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A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418
A multiple regression analysis produced the following tables.
Summary Output | ||||||||||
Regression Statistics | ||||||||||
MultipleR | 0.978724022 | |||||||||
RSquare | 0.957900711 | |||||||||
AdjustedRSquare | 0.952287472 | |||||||||
Standard Error | 67.67055418 | |||||||||
Observations | 18 | |||||||||
ANOVA | ||||||||||
df | SS | MS | F | SignificanceF | ||||||
Regression | 2 | 1562918.941 | 781459.5 | 170.6503 | 4.80907E-11 | |||||
Residual | 15 | 68689.55855 | 4579.304 | |||||||
Total | 17 | 1631608.5 | ||||||||
Coefficients | Standard Error | tStat | P-value | |||||||
Intercept | 1959.709718 | 306.4905312 | 6.39403 | 1.21E-05 | ||||||
X1 | -0.469657287 | 0.264557168 | -1.77526 | 0.096144 | ||||||
X2 | -2.163344882 | 0.278361425 | -7.77171 | 1.23E-06 |
Using= 0.01 to test the model, these results indicate that ____________.
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