Khả năng sinh trưởng, sản xuất thịt của bò lai sind, F1 brahman × lai sind và F1 charolais × lai sind nuôi tại Đăk Lăk

residuals to determine if there is any significant correlation based on the order in which they occur in your data file.

The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters. These intervals are approximate and most accurate for large sample sizes. You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value 0. Intervals covering 0 correspond to coefficients which may well be removed form the model without hurting the fit substantially.

Plot of Fitted Model

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Nonlinear Regression - Lai Sind NTD

Dependent variable: Kg Independent variables:

Tháng

Function to be estimated: m*exp(-a*exp(-b*Tháng)) Initial parameter estimates:

m = 100.0

a = 1.0

b = 0.1

Estimation method: Marquardt

Estimation stopped due to convergence of residual sum of squares. Number of iterations: 7

Number of function calls: 33

Estimation Results

		Asymptotic	95.0%
		Asymptotic	Confidence	Interval
Parameter	Estimate	Standard Error	Lower	Upper
m	350.379	9.47535	331.711	369.046
a	2.60163	0.0418526	2.51917	2.68408
b	0.0925204	0.00373819	0.0851559	0.0998849

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Analysis of Variance

Source

Sum of Squares	Df	Mean Square
Model	6.29082E6	3	2.09694E6
Residual	32798.5	236	138.977
Total	6.32362E6	239
Total (Corr.)	1.59501E6	238

R-Squared = 97.9437 percent

R-Squared (adjusted for d.f.) = 97.9263 percent Standard Error of Est. = 11.7888

Mean absolute error = 9.61235 Durbin-Watson statistic = 0.633805

Lag 1 residual autocorrelation = 0.674314

Residual Analysis

Estimation	Validation
n	239
MSE	138.977
MAE	9.61235
MAPE	12.5865
ME	-0.397958
MPE	-6.15615

The StatAdvisor

The output shows the results of fitting a nonlinear regression model to describe the relationship between Kg and 1 independent variables. The equation of the fitted model is

Kg = 350.379*exp(-2.60163*exp(-0.0925204*Tháng))

In performing the fit, the estimation process terminated successully after 7 iterations, at which point the estimated coefficients appeared to converge to the current estimates.

The R-Squared statistic indicates that the model as fitted explains 97.9437% of the variability in Kg. The adjusted R- Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 97.9263%. The standard error of the estimate shows the standard deviation of the residuals to be 11.7888. 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 9.61235 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.

Plot of Fitted Model

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Nonlinear Regression - F1(Bra × LS) NTD

Dependent variable: Kg Independent variables:

Tháng

Function to be estimated: m*exp(-a*exp(-b*Tháng)) Initial parameter estimates:

m = 100.0

a = 1.0

b = 0.1

Estimation method: Marquardt

Estimation stopped due to convergence of residual sum of squares. Number of iterations: 7

Number of function calls: 33

Estimation Results

		Asymptotic	95.0%
		Asymptotic	Confidence	Interval
Parameter	Estimate	Standard Error	Lower	Upper
m	401.478	13.4843	374.914	428.043
a	2.50368	0.0519627	2.40131	2.60605
b	0.0934432	0.00485186	0.0838849	0.103001

Analysis of Variance

Source

Sum of Squares	Df	Mean Square
Model	8.74683E6	3	2.91561E6
Residual	76604.9	237	323.227
Total	8.82344E6	240
Total (Corr.)	2.16643E6	239

R-Squared = 96.464 percent

R-Squared (adjusted for d.f.) = 96.4342 percent Standard Error of Est. = 17.9785

Mean absolute error = 14.9868 Durbin-Watson statistic = 0.464853

Lag 1 residual autocorrelation = 0.764558

Residual Analysis

Estimation	Validation
n	240
MSE	323.227
MAE	14.9868
MAPE	15.3557
ME	-0.568747
MPE	-7.56073

The StatAdvisor

The output shows the results of fitting a nonlinear regression model to describe the relationship between Kg and 1 independent variables. The equation of the fitted model is

Kg = 401.478*exp(-2.50368*exp(-0.0934432*Tháng))

In performing the fit, the estimation process terminated successully after 7 iterations, at which point the estimated coefficients appeared to converge to the current estimates.

The R-Squared statistic indicates that the model as fitted explains 96.464% of the variability in Kg. The adjusted R- Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 96.4342%. The standard error of the estimate shows the standard deviation of the residuals to be 17.9785. 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 14.9868 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.

Plot of Fitted Model

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Tháng

Nonlinear Regression - F1(Char × LS) NTD

Dependent variable: Kg Independent variables:

Tháng

Function to be estimated: m*exp(-a*exp(-b*Tháng)) Initial parameter estimates:

m = 100.0

a = 1.0

b = 0.1

Estimation method: Marquardt

Estimation stopped due to convergence of residual sum of squares. Number of iterations: 8

Number of function calls: 38

Estimation Results

		Asymptotic	95.0%
		Asymptotic	Confidence	Interval
Parameter	Estimate	Standard Error	Lower	Upper
m	418.902	9.87592	399.446	438.358
a	2.58998	0.0473538	2.4967	2.68327
b	0.100544	0.00392725	0.0928075	0.108281

Analysis of Variance

Source

Sum of Squares	Df	Mean Square
Model	1.02258E7	3	3.4086E6
Residual	55535.8	237	234.328
Total	1.02813E7	240
Total (Corr.)	2.55602E6	239

R-Squared = 97.8273 percent

R-Squared (adjusted for d.f.) = 97.8089 percent Standard Error of Est. = 15.3078

Mean absolute error = 12.3686 Durbin-Watson statistic = 0.675345

Lag 1 residual autocorrelation = 0.661407

Residual Analysis

Estimation	Validation
n	240
MSE	234.328
MAE	12.3686
MAPE	12.5273
ME	-0.462302
MPE	-5.9931

The StatAdvisor

The output shows the results of fitting a nonlinear regression model to describe the relationship between Kg and 1 independent variables. The equation of the fitted model is

Kg = 418.902*exp(-2.58998*exp(-0.100544*Tháng))

In performing the fit, the estimation process terminated successully after 8 iterations, at which point the estimated coefficients appeared to converge to the current estimates.

The R-Squared statistic indicates that the model as fitted explains 97.8273% of the variability in Kg. The adjusted R- Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 97.8089%. The standard error of the estimate shows the standard deviation of the residuals to be 15.3078. 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 12.3686 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.

Plot of Fitted Model

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