Simple Regression - Col_2 vs. Col_1

Dependent variable: Col_2

Independent variable: Col_1

Linear model: Y = a + b*X

 

Coefficients

	LeastSquares	Standard	T
Parameter	Estimate	Error	Statistic	P-Value
Intercept	30,7882	79,5716	0,386924	0,7051
Slope	-5,16319	19,2167	-0,268682	0,7924

 

AnalysisofVariance

Source	SumofSquares	Df	MeanSquare	F-Ratio	P-Value
Model	4,25208		4,25208	0,07	0,7924
Residual	765,714		58,9011
Total (Corr.)	769,966

 

CorrelationCoefficient = -0,074313

R-squared = 0,552242percent

R-squared (adjusted for d.f.) = -7,09759 percent

Standard Error of Est. = 7,6747

Mean absolute error = 5,68966

Durbin-Watson statistic = 2,56002 (P=0,8849)

Lag 1 residual autocorrelation = -0,341531

 

The StatAdvisor

The output shows the results of fitting a linear model to describe the relationship between Col_2 and Col_1. The equation of the fitted model is

 

Col_2 = 30,7882 - 5,16319*Col_1

 

Since the P-value in the ANOVA table is greater or equal to 0,05, there is not a statistically significant relationship between Col_2 and Col_1 at the 95,0% or higher confidence level.

 

The R-Squared statistic indicates that the model as fitted explains 0,552242% of the variability in Col_2. The correlation coefficient equals -0,074313, indicating a relatively weak relationship between the variables. The standard error of the estimate shows the standard deviation of the residuals to be 7,6747. This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu.

 

The mean absolute error (MAE) of 5,68966 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 P-value is greater than 0,05, there is no indication of serial autocorrelation in the residuals at the 95,0% confidence level.