B� GIÁO D C VÀ ĐÀO T�O TRƯ�NG Đ I H�C NÔNG NGHI�P HÀ N�I � � � TR�N QUANG H NH � � � � NGHIÊN C�U KH� NĂNG SINH TRƯ�NG, SINH S�N, NĂNG SU�T VÀ CH�T LƯ"NG S#A C$A BÒ CÁI HOLSTEIN FRIESIAN (HF) THU�N, C[.]
B GIÁO D C VÀ ĐÀO T O TRƯ NG Đ I H C NÔNG NGHI P HÀ N I TR N QUANG H NH NGHIÊN C U KH NĂNG SINH TRƯ NG, SINH S N, NĂNG SU T VÀ CH T LƯ"NG S#A C$A BÒ CÁI HOLSTEIN FRIESIAN (HF) THU N, CÁC TH- H LAI F1, F2 VÀ F3 GI#A HF VÀ LAI SIND NUÔI T I T2NH LÂM Đ5NG Chuyên ngành: CHĂN NUÔI Đ NG V>T Mã sA: 62.62.40.01 LU>N ÁN TI-N SĨ NÔNG NGHI P NgưHi hưJng dLn khoa hPc: GS.TS ĐRNG VŨ BÌNH HÀ N I – 2010 i L I CAM ĐOAN Tôi xin cam đoan cơng trình nghiên c$u c&a riêng tơi Các s* li+u k.t qu0 nêu lu1n án trung th2c chưa t4ng đư5c cơng b* b7t kỳ cơng trình khác M:i s2 giúp ñ= cho vi+c th2c hi+n lu1n án ñã ñư5c c0m ơn tài li+u tham kh0o đư5c trích dBn lu1n án ñCu ñư5c chD rõ nguFn g*c xu7t x$ th2c t rõ ràng Tác giW luYn án Tr[n Quang H\nh ii L I C M ƠN TrưGc tiên xin chân thành c0m ơn GS.TS ĐIng Vũ Bình ngưKi hưGng dBn khoa h:c t1n tình hưGng dBn đóng góp nhiCu ý ki.n h.t s$c q báu Cho phép bày tO lKi c0m ơn tGi Ban giám hi+u, Vi+n Đào tQo Sau ĐQi h:c, Khoa Chăn nuôi & Nuôi trFng Th&y s0n, thVy cô, bQn đFng nghi+p BW mơn Di truyCn & Ch:n gi*ng V1t nuôi, d2 án PHE, TrưKng ĐQi h:c Nông nghi+p Hà NWi; Ban giám hi+u, Ban ch& nhi+m Khoa Chăn nuôi Thú y, TrưKng ĐQi h:c Tây Nguyên, ñã cho phép tQo m:i ñiCu ki+n thu1n l5i giúp đ= tơi q trình nghiên c$u hồn thành lu1n án Tơi xin c0m ơn Ban giám đ*c, phịng K] thu1t c&a Chi c^c Thú Y, Cơng ty Thanh Sơn (Vi+t Nam – Hà Lan), Công ty Cd phVn Sea tDnh Lâm ĐFng hW ni bị sea thành ph* Đà LQt, huy+n Đ$c Tr:ng, Đơn Dương, Lâm Hà, B0o LWc… ñã tQo m:i ñiCu ki+n thu1n l5i cho chúng tơi ti.n hành thí nghi+m, thu th1p s* li+u làm sg cho b0n lu1n án C0m ơn Gia đình bQn đFng nghi+p đWng viên khích l+, tQo m:i điCu ki+n thu1n l5i góp phVn cho b0n lu1n án đư5c hồn thành Hà N i, ngày tháng năm 2010 Tác giW luYn án Tr[n Quang H\nh iii M^C L^C Trang LKi cam ñoan i LKi c0m ơn ii M^c l^c iii Danh m^c che vi.t tht vi Danh m^c b0ng vii Danh m^c biiu đF ix Danh m^c hình x ĐjT VkN Đl Chương TnNG QUAN 1.1 CƠ St LÝ THUYvT CwA VkN Đl NGHIÊN CyU 1.1.1 Tính trQng s* lư5ng s2 di truyCn c&a tính trQng s* lư5ng 1.1.2 Lai tQo gi*ng 1.2 SINH TRƯtNG, SINH S~N, NĂNG SUkT VÀ CHkT LƯ€NG S•A CwA BỊ S•A 1.2.1 Sinh trưgng 7 1.2.2 Sinh s0n 13 1.2.3 Năng su7t ch7t lư5ng sea 18 1.3 TÌNH HÌNH NGHIÊN CyU TRONG VÀ NGỒI NƯ‡C 32 1.3.1 Tình hình nghiên c$u ngồi nưGc 32 1.3.2 Tình hình nghiên c$u nưGc 34 1.4 M T Sˆ YvU Tˆ Vl ĐIlU KI‰N TŠ NHIÊN T‹NH LÂM Đ•NG 38 1.4.1 ĐŽa hình 38 1.4.2 Khí h1u 38 iv 1.4.3 MWt s* nét vC tình hình chăn ni bị sea s• d^ng th$c ăn c&a tDnh Lâm ĐFng Chương V‘T LI‰U VÀ PHƯƠNG PHÁP NGHIÊN CyU 2.1 V‘T LI‰U NGHIÊN CyU 40 42 42 2.1.1 Bò HF (Holstein Friesian) 42 2.1.2 Nhóm bị lai hưGng sea 43 2.2 N I DUNG NGHIÊN CyU 45 2.3 PHƯƠNG PHÁP NGHIÊN CyU 46 2.3.1 Kh0 sinh trưgng 47 2.3.2 Kh0 sinh s0n 48 2.3.3 Kh0 s0n xu7t sea 49 2.3.4 Tiêu t*n th$c ăn 50 2.4 X— LÝ Sˆ LI‰U Chương KvT QU~ VÀ TH~O LU‘N 3.1 51 53 KH~ NĂNG SINH