This document provides an overview of statistical software packages including their common features, advantages, types, and most common packages used in social science. It discusses features of Microsoft Excel, SPSS, SAS, Stata, R, Minitab, and GraphPad Prism. These software packages vary in their ease of use, available statistical analyses, programming capabilities, and cost. Excel is best for basic statistics while SAS and Stata provide more advanced analyses but are harder to use. SPSS and Minitab offer a good balance of capabilities and usability.

1. STATISTICAL SOFTWARES
PACKAGES , LAYOUT &
APPLICATIONS
DR.MUMTAZ ALI NAREJO
PhD STUDENT S.A.L.U KHAIRPUR MIRS

2. OUTLINES
• Introduction
• Common features
• Advantages
• Types
• Most common packages
• Layout
• Applications

3. STATISTICAL SOFTWARE
• Specialized programs=>complex statistical analysis
• organization =>collection ,interpretation , analysis , calculations ,
presentation of data
• vital tool for research analysis, data validation and findings
• Statistical solutions => statistical analysis
• capabilities =>support & analysis methodologies
regression analysis
predictive analysis
statistical modelling

4. • data scientists and mathematicians
• industry-specific features
• avoid routine mathematical mistakes and produce accurate figures
• features tailored to scientific research, cost modelling, or health
• Qualities:
Package statistical analysis capabilities, equations, and models
Facilitate data importing, preparation and modelling
Perform complex statistical analysis
Compare Statistical Analysis Software
 improve in the quality of research

5. COMMON FEATURES OF STATISTICAL SOFTWARES
• common characteristics that make reliable & suitable for data analysis
• Data editor is in rows & columns : very easy to enter numeric data
• availability of menu bar comprises drop-down menu, quick analysis
as well as brief user manual
• Statistical level of measurement is put into consideration in data entry
• Getting your data ready to enter into the software
• Defining and labeling variable
• Entering data appropriately with each row containing each case and
each column as variable

6. • Data checking and cleaning is possible
• All data should be numeric
• Data exploration can be done to check for errors and other accuracy
• The statistical level of significance for rejecting null hypothesis (Ho) is
when your p-value significance is less than 0.05
• Time & cost effective

7. ADVANTAGES OF STATISTICAL SOFTWARE
• Accuracy & speed
• Varsality
• Validity
• Graphics
• Flexibilty
• New variables
• Volume of data
• Easy transfer of data
• Easy compilation , tabulation ,Diagramatics prrsentaion
• averages , co-efficients of variation ,standard deviation error & percentiles

9. TYPES OF STATISTICAL SOFTWARE PACKAGES
• Open source
• Public domain
• Freeware
• Proprietary

10. OPEN SOURCE STATISTICAL SOFTWARE PACKAGES
• ADMB : Non-linear statistical modelling on C++
• DAP : Free replecement for SAS
• FITYK : Non-linear regression
• OPENEPI : Web-based , open source , independent for epidiomiology
& STATISTICS
• SCIPY: Regression , plotting , anova
• PSPP : Free & alternative to IBM SPSS
• R : A free implementation of S

11. PUBLIC DOMAIN STATISTICAL SOFTWARE
PACKAGES
• CSPRO :
Developed : US census beureau & ICF international
Used : entering , editing , tabulating , mapping , disseminting census & surveying data
• EPI-INFO :
Epidemiology
Developed : centre for disease controll & prevention in Atlanta & georgia (USA)
used : electronic survey creation , data entry , Analysis (t-test & Anova)
• X-12 -ARIMA :
 Developed : US census beureau
Use : seasonal variations

12. FREEWARE STATISTICAL SOFTWARE PACKAGES
• WINBUGS :
Baysian analysis
 use markov chain monte carlos methods
• WINPEPI :
Epidemilogy

13. PROPRIETARY STATISTICAL SOFTWARE PACKAGES
• GRAPHPAD INSTAT : very simple , lots guidance & explanation
• GRAPHPAD PRISM : biostatistic , non-linear regression & explanations
• IBM SPSS STATISTICS: comprehensive statistical package
• IBM SPSS MODELER: Comprehemsive data mining & text anaylsis
• MATLAB : programming language with statistical features
• SAS : comprehensive statistical package
• SPSS : social science
• STATS DIRECT : biomedical , public health & general health science

