SlideShare a Scribd company logo
1 of 23
SPSS ACTIVITY
[object Object],[object Object],[object Object]
1. We will start  by using the SPSS DATA EDITOR To define and enter data
[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
[object Object]
1. SPSS DATA EDITOR To define and enter data
 
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GENDER MATHS-PMR MATHS-FINAL PRETEST SCORE POSTTEST SCORE PROC CONC 2 B  A  6.0 14.0 19.0 8.0 2 B  C  7.0 14.0 19.0 8.0 2 A  A  1.0 14.5 17.0 9.0 2 A  C  7.0 14.5 19.0 9.0 2 B  C  7.0 13.5 18.0 8.0 2 B  C  10.0 12.0 20.0 6.0 2 A  A  8.0 15.0 19.0 9.0 2 C  B  6.0 9.0 18.0 3.0 2 B  B  8.0 10.0 17.0 5.0 2 A  A  10.0 15.0 19.0 10.0 1 C  D  1.0 17.0 18.0 11.0 1 B  C  6.0 16.0 18.0 10.0 1 A  C  4.0 12.0 18.0 8.0 1 D  D  .0 12.0 17.0 7.0 1 D  C  8.0 15.0 15.0 8.0 1 C  A  4.0 14.0 20.0 8.0 1 B  A  8.0 12.0 15.0 7.0 1 B  B  7.0 11.0 17.0 5.0 1 C  B  8.0 15.0 18.0 10.0 1 A  A  6.0 16.0 18.0 11.0 1 D  C  6.0 15.0 17.0 9.0 1 C  C  6.0 14.0 20.0 8.0 1 A  A  15.0 18.0 20.0 12.0 1 A  A  7.0 16.0 17.0 10.0 1 A  B  9.0 18.0 18.0 12.0 1 A  A  16.0 20.0 20.0 14.0 1 A  B  7.0 15.0 16.0 9.0 1 A  A  20.0 20.0 19.0 14.0 1 B  B  9.0 19.5 18.0 14.0 1 A  B  12.0 19.0 17.0 13.0 1 A  A  16.0 18.0 15.0 12.0 2 B  A  6.0 8.0 19.0 2.0 2 A  B  7.0 10.0 19.0 4.0 2 A  B  9.0 10.0 18.0 4.0 2 B  B  9.0 10.0 19.0 4.0 2 B  C  4.0 8.0 19.0 2.0 2 C  C  4.0 8.0 18.0 2.0 2 B B  7.0 12.0 19.0 6.0
[object Object],[object Object]
 
 
 
 
 
 
 
 
[object Object],Quantitative Statistics:  correlation & t-test Results suggest that males could eat more chillies than females. But need to conduct t-test to determine if this difference is significant.

More Related Content

Viewers also liked

BID CE workshop 1 session 08 - Biodiversity Data Cleaning
BID CE workshop 1   session 08 - Biodiversity Data CleaningBID CE workshop 1   session 08 - Biodiversity Data Cleaning
BID CE workshop 1 session 08 - Biodiversity Data CleaningAlberto González-Talaván
 
Theory & Practice of Data Cleaning: Introduction to OpenRefine
Theory & Practice of Data Cleaning: Introduction to OpenRefineTheory & Practice of Data Cleaning: Introduction to OpenRefine
Theory & Practice of Data Cleaning: Introduction to OpenRefineBertram Ludäscher
 
data warehousing & minining 1st unit
data warehousing & minining 1st unitdata warehousing & minining 1st unit
data warehousing & minining 1st unitbhagathk
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessingSlideshare
 
Correlation in simple terms
Correlation in simple termsCorrelation in simple terms
Correlation in simple termsstats2analytics
 
Pa 298 measures of correlation
Pa 298 measures of correlationPa 298 measures of correlation
Pa 298 measures of correlationMaria Theresa
 
Correlation in physical science
Correlation in physical science Correlation in physical science
Correlation in physical science teenathankachen1993
 
Costaatt spss presentation
Costaatt spss presentationCostaatt spss presentation
Costaatt spss presentationkesterdavid
 
Correlation VS Causation
Correlation VS CausationCorrelation VS Causation
Correlation VS CausationColleen Carmean
 
One Way Anova
One Way AnovaOne Way Anova
One Way Anovashoffma5
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessingankur bhalla
 
