using excel>data>data analysis >Regression
we have
| SUMMARY OUTPUT |
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| Regression Statistics |
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| Multiple R |
0.97913 |
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| R Square |
0.958696 |
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| Adjusted R Square |
0.944928 |
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| Standard Error |
0.795822 |
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| Observations |
5 |
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| ANOVA |
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df |
SS |
MS |
F |
Significance F |
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| Regression |
1 |
44.1 |
44.1 |
69.63158 |
0.003608 |
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| Residual |
3 |
1.9 |
0.633333 |
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| Total |
4 |
46 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
| Intercept |
-3.4 |
1.067708 |
-3.18439 |
0.049925 |
-6.79792 |
-0.00208 |
| x |
2.1 |
0.251661 |
8.344554 |
0.003608 |
1.299102 |
2.900898 |
(f) R2 =0.9587
(g) About 95.87% variation in number of large pizzas can be
explained by the Number of students watching a professional
football game on TV.
(h) the linear correlation coefficient r = 0.9791
i) there is a strong positive linear relationship between number
of large pizzas and the Number of students watching a professional
football game on TV.