14. MICROSOFT ADDON STATISTICAL SOFTWARE
PACKAGES
• ANALYSE IT : analysis
• NUM XL : general statistics & economics
• REGRESS IT : multivariate data analysis & linear regression(freeware)
• SIGMA XL : statistical & graphical analysis
• SPC XL : general statistics
• STATS HELPER : descriptive statistics & six sigma

15. MOST COMMON STATISTICAL SOFTWARE
PACKAGES IN SOCIAL SCIENCE
• MS-EXCEL
• SPSS
• GRAPHPAD INSTAT
• GRAPHPAD PRISM
• STATISTIX

16. MICROSOFT EXCEL
• Part of the Microsoft Office suite of programs
• Excel version 1.0 was first released in 1985
• latest version Excel 2016
• most popular software application worldwide
• Good points:
Extremely easy to use and interchanges nicely with other Microsoft products
Excel to analyze data, for example, in accounts, budgets, billing and many other areas
Excel spreadsheets can be read by many other statistical packages
Add on module which is part of Excel for undertaking basic statistical analyses
Can produce very nice graphs

17. • Bad points :
Good in only general statistics but poor in regression analysis ,logistic
regression ,survival , variance , Factor & multivariate analysis
Excel is designed for financial calculations, although it is possible to
use it for many other things
Cannot undertake more sophisticated statistical analyses without
purchase of expensive commercial add ons.
• Availability
Microsoft software already installed
For blue-plated (UniSA) computers, contact the IT Help Desk to install
the latest Microsoft office software
For your own computer, you can always purchase Microsoft Office
from a retail store.

21. SPSS
• Statistical Package for the Social Sciences
• Version 1 being released in 1968, well before the advent of desktop computers
• It is now on Version 23
• Data editor ,output viewer , syntax editor , script window
• Good points :
Very easy to learn and use
Can use either with menus or syntax files
Quite good graphics
Excels at descriptive statistics,testing hypothesis , co-relation, basic regression analysis,
analysis of variance, and some newer techniques such as Classification and Regression Trees
(CART)
Has its own structural equation modelling software AMOS, that dovetails with SPSS

22. • Bad points :
Focus is on statistical methods mainly used in the social sciences,
market research and psychology
Has advanced regression modelling procedures such as LMM and
GEE, but they are awful to use with very obscure syntax
Has few of the more powerful techniques required in epidemiological
analysis, such as competing risk analysis or standardised rates
 Availability :
SPSS is available on blue-plated (UniSA) computers
contact the IT Help Desk to install it

26. SAS
• Statistical Analysis System
• North Carolina State University in 1966
• contemporary with SPSS
• Good points :
Can use either with menus or syntax files
Much more powerful than SPSS
 "power users" like because of its power and programmability
Commonly used for data management in clinical trials
• Bad points :
Harder & longer time to learn and use than SPSS
 number of records is generally limited to the size of your hard disk.
• Availability :
To organise installation contact the IT Help Desk

28. STATA
• more recent statistical package with Version 1 being released in 1985
• popular in the areas of epidemiology and economics
• We are now on Version 14
• available for Windows, Unix, and Mac computers
• Good points :
Can use either with menus or syntax files
Much more powerful than SPSS – probably equivalent to SAS
Excels at advanced regression modelling
Has its own in-built structural equation modelling
Has a good suite of epidemiological procedures
Researchers around the world write their own procedures in Stata

29. • powerful statistical package with smart data-management facilities
• an excellent system for producing publication-quality graphs
• a wide array of up-to-date statistical techniques
• Bad points :
Harder to learn and use than SPSS
most general statistical analyses (regression, logistic regression, survival analysis, analysis
of variance, factor analysis, multivariate analysis and time series analysis
Does not yet have some specialised techniques such as CART or Partial Least squares
regression
• Availability :
Stata can be downloaded onto blue-plated computers by contacting the IT Help Desk
Students can purchase a full copy with a perpetual license from the Australian
distributors (Survey Design and Analysis) for about $200