Questionnaire Results and Analysis
Questionnaire Results and AnalysisQuestionnaire Results and Analysis
Questionnaire Results and Analysisantonia-roberts
 
Correlation and Regression
Correlation and RegressionCorrelation and Regression
Correlation and Regressionjasondroesch
 

Viewers also liked (19)

BID CE workshop 1 session 08 - Biodiversity Data Cleaning
BID CE workshop 1   session 08 - Biodiversity Data CleaningBID CE workshop 1   session 08 - Biodiversity Data Cleaning
BID CE workshop 1 session 08 - Biodiversity Data Cleaning
 
Theory & Practice of Data Cleaning: Introduction to OpenRefine
Theory & Practice of Data Cleaning: Introduction to OpenRefineTheory & Practice of Data Cleaning: Introduction to OpenRefine
Theory & Practice of Data Cleaning: Introduction to OpenRefine
 
data warehousing & minining 1st unit
data warehousing & minining 1st unitdata warehousing & minining 1st unit
data warehousing & minining 1st unit
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 
Correlation in simple terms
Correlation in simple termsCorrelation in simple terms
Correlation in simple terms
 
Pa 298 measures of correlation
Pa 298 measures of correlationPa 298 measures of correlation
Pa 298 measures of correlation
 
Correlation
CorrelationCorrelation
Correlation
 
Correlation in physical science
Correlation in physical science Correlation in physical science
Correlation in physical science
 
Costaatt spss presentation
Costaatt spss presentationCostaatt spss presentation
Costaatt spss presentation
 
Correlation VS Causation
Correlation VS CausationCorrelation VS Causation
Correlation VS Causation
 
Basic One-Way ANOVA
Basic One-Way ANOVABasic One-Way ANOVA
Basic One-Way ANOVA
 
One Way Anova
One Way AnovaOne Way Anova
One Way Anova
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 
Questionnaire Results and Analysis
Questionnaire Results and AnalysisQuestionnaire Results and Analysis
Questionnaire Results and Analysis
 
Anova ppt
Anova pptAnova ppt
Anova ppt
 
ANOVA II
ANOVA IIANOVA II
ANOVA II
 
Correlation
CorrelationCorrelation
Correlation
 
Data Analysis Using Spss T Test
Data Analysis Using Spss   T TestData Analysis Using Spss   T Test
Data Analysis Using Spss T Test
 
Correlation and Regression
Correlation and RegressionCorrelation and Regression
Correlation and Regression
 

Similar to Analyzing Social Science Data with SPSS - A Quick Guide

Missing Parts I don’t think you understood the assignment.docx
Missing Parts I don’t think you understood the assignment.docxMissing Parts I don’t think you understood the assignment.docx
Missing Parts I don’t think you understood the assignment.docxannandleola
 
Six Sigma Process Capability Study (PCS) Training Module
Six Sigma Process Capability Study (PCS) Training Module Six Sigma Process Capability Study (PCS) Training Module
Six Sigma Process Capability Study (PCS) Training Module Frank-G. Adler
 
BasicTools-Histogram.ppt
BasicTools-Histogram.pptBasicTools-Histogram.ppt
BasicTools-Histogram.pptWasiemHelaly
 
data analysis techniques and statistical softwares
data analysis techniques and statistical softwaresdata analysis techniques and statistical softwares
data analysis techniques and statistical softwaresDr.ammara khakwani
 
Spss in soil science
Spss in soil scienceSpss in soil science
Spss in soil scienceEmeni Joshua
 
Ultrasonic B Scan Laboratory Experiment Guidance
Ultrasonic B Scan Laboratory Experiment GuidanceUltrasonic B Scan Laboratory Experiment Guidance
Ultrasonic B Scan Laboratory Experiment GuidanceTony Toole
 
Six Sigma Statistical Process Control (SPC) Training Module
Six Sigma Statistical Process Control (SPC) Training ModuleSix Sigma Statistical Process Control (SPC) Training Module
Six Sigma Statistical Process Control (SPC) Training ModuleFrank-G. Adler
 
ASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COM
ASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COMASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COM
ASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COMjorge0048
 
Assignment #1 Directions The following series of questions co.docx
Assignment #1 Directions The following series of questions co.docxAssignment #1 Directions The following series of questions co.docx
Assignment #1 Directions The following series of questions co.docxfestockton
 
