SlideShare a Scribd company logo
Metode waktu

  1          10
  1           9
  1           5
  1           6
  1           3
  2          11
  2          16
  2           9
  2           4
  2          13
  3          23
  3          25
  3          18
  3           9
  3           8

Oneway


                                                         Descriptives

 waktu
                                                                     95% Confidence Interval for
                                                                               Mean
              N             Mean      Std. Deviation   Std. Error   Lower Bound   Upper Bound      Minimum     Maximum
 1.00              3         8.0000         2.64575      1.52753          1.4276        14.5724         5.00      10.00
 2.00              3        12.0000         3.60555      2.08167          3.0433        20.9567         9.00      16.00
 3.00              3        10.0000         2.64575      1.52753          3.4276        16.5724         8.00      13.00
 4.00              3        22.0000         3.60555      2.08167        13.0433         30.9567        18.00      25.00
 Total            12        13.0000         6.23772      1.80067          9.0367        16.9633         5.00      25.00



         Test of Homogeneity of Variances

 waktu
 Levene
 Statistic        df1           df2          Sig.
      .267              3             8         .848
ANOVA

 waktu
                                                      Sum of
                                                      Squares    df        Mean Square    F       Sig.
 Between          (Combined)                           348.000        3        116.000   11.600     .003
 Groups           Linear Term    Contrast              240.000        1        240.000   24.000     .001
                                 Deviation
                                                       108.000        2         54.000    5.400     .033

 Within Groups                                          80.000         8        10.000
 Total                                                 428.000        11



     Contrast Coefficientsa

   a. Coefficients for contrast 1 are not displayed
      because the number of contrast coefficients
      does not equal the number of groups.



         Contrast Testsa

   a. Contrast 1 cannot be evaluated because
      the number of contrast coefficients does
      not equal the number of groups.



Post Hoc Tests
Multiple Comparisons

 Dependent Variable: waktu


                                              Mean
                                            Difference                               95% Confidence Interval
               (I) metode    (J) metode         (I-J)        Std. Error   Sig.     Lower Bound   Upper Bound
 Tukey HSD     1.00          2.00             -4.00000         2.58199      .455       -12.2684         4.2684
                             3.00             -2.00000         2.58199      .864       -10.2684         6.2684
                             4.00            -14.00000*        2.58199      .003       -22.2684        -5.7316
               2.00          1.00              4.00000         2.58199      .455        -4.2684       12.2684
                             3.00              2.00000         2.58199      .864        -6.2684       10.2684
                             4.00            -10.00000*        2.58199      .020       -18.2684        -1.7316
               3.00          1.00              2.00000         2.58199      .864        -6.2684       10.2684
                             2.00             -2.00000         2.58199      .864       -10.2684         6.2684
                             4.00            -12.00000*        2.58199      .007       -20.2684        -3.7316
               4.00          1.00             14.00000*        2.58199      .003         5.7316       22.2684
                             2.00             10.00000*        2.58199      .020         1.7316       18.2684
                             3.00             12.00000*        2.58199      .007         3.7316       20.2684
 LSD           1.00          2.00             -4.00000         2.58199      .160        -9.9541         1.9541
                             3.00             -2.00000         2.58199      .461        -7.9541         3.9541
                             4.00            -14.00000*        2.58199      .001       -19.9541        -8.0459
               2.00          1.00              4.00000         2.58199      .160        -1.9541         9.9541
                             3.00              2.00000         2.58199      .461        -3.9541         7.9541
                             4.00            -10.00000*        2.58199      .005       -15.9541        -4.0459
               3.00          1.00              2.00000         2.58199      .461        -3.9541         7.9541
                             2.00             -2.00000         2.58199      .461        -7.9541         3.9541
                             4.00            -12.00000*        2.58199      .002       -17.9541        -6.0459
               4.00          1.00             14.00000*        2.58199      .001         8.0459       19.9541
                             2.00             10.00000*        2.58199      .005         4.0459       15.9541
                             3.00             12.00000*        2.58199      .002         6.0459       17.9541
   *. The mean difference is significant at the .05 level.




