SlideShare ist ein Scribd-Unternehmen logo
1 von 50
SPSS?



SPSS - Statistical Package for the
         Social Sciences
What is SPSS?

   SPSS is a comprehensive and flexible
    statistical analysis and data management
    solution.
   SPSS is a computer program used for survey
    authoring and deployment, data mining, text
    analytics, statistical analysis, and collaboration
    and deployment
Cont..
   SPSS can take data from almost any type of
    file and use them to generate tabulated
    reports, charts, and plots of distributions and
    trends, descriptive statistics, and conduct
    complex statistical analyses.
   SPSS is among the most widely used
    programs for statistical analysis in social
    science.
About
   Its is developed by Norman H. Nie and C.
    Hadlai Hull of IBM Corporation in the year
    1968. It is compatible with Windows, Linux,
    UNIX & Mac operating systems. SPSS is
    among the most widely used programs
    for statistical analysis in social science.
Used in…
   Telecommunications,
   Banking,
   Finance,
   Insurance,
   Healthcare,
   Manufacturing,
   Retail,
   Consumer packaged goods,
   Higher education,
   Government,
   and Market research.
Features of SPSS

   It is easy to learn and use.
   It includes a full range of data. management
    system and editing tools.
   It provides in-depth statistical capabilities.
   It offers complete plotting, reporting and
    presentation features.
Getting data into SPSS
   Creating new SPSS data files
   Opening existing SPSS system files
   Importing data from an ASCII file
   Importing data from other file formats
Entering Data
DATA EDITOR
   The data editor offers a simple and efficient
    spreadsheet like facility for entering data and
    browsing the working data file.
   This window displays the content of the data
    file.
   One can create new data files or modify
    existing ones.
   One can have only one data file open at a
    time.
Cont..
   This editor provides two views of the data,
DATA VIEW
       Displays the actual data values or defined
    value labels.
VARIABLE VIEW
       Displays variable definition information,
    including defined variable and value labels,
    data type, etc..,
Editing Data
PIVOT TABLE EDITOR


     Output can be modified in many ways with
  is editor, and can create multidimensional
  tables.
Ex:
            We can edit text, swap data in rows
  and columns
Cont..
TEXT OUTPUT EDITOR
      Text output not displayed in pivot tables
    can be modified with the text output editor.

CHART EDITOR
     High-resolution charts and plots can be
    modified in chart windows.
Saving Data
   We need to save it and give it a name. The
    default extension name for saving files is ‘.sav’
    .
   Ex. SSPS.sav
   Also we can able to retrieving already saved
    file
Variables

   Variable is a user defined name of Particular
    type of data to hold information (such as
    income or gender or temperature or dosage).
    Array of variable is a collection values of
    similar data types.
Variables types
 1.   Numeric
 2.   Comma
 3.   Dot
 4.   Scientific notation
 5.   Date
 6.   Custom currency
 7.   String
Rules for Variable Names
    Names must begin with a letter.
    Names must not end with a period.
    Names must be no longer than eight
    characters.
    Names cannot contain blanks or special
    characters.
    Names must be unique.
    Names are not case sensitive. It doesn’t matter
    if you call your variable CLIENT, client, or
    CliENt. It’s all client to SPSS.
BASIC STEPS IN DATA
ANALYSIS
   Get Your Data Into SPSS:
        We can open a previously saved SPSS
    data file, read a spreadsheet, database ,or
    text data file, or enter directly in the data
    editor.
   Select a Procedure:
        Select a procedure from the menus to
    calculate statistics or to create a chart.
Cont..
   Select The Variable For The Analysis:
        Variables in the data file are displayed in a
    dialog box for the procedure.

   Run The Procedure:
       Results are displayed in the viewer.
STATISTICAL PROCEDURES
 After entering the data set in data editor or
  reading an ASCII data file, we are now ready
  to analyze it.
The Procedures Available are
 Reports

