This document discusses the application of SPSS software for statistical analysis. SPSS can be used for tasks like descriptive statistics, regression modeling, ANOVA, and data manipulation. It is suitable for small to large enterprises and has advantages like online processing, database connectivity, and importing/exporting data to other programs. SPSS is highlighted as a robust option for statistical applications in research areas like quality control, clinical trials, and data mining in drug discovery.

1. APPLICATION OF SPSS
Mr. Umakant Bhaskar Gohatre
Smt. Indira Gandhi college of Engineering,Navi Mumbai

2. INTRODUCTION
 SPSS is a software package used for conducting statistical analysis,
manipulating data, and generating table and graphs that summarize
data.
 SPSS performs statistical analysis range from basic descriptive
statistics, such as average and prevalence, to advanced inferential
statistical, such as regression model, analysis of variance (ANOVA),
factor analysis etc.
 SPSS also contains several tools for manipulating data, including
functions for recording data, macros programming on visual basic editor,
merging data, and aggregating complex data sets.

3. FEATURES AND BENEFITS
 For Small And Medium Enterprises (SME)
 Resources & best practices.
 Techniques for cleaning data.
 Access Data in Relational Databases.
 For Large Scale Enterprises (LSE)
 Real time processing and online mapping.
 Online Database connectivity different networks.
 Data import & export data from different medium like SAS, Statistica.
 Compliances and Validations.

4. What are the options?
 Many commercial products available
 SAS, Statistica, Minitab, and others
 Excel
 Widely available (part of MS Office Suite)
 Not a statistical software – spreadsheet
 Finance, math, and statistics applications
 SPSS
 Robust software for sophisticated statistical applications
 Upgrade with add-ins

5. WHY SPSS?
 Strengths
 Very robust statistical software
 Many complex statistical tests available
 Good “stats coach” help with interpreting results
 Easily and quickly displays data tables
 Can be expanded
 Using the syntax feature
 Purchasing add-ins
 Limitations
 Can be expensive
 Not intuitive to use
 Typically requires additional training to maximize features (at a cost)
 Graphing feature not as simple as Excel

6. RESEARCH & DEVELOPMENT
SPSS given the technique of specialize in design, development, and
implementation of corporate analytical and data mining
solution integrated with the existing data acquisition and storage
of systems. Its continuously given the solution in Pharmaceutical and
Medical research.
 Quality and Manufacturing.
 Compliance and Validation..
 Empower Researcher with Tools.
 Regulatory Safety Testing.
 Clinical Trails.
 Data Mining in Drug Discovery.

7. QUALITY AND MANUFACTURING
SPSS can provide a single, standardized quality control and
improvement solution that can be easily validated that cover the whole
range of manufacturing statistics, including simple descriptive
statistics and graphics, QC charting, capability analyses for normal
and Non-normal distributions, design of experiment (DOE) much
more.
 Online and offline SPC (Statistical Process Control)
 Process Capability Analysis.
 Measurement System Analysis.
 Design of Experiment
 Process Validation.

8. COMPLIANCE AND VALIDATION
SPSS offers a pre-validation core platform with fully secure user
administration and configuration control, audit trails on system
configuration changes, data manipulation, analysis results and
integrated data modules.
Together these features can radically change the way of your
organization works and remove the need for extensive manual
documentation of analytical processes.
 Validation of Software.
 Analysis of Audit Trails.
 Computer Systems Validation

9. EMPOWER RESEARCHER WITH TOOLS
Even small improvement in research and development process directly
affect an organization’s bottom line and many pharmaceuticals
research organizations have experienced the positive impact of
deploying SPSS.
Offering a choice of desktop (stand-alone), networks, and server based
analytical platforms.
Statistician has provided analytical tools that are easy to use, relevant
and integrated with their data sources.
 Comprehensive Statistical Test & Graphical Tools.
 Efficient use of limited Statistician & Training Resources.
 Integrated with Existing System & Data Sources.
 Rapid return on investment.

10. REGULATORY SAFETY TESTING
From simple bioassay and dose-response experiments to long-term
survival and carcinogenicity studies, SPSS has the tools to handle
your data. Where the standard study designed are used.
SPSS tools provides the most efficient mechanism for automating and
validating routine analysis report.
 Power and Sample Size Calculation.
 Predictive Data Mining.
 Forecasting and Report Generation.
 Analysis of Complex Drug’s Assay.

11. CLINICAL TRIALS
Ethical considerations and at least in Phase I and Phase II Studies small
sample sizes make it imperative that the most power statistical
methods be used in ordered to established efficacy and detect side
effects.
SPSS is unique in providing a fully document management system
along with complete analytical toolkit. With ability to directly query
standard database platforms and to import a wide range of data file
format (including SAS files).
 Generalized Linear and Non-Linear Model
 Variance Component Estimations (ANOVA).

12. DATA MINING IN DRUG DISCOVERY
With the advent of new technologies that allow very large amounts of
data to be captured and stored automatically many new challenges
are presented.
In application of classical statistical methods many were developed
specially to extract the maximum amount of information from minimal
data sets.
According to the FDA Critical Path Initiative the use of data mining
technology and methodology is central to improvement discovery, pre-
clinical and clinical research. SPSS perform a excellent crunch the
complex data sets.
 Handle Huge Data Sets with Ease.
 Exploratory Data Mining Techniques.
 Comprehensive Data Mining Toolkit.

13. SUMMARY
 Industry standard process
 Open system
 Easy to use graphic interface
 Flexibility
 Productivity