PA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docx
PA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docxPA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docx
PA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docxgerardkortney
 
Copyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docx
Copyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docxCopyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docx
Copyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docxbobbywlane695641
 
16 ch ken black solution
16 ch ken black solution16 ch ken black solution
16 ch ken black solutionKrunal Shah
 
Statistical Model to Predict IPO Prices for Semiconductor
Statistical Model to Predict IPO Prices for SemiconductorStatistical Model to Predict IPO Prices for Semiconductor
Statistical Model to Predict IPO Prices for SemiconductorXuanhua(Peter) Yin
 
Data Analysis Presentation with Justin Jones-POP Winter Conference
Data Analysis Presentation with Justin Jones-POP Winter ConferenceData Analysis Presentation with Justin Jones-POP Winter Conference
Data Analysis Presentation with Justin Jones-POP Winter ConferenceE. L. Haynes Public Charter School
 
Multiple regression in spss
Multiple regression in spssMultiple regression in spss
Multiple regression in spssDr. Ravneet Kaur
 
Ecm time series forecast
Ecm time series forecastEcm time series forecast
Ecm time series forecastAyapparaj SKS
 

Similar to Analyzing Social Science Data with SPSS - A Quick Guide (20)

Missing Parts I don’t think you understood the assignment.docx
Missing Parts I don’t think you understood the assignment.docxMissing Parts I don’t think you understood the assignment.docx
Missing Parts I don’t think you understood the assignment.docx
 
Six Sigma Process Capability Study (PCS) Training Module
Six Sigma Process Capability Study (PCS) Training Module Six Sigma Process Capability Study (PCS) Training Module
Six Sigma Process Capability Study (PCS) Training Module
 
BasicTools-Histogram.ppt
BasicTools-Histogram.pptBasicTools-Histogram.ppt
BasicTools-Histogram.ppt
 
data analysis techniques and statistical softwares
data analysis techniques and statistical softwaresdata analysis techniques and statistical softwares
data analysis techniques and statistical softwares
 
Spss in soil science
Spss in soil scienceSpss in soil science
Spss in soil science
 
Ultrasonic B Scan Laboratory Experiment Guidance
Ultrasonic B Scan Laboratory Experiment GuidanceUltrasonic B Scan Laboratory Experiment Guidance
Ultrasonic B Scan Laboratory Experiment Guidance
 
Guagerr
GuagerrGuagerr
Guagerr
 
Control charts
Control chartsControl charts
Control charts
 
Six Sigma Statistical Process Control (SPC) Training Module
Six Sigma Statistical Process Control (SPC) Training ModuleSix Sigma Statistical Process Control (SPC) Training Module
Six Sigma Statistical Process Control (SPC) Training Module
 
Msa training
Msa trainingMsa training
Msa training
 
ASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COM
ASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COMASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COM
ASSESSMENT CASE PAPER ANALYSIS / TUTORIALOUTLET DOT COM
 
Assignment #1 Directions The following series of questions co.docx
Assignment #1 Directions The following series of questions co.docxAssignment #1 Directions The following series of questions co.docx
Assignment #1 Directions The following series of questions co.docx
 
PA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docx
PA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docxPA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docx
PA 1c. Decision VariablesabcdCalculated values0.21110.531110.09760.docx
 
Copyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docx
Copyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docxCopyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docx
Copyright © 2016 John Wiley & Sons, Inc.Chapter 6 - Stat.docx
 
16 ch ken black solution
16 ch ken black solution16 ch ken black solution
16 ch ken black solution
 
Statistical Model to Predict IPO Prices for Semiconductor
Statistical Model to Predict IPO Prices for SemiconductorStatistical Model to Predict IPO Prices for Semiconductor
Statistical Model to Predict IPO Prices for Semiconductor
 
Spc training
Spc training Spc training
Spc training
 
Data Analysis Presentation with Justin Jones-POP Winter Conference
Data Analysis Presentation with Justin Jones-POP Winter ConferenceData Analysis Presentation with Justin Jones-POP Winter Conference
Data Analysis Presentation with Justin Jones-POP Winter Conference
 
Multiple regression in spss
Multiple regression in spssMultiple regression in spss
Multiple regression in spss
 
Ecm time series forecast
Ecm time series forecastEcm time series forecast
Ecm time series forecast
 