Homogeneous Subsets


                                   waktu

                                                 Subset for alpha = .05
                   metode             N             1             2
 Tukey HSDa        1.00                    3       8.0000
                   3.00                    3      10.0000
                   2.00                    3      12.0000
                   4.00                    3                    22.0000
                   Sig.                               .455        1.000
 Duncan a          1.00                    3       8.0000
                   3.00                    3      10.0000
                   2.00                    3      12.0000
                   4.00                    3                    22.0000
                   Sig.                               .176        1.000
 Means for groups in homogeneous subsets are displayed.
   a. Uses Harmonic Mean Sample Size = 3.000.



Means Plots
22.00




                20.00




                18.00
Mean of waktu




                16.00




                14.00




                12.00




                10.00




                 8.00


                        1.00   2.00            3.00   4.00

                                      metode

More Related Content

Viewers also liked

project Cthis
project Cthisproject Cthis
project Cthis
Stephen Jones
 
Cthis E Brochure
Cthis E BrochureCthis E Brochure
Cthis E Brochure
Stephen Jones
 
Sapphire Online 2009 Or1005
Sapphire Online 2009 Or1005Sapphire Online 2009 Or1005
Sapphire Online 2009 Or1005
Shereen Zubair
 
ماذا نرى
ماذا نرىماذا نرى
ماذا نرى
Nour Zaid
 
Equipment Auction and Appraisal Services
Equipment Auction and Appraisal ServicesEquipment Auction and Appraisal Services
Equipment Auction and Appraisal Services
Kent State University
 
Take Time
Take TimeTake Time
Take Time
guest4fd216
 
Tragedy and Aristotle (ninth grade)
Tragedy and Aristotle (ninth grade)Tragedy and Aristotle (ninth grade)
Tragedy and Aristotle (ninth grade)
Titusville Area School District
 
Research Speech for cyber school
Research Speech for cyber schoolResearch Speech for cyber school
Research Speech for cyber school
Titusville Area School District
 
Four types of sentences
Four types of sentencesFour types of sentences
Four types of sentences
Titusville Area School District
 
المحاضرة التانية
المحاضرة التانيةالمحاضرة التانية
المحاضرة التانيةNour Zaid
 
Clauses fragments run-ons
Clauses fragments run-onsClauses fragments run-ons
Clauses fragments run-ons
Titusville Area School District
 
Titusville Ninth Grade Academy Presentation
Titusville Ninth Grade Academy PresentationTitusville Ninth Grade Academy Presentation
Titusville Ninth Grade Academy Presentation
Titusville Area School District
 
Point of View
Point of ViewPoint of View
Theme
ThemeTheme
Metode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet C
Metode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet CMetode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet C
Metode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet Cguest5b160ded
 

Viewers also liked (18)

project Cthis
project Cthisproject Cthis
project Cthis
 
Cthis E Brochure
Cthis E BrochureCthis E Brochure
Cthis E Brochure
 
Sapphire Online 2009 Or1005
Sapphire Online 2009 Or1005Sapphire Online 2009 Or1005
Sapphire Online 2009 Or1005
 
ماذا نرى
ماذا نرىماذا نرى
ماذا نرى
 
Equipment Auction and Appraisal Services
Equipment Auction and Appraisal ServicesEquipment Auction and Appraisal Services
Equipment Auction and Appraisal Services
 
Take Time
Take TimeTake Time
Take Time
 
Tragedy and Aristotle (ninth grade)
Tragedy and Aristotle (ninth grade)Tragedy and Aristotle (ninth grade)
Tragedy and Aristotle (ninth grade)
 
Research Speech for cyber school
Research Speech for cyber schoolResearch Speech for cyber school
Research Speech for cyber school
 
Four types of sentences
Four types of sentencesFour types of sentences
Four types of sentences
 
المحاضرة التانية
المحاضرة التانيةالمحاضرة التانية
المحاضرة التانية
 
Anova Slide
Anova SlideAnova Slide
Anova Slide
 
Anova Slide
Anova SlideAnova Slide
Anova Slide
 
Alecencio
AlecencioAlecencio
Alecencio
 
Clauses fragments run-ons
Clauses fragments run-onsClauses fragments run-ons
Clauses fragments run-ons
 
Titusville Ninth Grade Academy Presentation
Titusville Ninth Grade Academy PresentationTitusville Ninth Grade Academy Presentation
Titusville Ninth Grade Academy Presentation
 