 Descriptive Statistics

 Custom Tables

 Compare means

 General Linear model (GLM)
   Correlate
   Regression
   Loglinear
   Classify
   Data Reduction
   Scale
   Non parametric tests
   Time Series
   Survival
   Multiple response.
REPORTS
    Report is a textual work made with the
 specific intention of relaying information or
 recounting certain events in a
 widely presentable form
DESCRIPTIVE STATISTICS
   This provides techniques for summarizing
 Data with statistics, charts, and reports.
Cont..
CUSTOM TABLES
       It provides attractive, flexible, displays of
    frequency counts, percentages and other
    statistics.
COMPARE MEANS
        This provides techniques for testing
    differences among two or more means on their
    values for other variable.
Cont..
GENERAL LINEAR MODEL(GLM)
         This provides technique for testing
    univariate and multivariate analysis-of-
    variance models including repeated
    measures.
CORRELATE
   This provides measures of association for two
    or more Variable measured at the interval
    level.
Cont..
REGRESSION
   This provides a variety of regression
    techniques , including Linear, logistic,
    nonlinear, weighted, and two-stage least-
    squares regression.
LOGLINEAR
   This provides general and hierarchical log-
    linear analsis and logit analysis.
Cont..
CLASSIFY
   This provides cluster and discriminant analysis
DATA REDUCTION
   This provides factor analysis, correspondence
    analysis, and optional scaling.
Cont..
SCALE
   This provides reliability analysis and
    multidimensional scaling.
NON PARAMETRIC TESTS
   This provides non-parametric tests for one
    sample, or for two and paired or Independent
    sample.
Cont..
TIME SERIES
       Provides exponential smoothing, autocorrelated
  regression, ARIMA, X11 ARIMA, seasonal decomposition,
  spectral analysis, and related techniques.

SURVIVAL
       This provides techniques for analyzing the time for some
  terminal event to occur, including Kaplan-Meier analysis and
  Cox regression.

MULTIPLE RESPONSE:
     This provides facilities to define and analyze multiple-
 response .
GRAPHS
BAR
       Generate a simple , clustered , or stacked
    bar chart of the data.
LINE
       Generate a simple or multiple line chart of
    the data.
AREA
       Generate a simple or stacked area chart of
    the data.
Cont..
PIE
      Generates a simple pie chart or a
    composite bar chart from the data.
BOXPLOT
   Generates box plot showing the median,
    outline, and extreme cases of individual
    variables.
Cont..
PARETO
 Generates Pareto charts, bar charts with a line
 superimposed showing the cumulative sum.

CONTROL
 Produces the most commonly-used process-
 control charts.
Cont..
NORMAL P-P PLOTS
      The cumulative proportions of a variable's
 distribution against the cumulative proportions of the
 normal distribution.

NORMAL Q-Q PLOTS
     The quantiles of a variable's distribution against
 the quantiles of the normal distribution.

SEQUENCE
      Produces a plot of one or more variables by order
 in the file, suitable for examining time-series data.
TIME SERIES: AUTOCORRELATIONS
     Calculates and plots the autocorrelation
 function (ACF) and partial autocorrelation
 function of one or more series to any specified
 number of lags, displaying the Box-Ljung
 statistic at each lag to test the overall
 hypothesis that the ACF is zero at all lags.
TIME SERIES: CROSS-CORRELATIONS
    Calculates and plots the cross-correlation
 function of two or more series for positive,
 negative, and zero lags.
TIME SERIES: SPECTRAL
     Calculates and plots univariate or bivariate
 periodograms and spectral density functions,
 which express variation in a time series as the
 sum of a series of sinusoidal components. It
 can optionally save various components of the
 frequency analysis as new series.
Advantages
    SPSS offers a user friendliness that most
    packages are only now catching up to. It is
    popular, and though that is certainly not a
    reason for choosing a statistical package,
    many data sets are easily loaded into it and
    other programs can easily import SPSS files.
Disadvantages
   For academic use SPSS lags notably behind
    SAS, R and even perhaps others that are on the
    more mathematical rather than statistical side for
    modern data analysis.
   Its menu offerings are typically the most basic of
    an analysis and sometimes lacking even then,
    and it makes doing an inappropriate analysis very
    easy.
   It is expensive, sometimes ridiculously so, and
    even when you do buy you're really only leasing,
    and its license is definitely not user friendly.
   There are often compatibility issues with prior
Thank You. . .

Weitere ähnliche Inhalte

Was ist angesagt?