Recently uploaded

Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptxSherlyMaeNeri
 
ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4MiaBumagat1
 
Gas measurement O2,Co2,& ph) 04/2024.pptx
Gas measurement O2,Co2,& ph) 04/2024.pptxGas measurement O2,Co2,& ph) 04/2024.pptx
Gas measurement O2,Co2,& ph) 04/2024.pptxDr.Ibrahim Hassaan
 
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxBarangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxCarlos105
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPCeline George
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersSabitha Banu
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfTechSoup
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17Celine George
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTiammrhaywood
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxAnupkumar Sharma
 
Proudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptxProudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptxthorishapillay1
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)lakshayb543
 
Keynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-designKeynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-designMIPLM
 
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Celine George
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfMr Bounab Samir
 

Recently uploaded (20)

Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptx
 
ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4
 
Gas measurement O2,Co2,& ph) 04/2024.pptx
Gas measurement O2,Co2,& ph) 04/2024.pptxGas measurement O2,Co2,& ph) 04/2024.pptx
Gas measurement O2,Co2,& ph) 04/2024.pptx
 
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxBarangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERP
 
Raw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptxRaw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptx
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginners
 
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptxFINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17
 
YOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptx
YOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptxYOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptx
YOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptx
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
 
Proudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptxProudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptx
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
 
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdfTataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
 
OS-operating systems- ch04 (Threads) ...
OS-operating systems- ch04 (Threads) ...OS-operating systems- ch04 (Threads) ...
OS-operating systems- ch04 (Threads) ...
 
Keynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-designKeynote by Prof. Wurzer at Nordex about IP-design
Keynote by Prof. Wurzer at Nordex about IP-design
 
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
 

Analyzing Social Science Data with SPSS - A Quick Guide

  • 2.
  • 3. 1. We will start by using the SPSS DATA EDITOR To define and enter data
  • 4.
  • 5.
  • 6.
  • 7. 1. SPSS DATA EDITOR To define and enter data
  • 8.  
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. GENDER MATHS-PMR MATHS-FINAL PRETEST SCORE POSTTEST SCORE PROC CONC 2 B A 6.0 14.0 19.0 8.0 2 B C 7.0 14.0 19.0 8.0 2 A A 1.0 14.5 17.0 9.0 2 A C 7.0 14.5 19.0 9.0 2 B C 7.0 13.5 18.0 8.0 2 B C 10.0 12.0 20.0 6.0 2 A A 8.0 15.0 19.0 9.0 2 C B 6.0 9.0 18.0 3.0 2 B B 8.0 10.0 17.0 5.0 2 A A 10.0 15.0 19.0 10.0 1 C D 1.0 17.0 18.0 11.0 1 B C 6.0 16.0 18.0 10.0 1 A C 4.0 12.0 18.0 8.0 1 D D .0 12.0 17.0 7.0 1 D C 8.0 15.0 15.0 8.0 1 C A 4.0 14.0 20.0 8.0 1 B A 8.0 12.0 15.0 7.0 1 B B 7.0 11.0 17.0 5.0 1 C B 8.0 15.0 18.0 10.0 1 A A 6.0 16.0 18.0 11.0 1 D C 6.0 15.0 17.0 9.0 1 C C 6.0 14.0 20.0 8.0 1 A A 15.0 18.0 20.0 12.0 1 A A 7.0 16.0 17.0 10.0 1 A B 9.0 18.0 18.0 12.0 1 A A 16.0 20.0 20.0 14.0 1 A B 7.0 15.0 16.0 9.0 1 A A 20.0 20.0 19.0 14.0 1 B B 9.0 19.5 18.0 14.0 1 A B 12.0 19.0 17.0 13.0 1 A A 16.0 18.0 15.0 12.0 2 B A 6.0 8.0 19.0 2.0 2 A B 7.0 10.0 19.0 4.0 2 A B 9.0 10.0 18.0 4.0 2 B B 9.0 10.0 19.0 4.0 2 B C 4.0 8.0 19.0 2.0 2 C C 4.0 8.0 18.0 2.0 2 B B 7.0 12.0 19.0 6.0
  • 14.
  • 15.  
  • 16.  
  • 17.  
  • 18.  
  • 19.  
  • 20.  
  • 21.  
  • 22.  
  • 23.