31. R
• S-plus is a statistical programming language developed in Seattle in 1988
• R is a free version of S-plus developed in 1996
• it is a programming language and environment
• richest statistical systems contain impressive amount of libraries, growing each day
• Good points
Very powerful – easily matches or even surpasses many of the models found in SAS
or Statas
Researchers around the world write their own procedures in R
• Bad points
Much harder to learn and use than SAS or Stata
general statistical analysis
• Availability
http://cran.csiro.au/

32. MINITAB
• used by educators, students, scientists, business associates and researchers in a
multitude of areas
• developed around 1990
• one of the oldest statistical software programs available
• has compatibility with PC, Macintosh, Linux
• GOOD POINTS:
 easiest statistical software programs to use
 popular choice with those new statistical software.
With drop-down menus and dialog boxes describing how and what to do next
persists as a popular choice for teaching students about statistics and data analysis
 primarily has a user base of educators using the program to show students research
methods and analysis

33. • BAD POINTS :
performs most general statistical analyses (regression, logistic regression,
survival analysis, analysis of variance, factor analysis
has its weaknesses in general linear model (GLM) and Multilevel regression)

34. 1.Entering data in minitab

35. Viewing descriptive statistics in minitab

37. Creating graphs & chart in minitab

38. Runing regression analysis in minitab

41. GRAPHPAD PRISM
• written by Harvey Motulsky in 1989
• 2D graphics , curve fitting & statistical software for windows
• Good Points :
Non-linear regression & removal of outliers
 comparisons of models & curves, interpolation of standard curves
 automatic updating of results and graphs
functionality for displaying error bars
Built in formulae, batch processing and standardisation features,
along with automated analysis and data validation makes GraphPad
Prism a popular software amongst users

44. STATISTIX
• Statistix is a powerful statistical analysis program you can use to quickly analyze your
data
• Easy to Learn and Use
• Completely menu-driven, procedures are specified using concise Windows-style
dialog boxes.
• Reliable
• Developed in 1985
• Comprehensive
• Statistix performs all the basic and advanced statistics needed by most users.
• "Statistix gives the user easy access to all the common tools of data analysis
• Fast Computes lightening fast. No time consuming disk access needed
• Data are memory resident.
• "Statistix is fast, very fast.

49. APPLICATIONS
• quantitative research cannot be done effectively without SS
• It helps professionals to interact with data thereby paving way for creativity
and innovation
• user friendly interface with drop-down tips
• allowed experts greater freedom to come out with results within twinkle of
eye than ever before where it takes time to finish analysis
• It has been discovered that some analysis such as post Hoc, complex
analysis in time series, regression and variance analysis cannot be
calculated manually effectively without statistical software packages
• statistical software has contributed immensely to social research especially
in the area of demographic and data analysis

50. • Statistical software packages have been discovered to help academic
staffs in higher institution to improve their research expertise by attending
training on usage of statistical packages.
• Statistical packages make research work robust and faster.
• It was discovered that 81% efficiency of staff in statistical software is
determine by the years of experience in usage and the area of
specialization.
• Most reason for using statistical software is its easy usage, suitability for
many statistical analysis
• While reason for non usage range from lack of attention to learn, difficult
usage, cost of licensing
• statistical software are not expensive neither are they too difficult to use
but people do not give attention to its learning

51. • To provide magnitude of any health problem in community
• To findout basic factors underlying ill-health
• To calculate sample size from large population
• To calculate survival rates of varius diseases
• To determine association between two variables
• To study prevalence & incidence of disease
• To findout odd ratio ,relative risk ,attributable risk in case controll &
cohort
• To find out normal distribution of disease
• To test usefullness of both sera & vaccines
• Role of causative factors in disease
• To introdue & promote health legislation

52. • To evaluate the activity of drug
• To explore changes produced by drug are whether due to action of
drug or by chance
• To compare the actions of two or more different drugs
• To find out association between disease & risk factor like coronary
artery disease & smoking
• Population genetics inorder to findout variation in genotype &
phenotype
• Genomics & Proteomics
• Demography

53. • Education
• Government
• Marketing organizations
• NGO’s
• Telecommunication
• Banking
• Insurance
• Healthcare
• Manufacturing
• Social science
• Health scinece
• Pharmacy
• Economics

54. THANKYOU