Application of excel and spss programme in statistical
Application of excel and spss programme in statisticalApplication of excel and spss programme in statistical
Application of excel and spss programme in statisticalVeenaV29
 
Type i and type ii errors
Type i and type ii errorsType i and type ii errors
Type i and type ii errorsp24ssp
 
Analysis of variance (anova)
Analysis of variance (anova)Analysis of variance (anova)
Analysis of variance (anova)Sadhana Singh
 
Presentation on spss
Presentation on spssPresentation on spss
Presentation on spssalfiyajamalcj
 
Various statistical software's in data analysis.
Various statistical software's in data analysis.Various statistical software's in data analysis.
Various statistical software's in data analysis.SelvaMani69
 
Software packages for statistical analysis - SPSS
Software packages for statistical analysis - SPSSSoftware packages for statistical analysis - SPSS
Software packages for statistical analysis - SPSSANAND BALAJI
 
Testing of hypothesis
Testing of hypothesisTesting of hypothesis
Testing of hypothesisJags Jagdish
 
Regression
Regression Regression
Regression Ali Raza
 
Measures of Central Tendency - Biostatstics
Measures of Central Tendency - BiostatsticsMeasures of Central Tendency - Biostatstics
Measures of Central Tendency - BiostatsticsHarshit Jadav
 
Karl pearson's correlation
Karl pearson's correlationKarl pearson's correlation
Karl pearson's correlationfairoos1
 

Was ist angesagt? (20)

Application of excel and spss programme in statistical
Application of excel and spss programme in statisticalApplication of excel and spss programme in statistical
Application of excel and spss programme in statistical
 
Type i and type ii errors
Type i and type ii errorsType i and type ii errors
Type i and type ii errors
 
Analysis of variance (anova)
Analysis of variance (anova)Analysis of variance (anova)
Analysis of variance (anova)
 
Presentation on spss
Presentation on spssPresentation on spss
Presentation on spss
 
Regression Analysis
Regression AnalysisRegression Analysis
Regression Analysis
 
Various statistical software's in data analysis.
Various statistical software's in data analysis.Various statistical software's in data analysis.
Various statistical software's in data analysis.
 
Data analysis using spss
Data analysis using spssData analysis using spss
Data analysis using spss
 
Correlation analysis
Correlation analysisCorrelation analysis
Correlation analysis
 
Statistical software
Statistical softwareStatistical software
Statistical software
 
Software packages for statistical analysis - SPSS
Software packages for statistical analysis - SPSSSoftware packages for statistical analysis - SPSS
Software packages for statistical analysis - SPSS
 
Correlation Analysis
Correlation AnalysisCorrelation Analysis
Correlation Analysis
 
Uses of SPSS and Excel to analyze data
Uses of SPSS and Excel   to analyze dataUses of SPSS and Excel   to analyze data
Uses of SPSS and Excel to analyze data
 
Testing of hypothesis
Testing of hypothesisTesting of hypothesis
Testing of hypothesis
 
Regression
Regression Regression
Regression
 
Introduction to spss
Introduction to spssIntroduction to spss
Introduction to spss
 
Chi square test
Chi square testChi square test
Chi square test
 
Non parametric tests
Non parametric testsNon parametric tests
Non parametric tests
 
Measures of Central Tendency - Biostatstics
Measures of Central Tendency - BiostatsticsMeasures of Central Tendency - Biostatstics
Measures of Central Tendency - Biostatstics
 
Analysis of variance
Analysis of varianceAnalysis of variance
Analysis of variance
 
Karl pearson's correlation
Karl pearson's correlationKarl pearson's correlation
Karl pearson's correlation
 

Andere mochten auch

Andere mochten auch (10)

Introduction To SPSS
Introduction To SPSSIntroduction To SPSS
Introduction To SPSS
 
Basic guide to SPSS
Basic guide to SPSSBasic guide to SPSS
Basic guide to SPSS
 
Presenting statistics in social media
Presenting statistics in social mediaPresenting statistics in social media
Presenting statistics in social media
 
Statistical software packages
Statistical software packagesStatistical software packages
Statistical software packages
 
Research Methodology (MBA II SEM) - Introduction to SPSS
Research Methodology (MBA II SEM) - Introduction to SPSSResearch Methodology (MBA II SEM) - Introduction to SPSS
Research Methodology (MBA II SEM) - Introduction to SPSS
 
Introduction to Research Methodology
Introduction to Research MethodologyIntroduction to Research Methodology
Introduction to Research Methodology
 
Software for Qualitative and Quantitative Data Analysis
Software for Qualitative and Quantitative Data AnalysisSoftware for Qualitative and Quantitative Data Analysis
Software for Qualitative and Quantitative Data Analysis
 