TRƯtNG CwA BÊ, BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 53 3.1.1 Kh0 sinh trưgng c&a nhóm bê, bò theo dõi 53 3.1.2 Kh0 sinh trưgng c&a nhóm bê, bị thí nghi+m 60 3.2 KH~ NĂNG SINH S~N CwA BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 77 3.2.1 Tudi ph*i gi*ng lVn ñVu 77 3.2.2 Tudi ñ˜ l$a ñVu 78 3.2.3 ThKi gian ph*i lQi sau ñ˜ 81 3.2.4 Kho0ng cách giea l$a ñ˜ 83 3.2.5 H+ s* ph*i gi*ng 86 3.3 KH~ NĂNG S~N XUkT S•A CwA BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 88 v 3.3.1 S0n lư5ng sea th2c t thKi gian cho sea 88 3.3.2 S0n lư5ng sea 305 ngày 92 3.3.3 S0n lư5ng sea tiêu chu™n (4% m=) 96 3.3.4 S0n lư5ng sea qua l$a ñ˜ 97 3.3.5 Năng su7t sea qua tháng c&a chu kỳ 305 ngày 100 3.3.6 Ch7t lư5ng sea 109 3.3.7 Tiêu t*n th$c ăn cho cho 1kg sea 118 KvT LU‘N VÀ Đl NGH› 124 KvT LU‘N 124 Đl NGH› 126 Các cơng trình cơng b* có liên quan ñ.n lu1n án 127 Tài li+u tham kh0o 128 Ph^ l^c 154 vi DANH M^C CÁC CH# VI-T T_T CK CSDT : Ch7t khô : ChD s* dài thân CSKL CSTM CV Cv% DTC ĐVT EXP F1 F2 F3 HSSS HF KHKT : ChD s* kh*i lư5ng : ChD s* trịn : Cao vây : H+ s* bi.n sai : Dài thân chéo : Đơn vŽ tính : Exponent – s* mũ : Con lai giea bò HF bò lai Sind : Con lai giea bò HF bò F1 : Con lai giea bò HF bò F2 : H+ s* s^t sea : Holstein Friesian : Khoa h:c k] thu1t KL Max : Kh*i lư5ng : Maximum – C2c ñQi Min NLTĐ NXB PTNT SE TB TT TTTA VCK VCKKM VN : Minimum – C2c tiiu : Năng lư5ng trao ñdi : Nhà xu7t b0n : Phát triin nông thôn : Standard Error – Sai s* tiêu chu™n : Trung bình : Tăng trưgng : Tdng tiêu t*n th$c ăn : V1t ch7t khô : V1t ch7t khơ khơng m= : Vịng ng2c : Trung bình vii DANH M^C CÁC B NG STT Tên b0ng Trang 2.1 S* mBu nghiên c$u c&a ñC tài 45 3.1 Kh*i lư5ng bò (kg) t4 sơ sinh ñ.n 24 tháng tudi 53 3.2 Tăng trưgng tuy+t ñ*i (g/ngày) tăng trưgng tương đ*i (%) c&a nhóm bị 3.3 55 Kích thưGc (cm) mWt s* chiCu đo qua tháng tudi c&a nhóm bị 58 3.4 MWt s* chD s* c7u tQo thi hình c&a nhóm bị 59 3.5 Kh*i lư5ng bị (kg) t4 sơ sinh ñ.n 24 tháng tudi 60 3.6 Tăng trưgng truy+t ñ*i (g/ngày) tăng trưgng tương ñ*i (%) c&a nhóm bị 3.7 Kích thưGc mWt s* chiCu đo (cm) c&a nhóm bị qua tháng tudi 3.8 63 66 MWt s* chD s* c7u tQo thi hình c&a nhóm bị qua tháng tudi 67 3.9 Hàm sinh trưgng c&a bò lai HF 70 3.10 Tudi, kh*i lư5ng tăng kh*i lư5ng c2c ñQi tQi ñiim u*n 76 3.11 Tudi ph*i gi*ng lVn ñVu 77 3.12 Tudi ñ˜ l$a ñVu 79 3.13 ThKi gian ph*i lQi (ngày) sau ñ˜ 82 3.14 Kho0ng cách giea l$a ñ˜ 83 3.15 H+ s* ph*i gi*ng c&a nhóm bị 86 3.16 S0n lư5ng sea th2c t thKi gian cho sea 89 3.17 S0n lư5ng sea th2c t thKi gian cho sea 90 3.18 S0n lư5ng sea (kg/chu kỳ 305 ngày) c&a nhóm bị 92 3.19 S0n lư5ng sea tiêu chu™n 305 ngày (4% m=) 96 viii 3.20 S0n lư5ng sea qua l$a ñ˜ 3.21 Năng su7t sea (kg) h+ s* s^t sea (HSSS) qua tháng c&a chu kỳ 305 ngày 3.22 98 101 Năng su7t sea (kg) h+ s* s^t sea (HSSS) theo tháng c&a chu kỳ 305 ngày 102 3.23 Tž l+ (%) su7t sea bò qua tháng so vGi c0 chu kỳ 107 3.24 Tž tr:ng c&a sea (s* li+u theo dõi) 109 3.25 Tž l+ v1t ch7t khô không m= c&a sea (s* li+u theo dõi) 110 3.26 Tž l+ m= sea (s* li+u theo dõi) 112 