BrandA | BrandB | BrandC |
251.6 | 251.2 | 263.2 |
248.6 | 245.1 | 262.9 |
249.4 | 248 | 265 |
242 | 251.1 | 254.5 |
246.5 | 260.5 | 264.3 |
251.3 | 250 | 257 |
261.8 | 253.9 | 262.8 |
249 | 244.6 | 264.4 |
247.1 | 254.6 | 260.6 |
249.9 | 248.8 | 255.9 |
We can conduct a One-Way ANOVA to compare the true mean distances. However, your instructor wants to you analyze the data using multiple linear regression.
Write the estimated regression equation. Ensure that your regression coefficients are clearly defined and is reported in 3 decimal places.
SUMMARY OUTPUT | ||||||
Regression Statistics | ||||||
Multiple R | 0.303317 | |||||
R Square | 0.092001 | |||||
Adjusted R Square | -0.16743 | |||||
Standard Error | 5.470332 | |||||
Observations | 10 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 2 | 21.22431 | 10.61215 | 0.354631 | 0.713343 | |
Residual | 7 | 209.4717 | 29.92453 | |||
Total | 9 | 230.696 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 142.8814 | 154.1062 | 0.927162 | 0.384696 | -221.522 | 507.2847 |
BrandB | 0.013442 | 0.385285 | 0.03489 | 0.973142 | -0.89761 | 0.924497 |
BrandC | 0.396336 | 0.471888 | 0.839895 | 0.428717 | -0.7195 | 1.512174 |
estimated regression equation:
y^ = 142.881 + (0.013)Brand B + (0.396)Brand C
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