1. AGUS 14 maret 2012 - IPB...

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LAMPIRAN

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Lampiran 1 Analisis probit menggunakan POLO-PC untuk data uji cendawan Metarhizium sp. isolat CE 3 terhadap imago WBC pada 2 HSP

POLO-PC (C) Copyright LeOra Software 1987 Input file > input: = Uji Metarhizium input: = Tiga taraf konsentrasi plus kontrol input: = Empat ulangan per perlakuan, 20 wereng imago input: = Data mortalitas 2 hari setelah perlakuan input: = Konsentrasi, jumlah serangga uji, jumlah serangga mati input: *Metar input: 0 80 29 input: 1000000 80 62 input: 10000000 80 73 input: 100000000 80 75 preparation dose log-dose subjects responses resp/subj Metar .00000 .000000 80. 29. .363 1000000.00000 6.000000 80. 62. .775 ************* 7.000000 80. 73. .913 ************* 8.000000 80. 75. .938 Number of preparations: 1 Number of dose groups: 3 Do you want probits [Y] ? Is Natural Response a parameter [Y] ? Do you want the likelihood function to be maximized [Y] ? LD's to calculate [10 50 90] > Do you want to specify starting values of the parameters [N] ? The probit transformation is to be used The parameters are to be estimated by maximizing the likelihood function Maximum log-likelihood -137.92403 parameter standard error t ratio Metar -2.4257512 1.0873313 -2.2309220 SLOPE .47824680 .15816516 3.0237178 Variance-Covariance matrix Metar SLOPE Metar 1.182289 -.1708114 SLOPE -.1708114 .2501622E-01

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Chi-squared goodness of fit test preparation subjects responses expected deviation probability Metar 80. 62. 63.240 -1.240 .790505 80. 73. 70.908 2.092 .886353 80. 75. 75.883 -.883 .948539 chi-square .8588 degrees of freedom 1 heterogeneity .86 Index of significance for potency estimation: g(.90)=.29592 g(.95)=.42016 g(.99)=.72569 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. We will use only the probabilities for which g is less than 0.5 Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar .11808E lower 762.08 52.774 upper .61153E .75389E LD95 Metar ******** lower************************ upper .00000 .00000 Uji Metarhizium Metar subjects 240 controls 80 log(L) =-137.9 slope =.478+.158 nat.resp.=.363+.000 heterogeneity =.86 g =.420 LD50=118079.500 limits: 52.774 to 753890.249 LD95=324724484.531 limits: 53728141.448 to .000 Stop - Program terminated.

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Chi-squared goodness of fit test preparation subject responses expected deviation probability Metar 80. 63. 64.234 -1.234 .802929 80. 73. 71.035 1.965 .887937 80. 75. 75.802 -.802 .947521 chi-square .7670 degrees of freedom 1 heterogeneity .77 Index of significance for potency estimation: g(.90)=.34198 g(.95)=.48556 g(.99)=.83865 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. We will use only the probabilities for which g is less than 0.5 Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar .37483E lower 3670.3 225.52 upper .15871E .19228E LD95 Metar ******** lower************************ upper .00000 .00000 Uji Metarhizium Metar subjects 240 controls 80 log(L)=-139.6 slope =.504+.179 nat.resp.=.525+.000 heterogeneity =.77 g =.486 LD50=374829.166 limits: 225.520 to 1922782.275 LD95=682808466.849 limits: 84330606.017 to .000 Stop - Program terminated.

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Chi-squared goodness of fit test preparation subjects responses expected deviation probability Metar 80. 63. 64.311 -1.311 .803890 80. 73. 70.984 2.016 .887303 80. 75. 75.804 -.804 .947556 chi-square .8070 degrees of freedom 1 heterogeneity .81 Index of significance for potency estimation: g(.90)=.34631 g(.95)=.49171 g(.99)=.84928 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. We will use only the probabilities for which g is less than 0.5 Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar .65699E lower 11312. 980.37 upper .24723E .29754E LD95 Metar ********* lower************************ upper .00000 .00000 Uji Metarhizium Metar subjects 240 controls 80 log(L)=-138.8 slope =.531+.190 nat.resp.=.575+.000 heterogeneity =.81 g =.492 LD50=656993.780 limits: 980.369 to 2975376.663 LD95=826261001.394 limits: 97177642.292 to .000 Stop - Program terminated.