1.introduction to research methodology
1.introduction to research methodology1.introduction to research methodology
1.introduction to research methodology
 
Acknowledgement
AcknowledgementAcknowledgement
Acknowledgement
 
Marketing research ppt
Marketing research pptMarketing research ppt
Marketing research ppt
 

Ähnlich wie Spss

Spss by vijay ambast
Spss by vijay ambastSpss by vijay ambast
Spss by vijay ambastVijay Ambast
 
Data processing & Analysis: SPSS an overview
Data processing & Analysis: SPSS an overviewData processing & Analysis: SPSS an overview
Data processing & Analysis: SPSS an overviewATHUL RAVI
 
Topic 4 intro spss_stata
Topic 4 intro spss_stataTopic 4 intro spss_stata
Topic 4 intro spss_stataSM Lalon
 
Enhancing statistical report capabilities using the clin plus report engine
Enhancing statistical report capabilities using the clin plus report engineEnhancing statistical report capabilities using the clin plus report engine
Enhancing statistical report capabilities using the clin plus report engineClin Plus
 
spss-anintroduction-150704135929-lva1-app6892.ppt
spss-anintroduction-150704135929-lva1-app6892.pptspss-anintroduction-150704135929-lva1-app6892.ppt
spss-anintroduction-150704135929-lva1-app6892.pptFlorArquillano3
 
SPSS introduction Presentation
SPSS introduction Presentation SPSS introduction Presentation
SPSS introduction Presentation befikra
 
Ibm spss statistics 19 brief guide
Ibm spss statistics 19 brief guideIbm spss statistics 19 brief guide
Ibm spss statistics 19 brief guideMarketing Utopia
 
Evaluation Spss
Evaluation SpssEvaluation Spss
Evaluation Spssjackng
 
Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...
Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...
Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...Martin Magu
 
Introduction to SAS
Introduction to SASIntroduction to SAS
Introduction to SASImam Jaffer
 
Accounting serx
Accounting serxAccounting serx
Accounting serxzeer1234
 
Accounting serx
Accounting serxAccounting serx
Accounting serxzeer1234
 
IBM SPSS Custom Tables create custom tabls inn no time.pdf
IBM  SPSS Custom Tables create custom tabls inn no time.pdfIBM  SPSS Custom Tables create custom tabls inn no time.pdf
IBM SPSS Custom Tables create custom tabls inn no time.pdfahmedmaths03
 
A brief introduction to 'R' statistical package
A brief introduction to 'R' statistical packageA brief introduction to 'R' statistical package
A brief introduction to 'R' statistical packageShanmukha S. Potti
 
introduction to spss
introduction to spssintroduction to spss
introduction to spssOmid Minooee
 
Topic 4 intro spss_stata 30032012 sy_srini
Topic 4 intro spss_stata 30032012 sy_sriniTopic 4 intro spss_stata 30032012 sy_srini
Topic 4 intro spss_stata 30032012 sy_sriniSM Lalon
 

Ähnlich wie Spss (20)

Spss by vijay ambast
Spss by vijay ambastSpss by vijay ambast
Spss by vijay ambast
 
5116427.ppt
5116427.ppt5116427.ppt
5116427.ppt
 
Data processing & Analysis: SPSS an overview
Data processing & Analysis: SPSS an overviewData processing & Analysis: SPSS an overview
Data processing & Analysis: SPSS an overview
 
6967176.ppt
6967176.ppt6967176.ppt
6967176.ppt
 
Topic 4 intro spss_stata
Topic 4 intro spss_stataTopic 4 intro spss_stata
Topic 4 intro spss_stata
 
Enhancing statistical report capabilities using the clin plus report engine
Enhancing statistical report capabilities using the clin plus report engineEnhancing statistical report capabilities using the clin plus report engine
Enhancing statistical report capabilities using the clin plus report engine
 
spss-anintroduction-150704135929-lva1-app6892.ppt
spss-anintroduction-150704135929-lva1-app6892.pptspss-anintroduction-150704135929-lva1-app6892.ppt
spss-anintroduction-150704135929-lva1-app6892.ppt
 
SPSS introduction Presentation
SPSS introduction Presentation SPSS introduction Presentation
SPSS introduction Presentation
 
Ibm spss statistics 19 brief guide
Ibm spss statistics 19 brief guideIbm spss statistics 19 brief guide
Ibm spss statistics 19 brief guide
 
SPSS
SPSSSPSS
SPSS
 
Evaluation Spss
Evaluation SpssEvaluation Spss
Evaluation Spss
 
Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...
Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...
Documents.pub sigmaplot 13-smit-principal-components-analysis-principal-compo...
 