3.27 Tž l+ protein sea (s* li+u theo dõi) 114 3.28 Ch7t lư5ng sea l$a th$ nh7t c&a bị ni thí nghi+m 117 3.29 Tiêu t*n th$c ăn tinh cho kg sea 118 3.30 Tiêu t*n th$c ăn sg cho 1kg sea 119 3.31 Tiêu t*n th$c ăn cho 1kg sea (th$c ăn tinh th$c ăn sg) 120 3.32 ƯGc tính chi phí th$c ăn (v1t ch7t khơ) cho 1kg sea 121 ix DANH M^C CÁC BI`U Đ5 STT Tên biiu ñF Trang 3.1 Tăng trưgng tuy+t ñ*i c&a nhóm bị 56 3.2 Tăng trưgng tuy+t đ*i c&a nhóm bị 65 3.3 Tž l+ su7t sea theo tháng cho sea 108 3.4 Tž l+ su7t sea theo tháng cho sea 108 157 BWng Năng suqt soa theo tu[n (sA liˆu ni thí nghiˆm) F1 F2 F3 HF F1 F2 F3 HF F1 F2 F3 HF 123,80 129,30 143,40 144,30 11 132,50 133,00 141,10 149,40 21 97,60 106,20 110,20 117,60 31 80,40 83,20 93,80 96,50 120,30 129,60 144,00 152,00 12 133,30 138,50 154,00 156,90 22 98,80 101,90 116,60 115,60 32 81,60 89,00 93,90 101,80 124,20 133,60 148,10 145,20 13 128,10 129,10 139,20 141,10 23 101,50 105,90 107,60 115,60 33 76,50 73,10 82,00 84,60 Tu[n/Năng suqt soa 132,50 133,30 133,90 135,10 138,90 151,90 144,80 142,40 149,90 150,30 151,40 154,30 155,00 152,80 155,10 157,30 14 15 16 14 122,40 120,80 129,60 114,40 125,30 126,70 124,60 117,00 139,70 132,30 133,30 125,30 143,30 146,60 139,00 126,80 24 25 26 27 96,20 96,50 97,20 90,30 102,60 95,50 97,40 94,20 114,00 110,30 111,30 104,80 120,20 107,20 116,60 110,30 34 35 36 37 74,50 71,70 69,10 56,50 77,00 79,70 74,90 62,60 79,10 78,31 76,60 69,10 84,50 78,90 82,10 70,00 144,00 137,70 156,30 167,30 18 114,90 115,90 124,90 129,10 28 87,80 99,60 106,10 113,70 38 58,20 60,40 66,20 70,20 134,40 138,90 141,90 149,00 19 107,90 116,00 122,90 126,50 29 84,40 86,50 93,90 97,10 39 55,40 58,40 63,70 69,70 180 160 140 N ă n g s u > t s a (k g ) Nhóm bị F1 F2 F3 HF F1 120 F2 100 F3 HF 80 60 40 10 13 16 19 22 25 28 31 34 37 40 Tu@n Hình Năng suqt soa theo tu[n (sA liˆu ni thí nghiˆm) 10 126,70 136,80 154,00 157,00 20 112,20 114,90 124,70 128,80 30 84,30 89,30 93,80 100,10 40 55,50 59,10 61,90 66,40 158 BWng Hˆ sA tương quan gioa suqt soa thjc tn vJi chqt lưhng soa NSSTT c&a nhóm bị VCKKM M= Protein Tž tr:ng F1 0,70 0,60 0,70 0,09 F2 0,35 0,62 0,50 0,33 F3 0,77 0,46 0,29 0,84 HF 0,33 0,91 0,70 0,84 K-T QU CH Y HÀM GOMPERTZ TRÊN STATGRAPHICS CENTURION XV v 15.1.02 4.1 Knt quW ch\y hàm Gompert cla nhóm bò theo dõi Nonlinear Regression ™ KLF1TD Dependent variable: KLF1TD Independent variables: TTF1TD Function to be estimated: M*EXP( A*EXP( B*TTF1TD)) Estimation method: Marquardt Estimation stopped due to convergence of residual sum of squares Number of iterations: Number of function calls: 28 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 420.804 4.89388 411.722 430.067 A 2.37423 0.0233495 2.32837 2.4201 B 0.104943 0.00209512 0.100828 0.109058 Analysis of Variance Sum of Squares Df Mean Square Source Model 3.06549E7 1.02183E7 Residual 113987 567 201.035 Total 3.07689E7 570 Total (Corr.) 7.47363E6 569 R Squared = 98.3042 percent R Squared (adjusted for d.f.) = 98.2344 percent Standard Error of Est = 14.1787 Mean absolute error = 11.6272 Durbin Watson statistic = 