Coefficients

	Least Squares	Standard	T
Parameter	Estimate	Error	Statistic	P-Value
Intercept	5,58154	0,00870724	641,024	0,0000
Slope	-0,100216	0,000726866	-137,874	0,0000

NOTE: intercept = ln(a)

Analysis of Variance

Source	Sum of Squares	Df	Mean Square	F-Ratio	P-Value
Model	6,67878		6,67878	19009,32	0,0000
Residual	0,00632416		0,000351342
Total (Corr.)	6,6851

Correlation Coefficient = -0,999527

R-squared = 99,9054 percent

R-squared (adjusted for d.f.) = 99,9001 percent

Standard Error of Est. = 0,0187441

Mean absolute error = 0,013798

Durbin-Watson statistic = 2,09645 (P=0,4849)

Lag 1 residual autocorrelation = -0,0532291

The StatAdvisor

The output shows the results of fitting an exponential model to describe the relationship between В and N. The equation of the fitted model is

В = exp(5,58154 - 0,100216*N)

Since the P-value in the ANOVA table is less than 0,05, there is a statistically significant relationship between В and N at the 95,0% confidence level.

The R-Squared statistic indicates that the model as fitted explains 99,9054% of the variability in В after transforming to a reciprocal scale to linearize the model. The correlation coefficient equals -0,999527, indicating a relatively strong relationship between the variables. The standard error of the estimate shows the standard deviation of the residuals to be 0,0187441. 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 0,013798 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.

Comparison of Alternative Models

Model	Correlation	R-Squared
Exponential	-0,9995	99,91%
Reciprocal-Y squared-X	0,9988	99,77%
Square root-X	-0,9965	99,30%
Squared-Y logarithmic-X	-0,9938	98,76%
Square root-Y	-0,9926	98,52%
Logarithmic-X	-0,9863	97,29%
Logarithmic-Y square root-X	-0,9841	96,84%
Logarithmic-Y squared-X	-0,9729	94,65%
Linear	-0,9710	94,28%
Reciprocal-Y	0,9677	93,64%
Squared-Y square root-X	-0,9608	92,32%
Square root-Y squared-X	-0,9361	87,63%
Multiplicative	-0,9284	86,20%
Squared-Y reciprocal-X	0,9085	82,54%
Squared-Y	-0,9010	81,18%
Squared-X	-0,8874	78,76%
Reciprocal-Y logarithmic-X	0,8269	68,38%
Reciprocal-X	0,8257	68,17%
Double squared	-0,7774	60,44%
Square root-Y reciprocal-X	0,7687	59,10%
S-curve model	0,7040	49,55%
Double reciprocal	-0,5699	32,48%
Double square root	<no fit>
Reciprocal-Y square root-X	<no fit>
Square root-Y logarithmic-X	<no fit>
Logistic	<no fit>
Log probit	<no fit>

Дата добавления: 2015-09-06