Introduction to SAS
Introduction to SASIntroduction to SAS
Introduction to SAS
 
Accounting serx
Accounting serxAccounting serx
Accounting serx
 
Accounting serx
Accounting serxAccounting serx
Accounting serx
 
IBM SPSS Custom Tables create custom tabls inn no time.pdf
IBM  SPSS Custom Tables create custom tabls inn no time.pdfIBM  SPSS Custom Tables create custom tabls inn no time.pdf
IBM SPSS Custom Tables create custom tabls inn no time.pdf
 
A brief introduction to 'R' statistical package
A brief introduction to 'R' statistical packageA brief introduction to 'R' statistical package
A brief introduction to 'R' statistical package
 
introduction to spss
introduction to spssintroduction to spss
introduction to spss
 
Spss basics tutorial
Spss basics tutorialSpss basics tutorial
Spss basics tutorial
 
Topic 4 intro spss_stata 30032012 sy_srini
Topic 4 intro spss_stata 30032012 sy_sriniTopic 4 intro spss_stata 30032012 sy_srini
Topic 4 intro spss_stata 30032012 sy_srini
 

Mehr von Tech_MX

Virtual base class
Virtual base classVirtual base class
Virtual base classTech_MX
 
Theory of estimation
Theory of estimationTheory of estimation
Theory of estimationTech_MX
 
Templates in C++
Templates in C++Templates in C++
Templates in C++Tech_MX
 
String & its application
String & its applicationString & its application
String & its applicationTech_MX
 
Statistical quality__control_2
Statistical  quality__control_2Statistical  quality__control_2
Statistical quality__control_2Tech_MX
 
Stack data structure
Stack data structureStack data structure
Stack data structureTech_MX
 
Stack Data Structure & It's Application
Stack Data Structure & It's Application Stack Data Structure & It's Application
Stack Data Structure & It's Application Tech_MX
 
Spanning trees & applications
Spanning trees & applicationsSpanning trees & applications
Spanning trees & applicationsTech_MX
 
Set data structure 2
Set data structure 2Set data structure 2
Set data structure 2Tech_MX
 
Set data structure
Set data structure Set data structure
Set data structure Tech_MX
 
Real time Operating System
Real time Operating SystemReal time Operating System
Real time Operating SystemTech_MX
 
Mouse interrupts (Assembly Language & C)
Mouse interrupts (Assembly Language & C)Mouse interrupts (Assembly Language & C)
Mouse interrupts (Assembly Language & C)Tech_MX
 
Motherboard of a pc
Motherboard of a pcMotherboard of a pc
Motherboard of a pcTech_MX
 
More on Lex
More on LexMore on Lex
More on LexTech_MX
 
MultiMedia dbms
MultiMedia dbmsMultiMedia dbms
MultiMedia dbmsTech_MX
 
Merging files (Data Structure)
Merging files (Data Structure)Merging files (Data Structure)
Merging files (Data Structure)Tech_MX
 
Memory dbms
Memory dbmsMemory dbms
Memory dbmsTech_MX
 

Mehr von Tech_MX (20)

Virtual base class
Virtual base classVirtual base class
Virtual base class
 
Uid
UidUid
Uid
 
Theory of estimation
Theory of estimationTheory of estimation
Theory of estimation
 
Templates in C++
Templates in C++Templates in C++
Templates in C++
 
String & its application
String & its applicationString & its application
String & its application
 
Statistical quality__control_2
Statistical  quality__control_2Statistical  quality__control_2
Statistical quality__control_2
 
Stack data structure
Stack data structureStack data structure
Stack data structure
 
Stack Data Structure & It's Application
Stack Data Structure & It's Application Stack Data Structure & It's Application
Stack Data Structure & It's Application
 
Spanning trees & applications
Spanning trees & applicationsSpanning trees & applications
Spanning trees & applications
 
Set data structure 2
Set data structure 2Set data structure 2
Set data structure 2
 
Set data structure
Set data structure Set data structure
Set data structure
 
Real time Operating System
Real time Operating SystemReal time Operating System
Real time Operating System
 