0.836953 Lag residual autocorrelation = 0.580415 Residual Analysis Estimation Validation n 570 MSE 201.035 MAE 11.6272 MAPE 11.2176 ME 0.650562 MPE 5.75787 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF1DT and independent variables The equation of the fitted model is KLF1TD = 420.804*EXP( 2.37423*EXP( 0.104943*TTF1TD)) In performing the fit, the estimation process terminated successully after 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 98.3042% of the variability in KLF1TD The adjusted 159 R Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.2344% The standard error of the estimate shows the standard deviation of the residuals to be 14.1787 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 11.6272 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 Nonlinear Regression ™ KLF2TD Dependent variable: KLF2TD Independent variables: TTF2TD Function to be estimated: M*EXP( A*EXP( B*TTF2TD)) Estimation method: Marquardt Estimation stopped due to convergence of residual sum of squares Number of iterations: Number of function calls: 28 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 441.949 5.15766 431.999 452.258 A 2.35978 0.0240481 2.31255 2.40701 B 0.104381 0.00218541 0.100088 0.108673 Analysis of Variance Source Sum of Squares Df Mean Square Model 3.49492E7 1.16497E7 Residual 146839 587 250.151 Total 3.5096E7 590 Total (Corr.) 8.50234E6 589 R Squared = 98.463 percent R Squared (adjusted for d.f.) = 98.271 percent Standard Error of Est = 15.8162 24 160 Mean absolute error = 12.6605 Durbin Watson statistic = 0.586476 Lag residual autocorrelation = 0.705686 Residual Analysis Estimation Validation n 590 MSE 250.151 MAE 12.6605 MAPE 11.8795 ME 0.730073 MPE 6.17391 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF2TD and independent variables The equation of the fitted model is KLF2TD = 441.949*EXP( 2.35978*EXP( 0.104381*TTF2TD)) In performing the fit, the estimation process terminated successully after 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 98.463% of the variability in KLF2TD The adjusted R Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.271% The standard error of the estimate shows the standard deviation of the residuals to be 15.8162 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 12.6605 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o F (k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression ™ KLF3TD Dependent variable: KLF3TD Independent variables: TTF3TD Function to be estimated: M*EXP( A*EXP( B*TTF3TD)) Estimation method: Marquardt 18 24 161 Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic Standard Error 4.61922 0.020509 0.00184746 Parameter Estimate M 478.554 A 2.36299 B 0.105528 Analysis of Variance Sum of Squares Source Model 4.07927E7 Residual 119364 Total 4.09121E7 Total (Corr.) 9.86366E6 Df 577 580 579 Asymptotic Confidence Lower 469.703 2.32271 0.101899 95.0% Interval Upper 487.848 2.40328 0.109156 Mean Square 1.35976E7 206.87 R Squared = 98.7329 percent R Squared (adjusted for d.f.) = 98.7237 percent Standard Error