Nghiên cứu khả năng sinh trưởng, sinh sản, năng suất và chất lượng sữa của bò cái holstein friesian HF thuần, các thế hệ lai F1, F2 và F3 giữa HF và lai sind nuôi tại tỉnh Lâm Đồng

Durbin-Watson statistic = 0.711779

Lag 1 residual autocorrelation = 0.613465

Residual Analysis

Estimation	Validation
n	100
MSE	129.862
MAE	9.7617
MAPE	9.26129
ME	-0.717794
MPE	-5.01688

Có thể bạn quan tâm!

Djiko Soetrisno And Mahyuddin M. D. (1994), “Effect Of Graizing Systems On Reproductive Performance And The Net Return Of Milk Yielded By Shiwal – Friesian (Fs) Cows”, Proceeding Of The 7Th
Safi Jahanshahi A., Vaez Torshizi R., Kashan N. E. J. And Sayyad Nejad M. B. (2002), “Genetic Parameters For Milk Production Traits Of Iran Holsteins”, Proc. 7Th World Congress On Genetics
Nghiên cứu khả năng sinh trưởng, sinh sản, năng suất và chất lượng sữa của bò cái holstein friesian HF thuần, các thế hệ lai F1, F2 và F3 giữa HF và lai sind nuôi tại tỉnh Lâm Đồng - 22

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The StatAdvisor

The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF2NTN and 1 independent variables. The equation of the fitted model is

KLF2NTN = 468.184*EXP(-2.37464*EXP(-0.107303*TTNTN))

In performing the fit, the estimation process terminated successully after 7 iterations, at which point the residual sum of squares appeared to approach a minimum.

The R-Squared statistic indicates that the model as fitted explains 99.2372% of the variability in KLF2NTN. The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.2219%. The standard error of the estimate shows the standard deviation of the residuals to be 11.3957. 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 9.7617 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.

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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K h o i l u o n g b o F 2 ( k g )

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Thang tuoi

Nonlinear Regression - KLF3NTN

Dependent variable: KLF3NTN Independent variables: TTNTN

Function to be estimated: M*EXP(-A*EXP(-B*TTNTN)) Estimation method: Marquardt

Estimation stopped due to convergence of parameter estimates. Number of iterations: 7

Number of function calls: 31

Estimation Results

		Asymptotic	95.0%
		Asymptotic	Confidence	Interval
Parameter	Estimate	Standard Error	Lower	Upper
M	490.214	8.3501	473.801	506.946
A	2.37103	0.040621	2.29041	2.45165
B	0.107915	0.00355836	0.102852	0.116977

Analysis of Variance

Source

Sum of Squares	Df	Mean Square
Model	7.73198E6	3	2.57733E6
Residual	13610.8	97	140.317
Total	7.74559E6	100
Total (Corr.)	1.86283E6	99

R-Squared = 99.3094 percent

R-Squared (adjusted for d.f.) = 99.2143 percent Standard Error of Est. = 11.8455

Mean absolute error = 10.3627 Durbin-Watson statistic = 0.612331

Lag 1 residual autocorrelation = 0.684735

Residual Analysis

Estimation	Validation
n	100
MSE	140.317
MAE	10.3627
MAPE	10.4534
ME	-0.855128
MPE	-5.99548

The StatAdvisor

The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF3NTN and 1 independent variables. The equation of the fitted model is

KLF3NTN = 490.214*EXP(-2.37103*EXP(-0.107915*TTNTN))

In performing the fit, the estimation process terminated successully after 7 iterations, at which point the residual sum of squares appeared to approach a minimum.

The R-Squared statistic indicates that the model as fitted explains 99.3094% of the variability in KLF3NTN. The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.2143%. The standard error of the estimate shows the standard deviation of the residuals to be 11.8455. 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 10.3627 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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K h o i l u o n g b o F 3 ( k g )

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Thang tuoi

Nonlinear Regression - KLHFNTN

Dependent variable: KLHFNTN Independent variables: TTNTN

Function to be estimated: M*EXP(-A*EXP(-B*TTNTN)) Estimation method: Marquardt

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

Number of function calls: 32

Estimation Results

		Asymptotic	95.0%
		Asymptotic	Confidence	Interval
Parameter	Estimate	Standard Error	Lower	Upper
M	522.868	8.78139	505.71	540.607
A	2.41096	0.0410924	2.32941	2.49252
B	0.109181	0.00350163	0.103231	0.117131

Analysis of Variance

Source

Sum of Squares	Df	Mean Square
Model	8.71709E6	3	2.9057E6
Residual	14864.8	97	153.246
Total	8.73195E6	100
Total (Corr.)	2.1363E6	99

R-Squared = 99.3542 percent

R-Squared (adjusted for d.f.) = 99.2698 percent Standard Error of Est. = 12.3792

Mean absolute error = 10.8623

Durbin-Watson statistic = 0.512386

Lag 1 residual autocorrelation = 0.733998

Residual Analysis

Estimation	Validation
n	100
MSE	153.246
MAE	10.8623
MAPE	11.0878
ME	-0.975136
MPE	-6.6848

The StatAdvisor

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

KLHFNTN = 522.868*EXP(-2.41096*EXP(-0.10981*TTNTN))

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 99.3542% of the variability in KLHFNTN. The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.2698%. The standard error of the estimate shows the standard deviation of the residuals to be 12.3792. 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 10.8623 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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