Parsing
ParsingParsing
Parsing
 
Mouse interrupts (Assembly Language & C)
Mouse interrupts (Assembly Language & C)Mouse interrupts (Assembly Language & C)
Mouse interrupts (Assembly Language & C)
 
Motherboard of a pc
Motherboard of a pcMotherboard of a pc
Motherboard of a pc
 
More on Lex
More on LexMore on Lex
More on Lex
 
MultiMedia dbms
MultiMedia dbmsMultiMedia dbms
MultiMedia dbms
 
Merging files (Data Structure)
Merging files (Data Structure)Merging files (Data Structure)
Merging files (Data Structure)
 
Memory dbms
Memory dbmsMemory dbms
Memory dbms
 
Linkers
LinkersLinkers
Linkers
 

Kürzlich hochgeladen

A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 

Kürzlich hochgeladen (20)

A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 

Spss

  • 1.
  • 2. SPSS? SPSS - Statistical Package for the Social Sciences
  • 3. What is SPSS?  SPSS is a comprehensive and flexible statistical analysis and data management solution.  SPSS is a computer program used for survey authoring and deployment, data mining, text analytics, statistical analysis, and collaboration and deployment
  • 4. Cont..  SPSS can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and conduct complex statistical analyses.  SPSS is among the most widely used programs for statistical analysis in social science.
  • 5. About  Its is developed by Norman H. Nie and C. Hadlai Hull of IBM Corporation in the year 1968. It is compatible with Windows, Linux, UNIX & Mac operating systems. SPSS is among the most widely used programs for statistical analysis in social science.
  • 6. Used in…  Telecommunications,  Banking,  Finance,  Insurance,  Healthcare,  Manufacturing,  Retail,  Consumer packaged goods,  Higher education,  Government,  and Market research.
  • 7. Features of SPSS  It is easy to learn and use.  It includes a full range of data. management system and editing tools.  It provides in-depth statistical capabilities.  It offers complete plotting, reporting and presentation features.
  • 8. Getting data into SPSS  Creating new SPSS data files  Opening existing SPSS system files  Importing data from an ASCII file  Importing data from other file formats
  • 9. Entering Data DATA EDITOR  The data editor offers a simple and efficient spreadsheet like facility for entering data and browsing the working data file.  This window displays the content of the data file.  One can create new data files or modify existing ones.  One can have only one data file open at a time.
  • 10. Cont..  This editor provides two views of the data, DATA VIEW  Displays the actual data values or defined value labels. VARIABLE VIEW  Displays variable definition information, including defined variable and value labels, data type, etc..,
  • 11. Editing Data PIVOT TABLE EDITOR  Output can be modified in many ways with is editor, and can create multidimensional tables. Ex:  We can edit text, swap data in rows and columns
  • 12. Cont.. TEXT OUTPUT EDITOR  Text output not displayed in pivot tables can be modified with the text output editor. CHART EDITOR  High-resolution charts and plots can be modified in chart windows.
  • 13. Saving Data  We need to save it and give it a name. The default extension name for saving files is ‘.sav’ .  Ex. SSPS.sav  Also we can able to retrieving already saved file
  • 14. Variables  Variable is a user defined name of Particular type of data to hold information (such as income or gender or temperature or dosage). Array of variable is a collection values of similar data types.
  • 15. Variables types 1. Numeric 2. Comma 3. Dot 4. Scientific notation 5. Date 6. Custom currency 7. String
  • 16. Rules for Variable Names  Names must begin with a letter.  Names must not end with a period.  Names must be no longer than eight characters.  Names cannot contain blanks or special characters.  Names must be unique.  Names are not case sensitive. It doesn’t matter if you call your variable CLIENT, client, or CliENt. It’s all client to SPSS.
  • 17. BASIC STEPS IN DATA ANALYSIS  Get Your Data Into SPSS: We can open a previously saved SPSS data file, read a spreadsheet, database ,or text data file, or enter directly in the data editor.  Select a Procedure: Select a procedure from the menus to calculate statistics or to create a chart.
  • 18. Cont..  Select The Variable For The Analysis: Variables in the data file are displayed in a dialog box for the procedure.  Run The Procedure: Results are displayed in the viewer.
  • 19. STATISTICAL PROCEDURES  After entering the data set in data editor or reading an ASCII data file, we are now ready to analyze it. The Procedures Available are  Reports  Descriptive Statistics  Custom Tables  Compare means  General Linear model (GLM)
