = 1.027901524*X
- 43.05885111
Here Y = Quantity sold in
thousands
X = Average Daily
Temperature
Based on the above equation When X
= 75
We have Y = .027901524*75
- 43.05885111
= 34
So Quantity sold in thousands is
34
