General Principles of Intellectual Property: Concepts of Intellectual Proper...
Computer assistance in statistical methods.28.04.2021
1. BASIC WORKSHOP OF RESEARCH METHODOLOGY
MUHS,NASHIK
COMMON STATISTICAL SOFTWARE’S AND ITS
APPLICATIONS
Dr. Anjali Upadhye
M.Sc.(Statistics),M.B.A.,PGDTQM,Ph.D.
Head of Department,Research ,ADAMC,Ashta
(Contact :+91 9922494537 , anjaliupadhye1511@gmail.com)
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2. COMMON STATISTICAL SOFTWARE’S
AND ITS APPLICATIONS
LEARNING OBJECTIVES
– Introduction
– Features of Statistical software
– MS-EXCEL
– SPSS: Statistical Package for the Social Sciences
– EPI INFO
– SAS: Statistical Analysis System
– E-views: Statistical Package for windows
– MINITAB
– STATA
– R & MATLAB
– PSPP
– G POWER TOOLS
– REVMAN software for systematic review
– Write about other Statistical related Software’s
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3. Introduction
■ Statistical analysis is the science of collecting, exploring and presenting large
amounts of data to discover underlying patterns and trends and these are
applied every day in research, industry and government to become more
scientific about decisions that need to be made. Statistical software’s helps in
analysis of data.
■ The software can either read data directly from EXCEL spreadsheet, a user can
enter the data directly to software, or has specialized data entry software to
capture data.
■ The statistical software’s then manipulates the information they possess to
discover patterns which can help the user uncover the predictions and help to
interpret the data.
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5. Initial steps to enter Statistical Software’s in
research project
■ 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.
■ Data checking and cleaning is possible.
– All data should be numeric, although it may not be all variables it is not desirable
to use letter or word (String variable) as data. This can be achieved by recoding
the letter or word (string data) into desirable numeric and labeled appropriately.
– 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 than0.05.
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6. MS-EXCEL
■ This is part of the Microsoft Office suite of programs. Excel version 1.0 was first released in
1985, with the latest version Excel 2016.Microsoft Excel 2010 is one of the most popular
software applications worldwide and is part of the Microsoft Office 2010 productivity suite.
■ You can use Excel to analyze data, for example, in accounts, budgets, billing and many
other areas.
■ Excel allows you to explore the menu bar and the different tasks that can be done with it.
■ You can work on sample spreadsheets doing basic math, adding and deleting columns and
rows, and preparing the worksheet for printing.
■ You can run your data visually to show trends, patterns and comparisons between the data
in a chart, table or other template, and Excel performs most general statistical analyses but
weak in regression, logistic regression, survival analysis, analysis of variance, factor
analysis, multivariate analysis.
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7. SPSS: STATISTICAL PACKAGE FOR THE SOCIAL
SCIENCES
■ SPSS stands for Statistical Package for the Social Sciences. It was one of the earliest
statistical packages with Version 1 being released in 1968, well before the advent of
desktop computers.
■ SPSS assists the user in describing data, testing hypotheses and looking for a correlation
or relationship between one or more variables. SPSS is very suitable for most regression
analysis and different kinds of ANOVA (regression, logistic regression, survival analysis,
analysis of variance, factor analysis, multivariate analysis but not suitable for time series
analysis and multilevel regression analysis)-Wikipedia (2014).
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8. EPI INFO
Epi Info is public domain statistical
software for epidemiology
developed by Centers for Disease
Control and Prevention (CDC) in
Atlanta, Georgia (USA). Epi Info has
been in existence for over 20 years
and is currently available for
Microsoft windows.
http://www.cdc.gov/epiinfo.
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9. SAS: STATISTICAL ANALYSIS SYSTEM
■ SAS - SAS stands for Statistical Analysis System.
■ It was developed at the North Carolina State University in 1966, so is contemporary with
SPSS &which has its latest version produced in December 2011 is a package that many
"power users" like because of its power and programmability.
■ SAS is one of the packages that are difficult to learn.
■ To use SAS, you must write SAS programs that manipulate your data and perform your
data analyses.
■ SAS performs most general statistical analyses (regression, logistic regression, survival
analysis, analysis of variance, factor analysis, multivariate analysis). The greatest
strengths of SAS are probably in its ANOVA, mixed model analysis and multivariate
analysis, while it is probably weakest in ordinal and multinomial logistic
■ Regression (because these commands are especially difficult), and robust methods (it is
difficult to perform robust regression, or other kinds of robust methods)
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10. E-VIEWS:
STATISTICAL
PACKAGE FOR
WINDOWS
E-Views is a statistical package for window,
used mainly for time-series oriented
econometrics analysis.
EViews can be used for general statistical
analysis and econometric analyses, such as
cross-section and panel data analysis and time
series estimation and forecasting.
EViews relies heavily on a proprietary and
undocumented file format for data storage.
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11. MINITAB & MATLAB
■ MINITAB is statistical software used by educators, students, scientists, business
associates and researchers to provide statistical software in a multitude of areas.
MINITAB, developed around 1990, and remains one of the oldest statistical software
programs available. MINITAB has compatibility with PC, Macintosh, Linux and all
other major platforms.
■ MINITAB performs most general statistical analyses (regression, logistic regression,
survival analysis, analysis of variance, factor analysis, but has its weaknesses in
general linear model (GLM) and Multilevel regression).
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12. STATA
■ Stata is a more recent statistical package with Version 1 being released in 1985.
Since then, it has become increasingly popular in the areas of epidemiology and
economics, and probably now rivals SPSS and SAS in it user base.
■ STATA is a powerful statistical package with smart data-management facilities, a
wide array of up-to-date statistical techniques, and an excellent system for producing
publication-quality graphs
■ STATA performs most general statistical analyses (regression, logistic regression,
survival analysis, analysis of variance, factor analysis, multivariate analysis and time
series analysis).
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13. R & MATLAB
R
■ S-plus is a statistical programming language
developed in Seattle in 1988. R is a free version of S-
plus developed in 1996. Since then, the original
team has expanded to include dozens of individuals
from all over the globe. Because it is a programming
language and environment, it is used by giving the
software a series of commands, often saved in text
documents called syntax files or scripts, rather than
having a menu-based system. Because of this, it is
probably best used by people already reasonably
expert at statistical analysis, or who have an affinity
for computers.
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, which are then available to all users
■ Free! At http://cran.csiro.au/
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16. Reliability
Analysis
(Normality
Check)
Variables
Reliability Statistics
Normality
Cronbach's
Alphaa
Cronbach's
Alpha Based
on
Standardized
Items
N of Items
Demographic Characteristics
about Purchase of Green
Products
0.463 0.474 5 Deviated
Consumer's Awareness about
the green products
0.927 0.911 19 Normal
Green products purchased by
the Consumers
0.959 0.962 10 Normal
Reasons for purchase of green
products
0.875 0.804 7 Normal
Influencing key aspects of green
products
0.888 0.895 13 Normal
Influencing factors for purchase
decision of green products
0.483 0.165 7 Deviated
Consumer behavioral
perceptions regarding value of
being ecofriendly
0.977 0.977 13 Normal
Consumer Attitude regarding
Green Products
0.900 0.899 13 Normal
Opportunities available in future -0.082 0.068 5 Deviated
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