  • 20. Correlate  Regression  Loglinear  Classify  Data Reduction  Scale  Non parametric tests  Time Series  Survival  Multiple response.
  • 21. REPORTS Report is a textual work made with the specific intention of relaying information or recounting certain events in a widely presentable form DESCRIPTIVE STATISTICS This provides techniques for summarizing Data with statistics, charts, and reports.
  • 22. Cont.. CUSTOM TABLES  It provides attractive, flexible, displays of frequency counts, percentages and other statistics. COMPARE MEANS  This provides techniques for testing differences among two or more means on their values for other variable.
  • 23. Cont.. GENERAL LINEAR MODEL(GLM)  This provides technique for testing univariate and multivariate analysis-of- variance models including repeated measures. CORRELATE  This provides measures of association for two or more Variable measured at the interval level.
  • 24. Cont.. REGRESSION  This provides a variety of regression techniques , including Linear, logistic, nonlinear, weighted, and two-stage least- squares regression. LOGLINEAR  This provides general and hierarchical log- linear analsis and logit analysis.
  • 25. Cont.. CLASSIFY  This provides cluster and discriminant analysis DATA REDUCTION  This provides factor analysis, correspondence analysis, and optional scaling.
  • 26. Cont.. SCALE  This provides reliability analysis and multidimensional scaling. NON PARAMETRIC TESTS  This provides non-parametric tests for one sample, or for two and paired or Independent sample.
  • 27. Cont.. TIME SERIES Provides exponential smoothing, autocorrelated regression, ARIMA, X11 ARIMA, seasonal decomposition, spectral analysis, and related techniques. SURVIVAL This provides techniques for analyzing the time for some terminal event to occur, including Kaplan-Meier analysis and Cox regression. MULTIPLE RESPONSE: This provides facilities to define and analyze multiple- response .
  • 28. GRAPHS BAR  Generate a simple , clustered , or stacked bar chart of the data. LINE  Generate a simple or multiple line chart of the data. AREA  Generate a simple or stacked area chart of the data.
  • 29.
  • 30.
  • 31.
  • 32. Cont.. PIE  Generates a simple pie chart or a composite bar chart from the data. BOXPLOT  Generates box plot showing the median, outline, and extreme cases of individual variables.
  • 33.
  • 34.
  • 35. Cont.. PARETO Generates Pareto charts, bar charts with a line superimposed showing the cumulative sum. CONTROL Produces the most commonly-used process- control charts.
  • 36.
  • 37.
  • 38. Cont.. NORMAL P-P PLOTS The cumulative proportions of a variable's distribution against the cumulative proportions of the normal distribution. NORMAL Q-Q PLOTS The quantiles of a variable's distribution against the quantiles of the normal distribution. SEQUENCE Produces a plot of one or more variables by order in the file, suitable for examining time-series data.
  • 39.
  • 40.
  • 41.
  • 42. TIME SERIES: AUTOCORRELATIONS Calculates and plots the autocorrelation function (ACF) and partial autocorrelation function of one or more series to any specified number of lags, displaying the Box-Ljung statistic at each lag to test the overall hypothesis that the ACF is zero at all lags.
  • 43.
  • 44. TIME SERIES: CROSS-CORRELATIONS Calculates and plots the cross-correlation function of two or more series for positive, negative, and zero lags.
  • 45.
  • 46. TIME SERIES: SPECTRAL Calculates and plots univariate or bivariate periodograms and spectral density functions, which express variation in a time series as the sum of a series of sinusoidal components. It can optionally save various components of the frequency analysis as new series.
  • 47.
  • 48. Advantages  SPSS offers a user friendliness that most packages are only now catching up to. It is popular, and though that is certainly not a reason for choosing a statistical package, many data sets are easily loaded into it and other programs can easily import SPSS files.
  • 49. Disadvantages  For academic use SPSS lags notably behind SAS, R and even perhaps others that are on the more mathematical rather than statistical side for modern data analysis.  Its menu offerings are typically the most basic of an analysis and sometimes lacking even then, and it makes doing an inappropriate analysis very easy.  It is expensive, sometimes ridiculously so, and even when you do buy you're really only leasing, and its license is definitely not user friendly.  There are often compatibility issues with prior