Point of View
Point of ViewPoint of View
Point of View
 
Theme
ThemeTheme
Theme
 
Metode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet C
Metode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet CMetode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet C
Metode Pengajaran Oleh Pengawas Terhadap Anak Anak Pakeyet C
 

Similar to Metode Pengajaran Paket C Data Anova

Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
Franko Zzoto Medina
 
histograma
histograma histograma
histograma
Franko Zzoto Medina
 
Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
Franko Zzoto Medina
 
Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
Franko Zzoto Medina
 
Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
Franko Zzoto Medina
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
guest7afef24
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
guest578cc8
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
guest7afef24
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
guest578cc8
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
guest7afef24
 
Refuerzo
RefuerzoRefuerzo
Refuerzo
Kt Silva
 
Refuerzo
RefuerzoRefuerzo
Refuerzo
Kt Silva
 
VaR of Operational Risk
VaR of Operational RiskVaR of Operational Risk
VaR of Operational Risk
Rahmat Mulyana
 
Standard resistor and_capaciter_values
Standard resistor and_capaciter_valuesStandard resistor and_capaciter_values
Standard resistor and_capaciter_values
Vishwanada
 
Sabrina olivares
Sabrina olivaresSabrina olivares
Sabrina olivares
Universidad de Chile
 
Change Point Analysis (CPA)
Change Point Analysis (CPA)Change Point Analysis (CPA)
Change Point Analysis (CPA)
Taha Kass-Hout, MD, MS
 
SPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARK
SPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARKSPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARK
SPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARK
Tsuyoshi Horigome
 
Laboratorio valoración de cartera resolución enviar
Laboratorio valoración de cartera resolución enviarLaboratorio valoración de cartera resolución enviar
Laboratorio valoración de cartera resolución enviar
Edlin Rodriguez
 
Nuy Anova
Nuy AnovaNuy Anova
Nuy Anova
guestbed2c6
 
Plantilla metodos
Plantilla metodosPlantilla metodos
Plantilla metodos
Marcela Carrillo
 

Similar to Metode Pengajaran Paket C Data Anova (20)

Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
 
histograma
histograma histograma
histograma
 
Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
 
Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
 
Ejercicio 9
Ejercicio 9Ejercicio 9
Ejercicio 9
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
 
Tugas Anova Desti
Tugas Anova DestiTugas Anova Desti
Tugas Anova Desti
 
Refuerzo
RefuerzoRefuerzo
Refuerzo
 
Refuerzo
RefuerzoRefuerzo
Refuerzo
 
VaR of Operational Risk
VaR of Operational RiskVaR of Operational Risk
VaR of Operational Risk
 
Standard resistor and_capaciter_values
Standard resistor and_capaciter_valuesStandard resistor and_capaciter_values
Standard resistor and_capaciter_values
 
Sabrina olivares
Sabrina olivaresSabrina olivares
Sabrina olivares
 
Change Point Analysis (CPA)
Change Point Analysis (CPA)Change Point Analysis (CPA)
Change Point Analysis (CPA)
 
SPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARK
SPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARKSPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARK
SPICE MODEL of CR2_RL=4.7(Ohm) in SPICE PARK
 
Laboratorio valoración de cartera resolución enviar
Laboratorio valoración de cartera resolución enviarLaboratorio valoración de cartera resolución enviar
Laboratorio valoración de cartera resolución enviar
 
Nuy Anova
Nuy AnovaNuy Anova
Nuy Anova
 
Plantilla metodos
Plantilla metodosPlantilla metodos
Plantilla metodos
 

Recently uploaded

Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 
AppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSFAppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSF
Ajin Abraham
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Pitangent Analytics & Technology Solutions Pvt. Ltd
 
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing InstancesEnergy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Alpen-Adria-Universität
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
akankshawande
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
Jason Packer
 
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframeDigital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Precisely
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
Pablo Gómez Abajo
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Edge AI and Vision Alliance
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Neo4j
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
Ivo Velitchkov
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
Neo4j
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
ScyllaDB
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
Jakub Marek
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Alex Pruden
 

Recently uploaded (20)

Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 
AppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSFAppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSF
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
 
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing InstancesEnergy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
 
