Dependent variable: Col_2
Independent variables:
Col_1
Col_1^2
| Standard | T | |||
| Parameter | Estimate | Error | Statistic | P-Value |
| CONSTANT | 2,58514 | 0,688316 | 3,75575 | 0,0027 |
| Col_1 | 0,00038451 | 0,00029149 | 1,31912 | 0,2117 |
| Col_1^2 | -1,57025E-8 | 3,07015E-8 | -0,511458 | 0,6183 |
Analysis of Variance
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| Model | 0,152834 | 0,0764169 | 113,70 | 0,0000 | |
| Residual | 0,00806519 | 0,000672099 | |||
| Total (Corr.) | 0,160899 |
R-squared = 94,9874 percent
R-squared (adjusted for d.f.) = 94,152 percent
Standard Error of Est. = 0,0259249
Mean absolute error = 0,0175458
Durbin-Watson statistic = 1,43507 (P=0,1623)
Lag 1 residual autocorrelation = 0,0304468
The StatAdvisor
The output shows the results of fitting a multiple linear regression model to describe the relationship between Col_2 and 2 independent variables. The equation of the fitted model is
Col_2 = 2,58514 + 0,00038451*Col_1 - 1,57025E-8*Col_1^2
Since the P-value in the ANOVA table is less than 0,05, there is a statistically significant relationship between the variables at the 95,0% confidence level.
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