of Est = 14.383 Mean absolute error = 11.8348 Durbin Watson statistic = 0.90785 Lag residual autocorrelation = 0.54493 Residual Analysis Estimation Validation n 580 MSE 206.87 MAE 11.8348 MAPE 10.7227 ME 0.748078 MPE 5.66541 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF3TD and independent variables The equation of the fitted model is KLF3TD = 478.554*EXP( 2.36299*EXP( 0.105528*TTF3TD)) In performing the fit, the estimation process terminated successully after 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 98.7329% of the variability in KLF3TD The adjusted R Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.7237% The standard error of the estimate shows the standard deviation of the residuals to be 14.383 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 11.8348 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially 162 K h o i lu o n g F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression ™ KLHFTD Dependent variable: KLHFTD Independent variables: TTHFTD Function to be estimated: M*EXP( A*EXP( B*TTHFTD)) Estimation method: Marquardt Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 498.823 3.38154 492.612 505.867 A 2.36657 0.0151509 2.33188 2.39127 B 0.107524 0.00135165 0.104875 0.110173 Analysis of Variance Sum of Squares Df Mean Square Source Model 1.00592E8 3.35307E7 Residual 344281 1282 268.55 Total 1.00936E8 1285 Total (Corr.) 2.43181E7 1284 R Squared = 98.5843 percent R Squared (adjusted for d.f.) = 98.5821 percent Standard Error of Est = 16.3875 Mean absolute error = 13.4649 Durbin Watson statistic = 0.883896 Lag residual autocorrelation = 0.557709 18 24 163 Residual Analysis Estimation Validation n 1285 MSE 268.55 MAE 13.4649 MAPE 11.0368 ME 0.788923 MPE 5.67459 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLHFTD and independent variables The equation of the fitted model is KLHFTD = 498.823*EXP( 2.36657*EXP( 0.107524*TTHFTD)) In performing the fit, the estimation process terminated successully after 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 98.5843% of the variability in KLHFTD The adjusted R Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.5821% The standard error of the estimate shows the standard deviation of the residuals to be 16.3875 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 13.4649 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially Plot of Fitted Model K h o i lu o n g b o H F ( k g ) 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 24 164 4.2 Knt quW ch\y hàm Gompert cla nhóm bị ni thí nghiˆm Nonlinear Regression ™ KLF1NTN Dependent variable: KLF1NTN 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: Number of function calls: 31 Estimation Results Asymptotic Standard Error 7.62082 0.0347721 0.00327434 Asymptotic Confidence Lower 429.544 2.23385 0.0981884 Parameter Estimate M 444.484 A 2.30287 B 0.104687 Analysis of Variance Source Sum of Squares Df Mean Square Model 6.13112E6 2.04371E6 Residual 9480.44 97 97.7365 Total 6.1406E6 100 Total (Corr.) 1.43782E6 99 R Squared = 99.3406 percent R Squared (adjusted