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POLO-PC (C) Copyright LeOra Software 1987 Input file > input: = Uji Metarhizium input: = Tiga taraf konsentrasi plus kontrol input: = Empat ulangan per perlakuan, 20 wereng imago input: = Data mortalitas 5 hari setelah perlakuan input: = Konsentrasi, jumlah serangga uji, jumlah serangga mati input: *Metar input: 0 80 47 input: 1000000 80 77 input: 10000000 80 78 input: 100000000 80 79 preparation dose log-dose subjects responses resp/subj Metar .00000 .000000 80. 47. .588 1000000.00000 6.000000 80. 77. .963 ************* 7.000000 80. 78. .975 ************* 8.000000 80. 79. .988 Number of preparations: 1 Number of dose groups: 3 Do you want probits [Y] ? Is Natural Response a parameter [Y] ? Do you want the likelihood function to be maximized [Y] ? LD's to calculate [10 50 90] > Do you want to specify starting values of the parameters [N] ? The probit transformation is to be used The parameters are to be estimated by maximizing the likelihood function Maximum log-likelihood -81.749965 parameter standard error t ratio Metar -.26370415 1.7941442 -.14698047 SLOPE .26415278 .26232782 1.0069568 Variance-Covariance matrix Metar SLOPE Metar 3.218953 -.4675124 SLOPE -.4675124 .6881588E-01

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Chi-squared goodness of fit test preparation subjects responses expected deviation probability Metar 80. 77. 76.924 .076 .961549 80. 78. 78.137 -.137 .976718 80. 79. 78.938 .062 .986721 chi-square .0160 degrees of freedom 1 heterogeneity .02 Index of significance for potency estimation: g(.90)=2.6683 g(.95)=3.7886 g(.99)=6.5435 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar 9.96097 LD95 Metar .16796E Uji Metarhizium Metar subjects 240 controls 80 log(L)=-81.75 slope =.264+.262 nat.resp.=.588+.000 heterogeneity =.02 g = 3.789 Stop - Program terminated.

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Lampiran 5 Analisis probit menggunakan POLO-PC untuk data uji cendawan Metarhizium sp. isolat CE 3 terhadap imago WBC pada 106

POLO-PC (C) Copyright LeOra Software 1987 Input file > input: = Uji Metarhizium input: = Tujuh taraf pengamatan input: = Empat ulangan per perlakuan, 20 wereng imago input: = Data mortalitas harian input: = Pengamatan hari ke-, jumlah serangga uji, jumlah serangga mati input: *Metar input: 0 80 0 input: 1 80 15 input: 2 80 62 input: 3 80 63 input: 4 80 63 input: 5 80 77 input: 6 80 77 preparation dose log-dose subjects responses resp/subj Metar .00000 .000000 80. 0. .000 1.00000 .000000 80. 15. .188 2.00000 .301030 80. 62. .775 3.00000 .477121 80. 63. .788 4.00000 .602060 80. 63. .788 5.00000 .698970 80. 77. .963 6.00000 .778151 80. 77. .963 Number of preparations: 1 Number of dose groups: 6 Do you want probits [Y] ? Is Natural Response a parameter [Y] ? Do you want the likelihood function to be maximized [Y] ? LD's to calculate [10 50 90] > Do you want to specify starting values of the parameters [N] ? The probit transformation is to be used The parameters are to be estimated by maximizing the likelihood function Maximum log-likelihood -199.49230 parameter standard error t ratio Metar -.64998081 .13264755 -4.9000591 SLOPE 3.0967789 .28030324 11.047960 Variance-Covariance matrix Metar SLOPE Metar .1759537E-01 -.3139422E-01 SLOPE -.3139422E-01 .7856991E-01

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Chi-squared goodness of fit test preparation subjects responses expected deviation probability Metar 80. 15. 20.628 -5.628 .257852 80. 62. 48.890 13.110 .611121 80. 63. 63.683 -.683 .796040 80. 63. 71.017 -8.017 .887715 80. 77. 74.805 2.195 .935060 80. 77. 76.862 .138 .960778 chi-square 20.204 degrees of freedom 4 heterogeneity 5.0511 A large chi-square indicates a poor fit of the data by the probit analysis model. Large deviations for expected probabilities near 0 or 1 are especially troublesome. A plot of the data should be consulted. See D. J. Finney, "Probit Analysis" (1972), pages 70-75. Index of significance for potency estimation: g(.90)=.18808 g(.95)=.31901 g(.99)=.87722 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. We will use only the probabilities for which g is less than 0.5 Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar 1.62140 lower 1.01705 .77055 upper 2.11135 2.26604 LD95 Metar 5.50857 lower 3.98738 3.70551 upper 10.95437 17.10591 Uji Metarhizium Metar subjects 480 controls 80 log(L)=-199.5 slope=3.097+.280 nat.resp.=.000+.000 heterogeneity = 5.05 g =.319 LD50=1.621 limits: .771 to 2.266 LD95=5.509 limits: 3.706 to 17.106 Stop - Program terminated.

