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ANALISIS REGRESI LINIER DENGAN ADD-INS ANALYSIS TOOLPAK
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Pansuan
https://www.smartstat.info/tutorial/microsoft-excel/tutorial-excel-regresi-linier-sederhana-toolpak.html#embed_flash
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Perkembangan penyakit hawar bertepi (halo blight) yang disebabkan oleh bakteri Pseudomonas syringae pv. phaseolicola pada tanaman buncis
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https://www.apsnet.org/edcenter/disimpactmngmnt/topc/EpidemiologyTemporal/Pages/ModellingProgress.aspx
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Tabel 1
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Peng-amatan (HST)Tanaman Terinfeksi (%)
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101
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204
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3015
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4031
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5065
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6088
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7094
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Langkah-langkah melaksanakan analisis regrsi linier sederhana terhadap data hasil transformasi monit, logit, dan gompit perkembangan penyakit
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1) Mengubah data persentase tanaman terinfeksi menjadi proporsi tanaman terinfeksi
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Tabel 2
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Peng-amatan (HST)Tanaman Terinfeksi (%)Tanaman Terinfeksi (proporsi)
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1010,01
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2040,04
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30150,15
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40310,31
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50650,65
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60880,88
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70940,94
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2) Melakukan transformasi monit, logit, dan gompit terhadap data proporsi tanaman terinfeksi
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Tabel 3
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Peng-amatan (HST)Tanaman Terinfeksi (%)Tanaman Terinfeksi (proporsi)Transformasi monitTransformasi logitTransformasi gompit
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1010,010,0101-4,5951-1,5272
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2040,040,0408-3,1781-1,1690
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30150,150,1625-1,7346-0,6403
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40310,310,3711-0,8001-0,1580
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50650,651,04980,61900,8422
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60880,882,12031,99242,0570
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70940,942,81342,75152,7826
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3) Melakukan analisis regresi linier derajat satu masing-masing terhadap data hasil transormasi monit, logit, dan gompit
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3a) Analisis regresi linier derajat satu terhadap data hasil transformasi monit
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SUMMARY OUTPUT
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Tabel 4a
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Regression Statistics
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Multiple R0,927568035
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R Square0,8603824596
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Adjusted R Square
0,8324589515
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Standard Error
0,4581258619
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Observations
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Tabel 4b
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ANOVA
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dfSSMSF
Significance F
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Regression16,4668261756,46682617530,812119210,002607450353
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Residual51,0493965270,2098793053
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Total67,516222702
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Coefficients
Standard Error
t StatP-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
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Intercept-0,98404515750,3871870214-2,541524130,05179758911-1,9793410820,01125076666-1,9793410820,01125076666
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Peng-amatan (HST)
0,048058097930,0086577649985,5508665280,0026074503530,025802604490,070313591370,025802604490,07031359137
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Tabel 4c
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RESIDUAL OUTPUT
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Observation
Predicted Transformasi monit
Residuals
Standard Residuals
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1-0,50346417820,5135145141,227887383
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2-0,022883198830,063705193350,1523283199
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30,4576977805-0,295178851-0,7058152732
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40,9382787598-0,5672150784-1,35629319
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51,418859739-0,3690376146-0,8824222465
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61,8994407180,22082281770,5280192565
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72,3800216980,4333890191,03629575
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3b) Analisis regresi linier derajat satu terhadap data hasil transformasi logit
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SUMMARY OUTPUT
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Tabel 5a
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Regression Statistics
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Multiple R0,9976177303
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R Square0,9952411357
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Adjusted R Square
0,9942893629
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Standard Error
0,2029950538
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Observations
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Tabel 5b
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ANOVA
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dfSSMSF
Significance F
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Regression143,0889504743,088950471045,6708560,0000005313451213
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Residual50,20603495940,04120699187
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Total643,29498543
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Coefficients
Standard Error
t StatP-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
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Intercept-5,6684947280,1715621334-33,040477030,0000004773306626-6,109509231-5,227480224-6,109509231-5,227480224
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Peng-amatan (HST)
0,12405204910,00383624592732,336834360,00000053134512130,1141906650,13391343320,1141906650,1339134332
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Tabel 5c
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RESIDUAL OUTPUT
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Observation
Predicted Transformasi logit
Residuals
Standard Residuals
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1-4,427974237-0,1671456134-0,9019867343
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2-3,1874537460,0093999155870,05072582518
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3-1,9469332550,21233219971,145832209