Statistical software packages ,their layout & applications

It's about statistical software ,packages ,layout & application

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

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

9. 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

10. 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

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

12. 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

13. 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

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

15. 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

16. 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.

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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/

23. 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

24. 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)

25. 1.Entering data in minitab

26. Viewing descriptive statistics in minitab

27. Creating graphs & chart in minitab

28. Runing regression analysis in minitab

29. 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

30. 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.

31. 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

32. 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

33. 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

34. 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

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

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