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POLO-PC (C) Copyright LeOra Software 1987 Input file > input: = Uji Metarhizium input: = Tujuh taraf pengamatan input: = Empat ulangan per perlakuan, 20 wereng imago input: = Data mortalitas harian input: = Pengamatan hari ke-, jumlah serangga uji, jumlah serangga mati input: *Metar input: 0 80 0 input: 1 80 28 input: 2 80 73 input: 3 80 73 input: 4 80 73 input: 5 80 78 input: 6 80 78 preparation dose log-dose subjects responses resp/subj Metar .00000 .000000 80. 0. .000 1.00000 .000000 80. 28. .350 2.00000 .301030 80. 73. .913 3.00000 .477121 80. 73. .913 4.00000 .602060 80. 73. .913 5.00000 .698970 80. 78. .975 6.00000 .778151 80. 78. .975 Number of preparations: 1 Number of dose groups: 6 Do you want probits [Y] ? Is Natural Response a parameter [Y] ? Do you want the likelihood function to be maximized [Y] ? LD's to calculate [10 50 90] > Do you want to specify starting values of the parameters [N] ? The probit transformation is to be used The parameters are to be estimated by maximizing the likelihood function Maximum log-likelihood -149.77164 parameter standard error t ratio Metar -.17067168 .12871576 -1.3259579 SLOPE 3.0760066 .30831129 9.9769509 Variance-Covariance matrix Metar SLOPE Metar .1656775E-01 -.3082253E-01 SLOPE -.3082253E-01 .9505585E-01

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Chi-squared goodness of fit test preparation subjects responses expected deviation probability Metar 80. 28. 34.579 -6.579 .432241 80. 73. 61.997 11.003 .774965 80. 73. 72.214 .786 .902677 80. 73. 76.292 -3.292 .953645 80. 78. 78.089 -.089 .976113 80. 78. 78.951 -.951 .986890 chi-square 14.912 degrees of freedom 4 heterogeneity 3.7280 A large chi-square indicates a poor fit of the data by the probit analysis model. Large deviations for expected probabilities near 0 or 1 are especially troublesome. A plot of the data should be consulted. See D. J. Finney, "Probit Analysis" (1972), pages 70-75. Index of significance for potency estimation: g(.90)=.17021 g(.95)=.28871 g(.99)=.79390 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. We will use only the probabilities for which g is less than 0.5 Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar 1.13628 lower .66275 .48213 upper 1.51132 1.62040 LD95 Metar 3.89244 lower 2.92963 2.73260 upper 6.64879 9.11528 Uji Metarhizium Metar subjects 480 controls 80 log(L)=-149.8 slope =3.076+.308 nat.resp.=.000+.000 heterogeneity = 3.73 g =.289 LD50=1.136 limits: .482 to 1.620 LD95=3.892 limits: 2.733 to 9.115 Stop - Program terminated.

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POLO-PC (C) Copyright LeOra Software 1987 Input file > input: = Uji Metarhizium input: = Tujuh taraf pengamatan input: = Empat ulangan per perlakuan, 20 wereng imago input: = Data mortalitas harian input: = Pengamatan hari ke-, jumlah serangga uji, jumlah serangga mati input: *Metar input: 0 80 0 input: 1 80 24 input: 2 80 75 input: 3 80 75 input: 4 80 75 input: 5 80 79 input: 6 80 79 preparation dose log-dose subjects responses resp/subj Metar .00000 .000000 80. 0. .000 1.00000 .000000 80. 24. .300 2.00000 .301030 80. 75. .938 3.00000 .477121 80. 75. .938 4.00000 .602060 80. 75. .938 5.00000 .698970 80. 79. .988 6.00000 .778151 80. 79. .988 Number of preparations: 1 Number of dose groups: 6 Do you want probits [Y] ? Is Natural Response a parameter [Y] ? Do you want the likelihood function to be maximized [Y] ? LD's to calculate [10 50 90] > Do you want to specify starting values of the parameters [N] ? The probit transformation is to be used The parameters are to be estimated by maximizing the likelihood function Maximum log-likelihood -125.29871 parameter standard error t ratio Metar -.30003838 .13284570 -2.2585479 SLOPE 3.7630401 .35466769 10.610045 Variance-Covariance matrix Metar SLOPE Metar .1764798E-01 -.3517918E-01 SLOPE -.3517918E-01 .1257892