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframeDigital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
 
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and BioinformaticiansBiomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
 

Metode Pengajaran Paket C Data Anova

  • 1. Metode waktu 1 10 1 9 1 5 1 6 1 3 2 11 2 16 2 9 2 4 2 13 3 23 3 25 3 18 3 9 3 8 Oneway Descriptives waktu 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum 1.00 3 8.0000 2.64575 1.52753 1.4276 14.5724 5.00 10.00 2.00 3 12.0000 3.60555 2.08167 3.0433 20.9567 9.00 16.00 3.00 3 10.0000 2.64575 1.52753 3.4276 16.5724 8.00 13.00 4.00 3 22.0000 3.60555 2.08167 13.0433 30.9567 18.00 25.00 Total 12 13.0000 6.23772 1.80067 9.0367 16.9633 5.00 25.00 Test of Homogeneity of Variances waktu Levene Statistic df1 df2 Sig. .267 3 8 .848
  • 2. ANOVA waktu Sum of Squares df Mean Square F Sig. Between (Combined) 348.000 3 116.000 11.600 .003 Groups Linear Term Contrast 240.000 1 240.000 24.000 .001 Deviation 108.000 2 54.000 5.400 .033 Within Groups 80.000 8 10.000 Total 428.000 11 Contrast Coefficientsa a. Coefficients for contrast 1 are not displayed because the number of contrast coefficients does not equal the number of groups. Contrast Testsa a. Contrast 1 cannot be evaluated because the number of contrast coefficients does not equal the number of groups. Post Hoc Tests
  • 3. Multiple Comparisons Dependent Variable: waktu Mean Difference 95% Confidence Interval (I) metode (J) metode (I-J) Std. Error Sig. Lower Bound Upper Bound Tukey HSD 1.00 2.00 -4.00000 2.58199 .455 -12.2684 4.2684 3.00 -2.00000 2.58199 .864 -10.2684 6.2684 4.00 -14.00000* 2.58199 .003 -22.2684 -5.7316 2.00 1.00 4.00000 2.58199 .455 -4.2684 12.2684 3.00 2.00000 2.58199 .864 -6.2684 10.2684 4.00 -10.00000* 2.58199 .020 -18.2684 -1.7316 3.00 1.00 2.00000 2.58199 .864 -6.2684 10.2684 2.00 -2.00000 2.58199 .864 -10.2684 6.2684 4.00 -12.00000* 2.58199 .007 -20.2684 -3.7316 4.00 1.00 14.00000* 2.58199 .003 5.7316 22.2684 2.00 10.00000* 2.58199 .020 1.7316 18.2684 3.00 12.00000* 2.58199 .007 3.7316 20.2684 LSD 1.00 2.00 -4.00000 2.58199 .160 -9.9541 1.9541 3.00 -2.00000 2.58199 .461 -7.9541 3.9541 4.00 -14.00000* 2.58199 .001 -19.9541 -8.0459 2.00 1.00 4.00000 2.58199 .160 -1.9541 9.9541 3.00 2.00000 2.58199 .461 -3.9541 7.9541 4.00 -10.00000* 2.58199 .005 -15.9541 -4.0459 3.00 1.00 2.00000 2.58199 .461 -3.9541 7.9541 2.00 -2.00000 2.58199 .461 -7.9541 3.9541 4.00 -12.00000* 2.58199 .002 -17.9541 -6.0459 4.00 1.00 14.00000* 2.58199 .001 8.0459 19.9541 2.00 10.00000* 2.58199 .005 4.0459 15.9541 3.00 12.00000* 2.58199 .002 6.0459 17.9541 *. The mean difference is significant at the .05 level. Homogeneous Subsets waktu Subset for alpha = .05 metode N 1 2 Tukey HSDa 1.00 3 8.0000 3.00 3 10.0000 2.00 3 12.0000 4.00 3 22.0000 Sig. .455 1.000 Duncan a 1.00 3 8.0000 3.00 3 10.0000 2.00 3 12.0000 4.00 3 22.0000 Sig. .176 1.000 Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 3.000. Means Plots
  • 4. 22.00 20.00 18.00 Mean of waktu 16.00 14.00 12.00 10.00 8.00 1.00 2.00 3.00 4.00 metode