Independent variables. The equation of the fitted model is

U = 22,9495 + 0,00115653*Ypr - 1,61086*Wpr - 0,0703783*Ppr +

0,433354*time

Since the P-value in the ANOVA table is less than 0.01, there is a

statistically significant relationship between the variables at the

99% Confidence level.

The R-Squared statistic indicates that the model as fitted

explains 85,2393% of the variability in U. The adjusted R-squared

statistic, which is more suitable for comparing models with different

numbers of independent variables, is 82,6722%. The standard error of

the estimate shows the standard deviation of the residuals to be

0,507955. This value can be used to construct prediction limits for

new observations by selecting the Reports option from the text menu.

The mean absolute error (MAE) of 0,384621 is the average value of the

residuals. The Durbin-Watson (DW) statistic tests the residuals to

determine if there is any significant correlation based on the order

in which they occur in your data file. Since the DW value is greater

than 1.4, there is probably not any serious autocorrelation in the

residuals.

In determining whether the model can be simplified, notice that the

highest P-value on the independent variables is 0,0638, belonging to

Ypr. Since the P-value is less than 0.10, that term is statistically

significant at the 90% confidence level. Depending on the confidence

level at which you wish to work, you may or may not decide to remove

Ypr from the model.

Multiple Regression Analysis 2MNK2.M

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Dependent variable: M

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Standard T

Parameter Estimate Error Statistic P-Value

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CONSTANT 482,1 7,61753 63,2883 0,0000

Ypr*Ppr 0,000318273 0,0000182176 17,4706 0,0000

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Analysis of Variance

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Source Sum of Squares Df Mean Square F-Ratio P-Value

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Model 164986,0 1 164986,0 305,22 0,0000

Residual 14054,1 26 540,541

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Total (Corr.) 179040,0 27

R-squared = 92,1503 percent

R-squared (adjusted for d.f.) = 91,8484 percent

Standard Error of Est. = 23,2495

Mean absolute error = 18,7576

Durbin-Watson statistic = 1,19218

The StatAdvisor

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The output shows the results of fitting a multiple linear

regression model to describe the relationship between M and 1

Independent variables. The equation of the fitted model is

M = 482,1 + 0,000318273*Ypr*Ppr

Since the P-value in the ANOVA table is less than 0.01, there is a

statistically significant relationship between the variables at the