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Chi-squared goodness of fit test preparation subjects responses expected deviation probability Metar 80. 24. 30.566 -6.566 .382074 80. 75. 63.801 11.199 .797507 80. 75. 74.607 .393 .932593 80. 75. 78.026 -3.026 .975324 80. 79. 79.208 -.208 .990103 80. 79. 79.657 -.657 .995708 chi-square 18.094 degrees of freedom 4 heterogeneity 4.5234 A large chi-square indicates a poor fit of the data by the probit analysis model. Large deviations for expected probabilities near 0 or 1 are especially troublesome. A plot of the data should be consulted. See D. J. Finney, "Probit Analysis" (1972), pages 70-75. Index of significance for potency estimation: g(.90)=.18262 g(.95)=.30975 g(.99)=.85177 "With almost all good sets of data, g will be substantially smaller than 1.0, and seldom greater than 0.4." - D. J. Finney, "Probit Analysis" (1972), page 79. We will use only the probabilities for which g is less than 0.5 Effective Doses dose limits 0.90 0.95 0.99 LD50 Metar 1.20153 lower .75439 .57274 upper 1.55788 1.66750 LD95 Metar 3.28731 lower 2.50504 2.34121 upper 5.50610 7.53535 Uji Metarhizium Metar subjects 480 controls 80 log(L)=-125.3 slope = 3.763+.355 nat.resp.=.000+.000 heterogeneity =4.52 g =.310 LD50=1.202 limits: .573 to 1.667 LD95=3.287 limits: 2.341 to 7.535 Stop - Program terminated.

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Lampiran 8 Hasil analisis ragam pada uji lanjutan cendawan Metarhizium sp. isolat CE 3 terhadap imago WBC hari ke-1

The ANOVA Procedure Class Level Information Class Levels Values trtment 4 MP6 MP8 MW6 MW8 Number of observations 16 The ANOVA Procedure Dependent Variable: yield Sum of Source DF Squares Mean Square F Value Pr > F Model 3 1188.000000 396.000000 2.21 0.1401 Error 12 2154.000000 179.500000 Corrected Total 15 3342.000000 R-Square Coeff Var Root MSE yield Mean 0.355476 178.6368 13.39776 7.500000 Source DF Anova SS Mean Square F Value Pr > F trtment 3 1188.000000 396.000000 2.21 0.1401 The ANOVA Procedure Duncan's Multiple Range Test for yield NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 179.5 Number of Means 2 3 4 Critical Range 20.64 21.61 22.19 Means with the same letter are not significantly different. Duncan Grouping Mean N trtment A 21.000 4 MW8 A 9.000 4 MW6 A 0.000 4 MP6 A 0.000 4 MP8

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The ANOVA Procedure Class Level Information Class Levels Values trtment 4 MP6 MP8 MW6 MW8 Number of observations 16 The ANOVA Procedure Dependent Variable: yield Sum of Source DF Squares Mean Square F Value Pr > F Model 3 24436.68750 8145.56250 109.00 <.0001 Error 12 896.75000 74.72917 Corrected Total 15 25333.43750 R-Square Coeff Var Root MSE yield Mean 0.964602 22.93759 8.644603 37.68750 Source DF Anova SS Mean Square F Value Pr > F trtment 3 24436.68750 8145.56250 109.00 <.0001 The ANOVA Procedure Duncan's Multiple Range Test for yield NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 74.72917 Number of Means 2 3 4 Critical Range 13.32 13.94 14.32 Means with the same letter are not significantly different. Duncan Grouping Mean N trtment A 90.000 4 MW8 B 60.750 4 MW6 C 0.000 4 MP6 C 0.000 4 MP8

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The ANOVA Procedure Class Level Information Class Levels Values trtment 4 MP6 MP8 MW6 MW8 Number of observations 16 The ANOVA Procedure Dependent Variable: yield Sum of Source DF Squares Mean Square F Value Pr > F Model 3 32468.97000 10822.99000 61.04 <.0001 Error 12 2127.58000 177.29833 Corrected Total 15 34596.55000 R-Square Coeff Var Root MSE yield Mean 0.938503 28.52778 13.31534 46.67500 Source DF Anova SS Mean Square F Value Pr > F trtment 3 32468.97000 10822.99000 61.04 <.0001 The ANOVA Procedure Duncan's Multiple Range Test for yield NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 177.2983 Number of Means 2 3 4 Critical Range 20.51 21.47 22.05 Means with the same letter are not significantly different. Duncan Grouping Mean N trtment A 95.750 4 MW8 A 87.500 4 MW6 B 2.500 4 MP8 B 0.950 4 MP6

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The ANOVA Procedure Class Level Information Class Levels Values trtment 4 MP6 MP8 MW6 MW8 Number of observations 16 The ANOVA Procedure Dependent Variable: yield Sum of Source DF Squares Mean Square F Value Pr > F Model 3 32016.40750 10672.13583 58.89 <.0001 Error 12 2174.83000 181.23583 Corrected Total 15 34191.23750 R-Square Coeff Var Root MSE yield Mean 0.936392 28.65099 13.46239 46.98750 Source DF Anova SS Mean Square F Value Pr > F trtment 3 32016.40750 10672.13583 58.89 <.0001 The ANOVA Procedure Duncan's Multiple Range Test for yield NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 181.2358 Number of Means 2 3 4 Critical Range 20.74 21.71 22.30 Means with the same letter are not significantly different. Duncan Grouping Mean N trtment A 95.750 4 MW8 A 87.500 4 MW6 B 2.500 4 MP8 B 2.200 4 MP6

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