for d.f.) = 99.327 percent Standard Error of Est = 9.88618 Mean absolute error = 8.64538 Durbin Watson statistic = 0.352689 Lag residual autocorrelation = 0.817525 Residual Analysis Estimation Validation n 100 MSE 97.7365 MAE 8.64538 MAPE 9.70995 ME 0.700724 MPE 5.2761 95.0% Interval Upper 459.834 2.37188 0.111186 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF1NTN and independent variables The equation of the fitted model is KLF1NTN = 444.484*EXP( 2.30287*EXP( 0.104687*TTNTN)) In performing the fit, the estimation process terminated successully after 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 99.3406% of the variability in KLF1NTN The adjusted R Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.327% The standard error of the estimate shows the standard deviation of the residuals to be 9.88618 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 8.64538 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially 165 K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression ™ KLF2NTN Dependent variable: KLF2NTN Independent variables: TTNTD Function to be estimated: M*EXP( A*EXP( B*TTNTN)) Estimation method: Marquardt Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 468.184 8.03365 452.489 484.422 A 2.37464 0.0410211 2.29322 2.45605 B 0.107303 0.00358498 0.102788 0.117018 Analysis of Variance Source Sum of Squares Df Mean Square Model 7.04729E6 2.3491E6 Residual 12596.6 97 129.862 Total 7.05989E6 100 Total (Corr.) 1.69594E6 99 R Squared = 99.2372 percent R Squared (adjusted for d.f.) = 99.2219 percent Standard Error of Est = 11.3957 Mean absolute error = 9.7617 18 24 166 Durbin Watson statistic = 0.711779 Lag 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 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF2NTN and 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 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 24 167 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: 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 Sum of Squares Df Mean Square Source Model 7.73198E6 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 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 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 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 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially 168 K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 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: 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 Sum of Squares Df Mean Square Source Model 8.71709E6 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 18 24 169 Durbin Watson statistic = 0.512386 Lag 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 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 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 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 Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o H F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 24 170 M T Sz HÌNH NH Hình Bị F1 Hình Bị F2 171 Hình Bị F3 Hình Bị HF