Research

1. 1. (a) what are different factors in research ethics?
There are following different factors in research ethics;
No Pressure
Never any pressuring of participants.
Example: Researchers must respect that individuals should make their own informed decisions
about whether to participate in research
Safety
Safety of participants essential.
Example: Attempt to ensure that participants are not harmed by the research
Credit
Every researcher must receive precise, appropriate credit.
Communicate
One should try to make results known to participants.
Ill Usage of Research
One should be conscious of possible bad uses of research.
(b) What is plagiarism? What are the different types of plagiarism? How we
can avoid plagiarism (Techniques)?
Plagiarism
Plagiarism is the act of stealing someone else's work and attempting to "pass it off" as your own.
This can apply to anything, from term papers to photographs to songs, even ideas!
Different Type of Plagiarism
There are four types of plagiarism
 Copying
 Patchwork plagiarism
 Paraphrasing plagiarism
 Unintentional plagiarism
Copying
The most well-known and, sadly, the most common type of plagiarism is the simplest: copying. If
you copy someone else's work and put your name on it, you have plagiarized.
Example
A writer decides that he wants to create an Internet website to generate ad revenue. Instead of
writing his own articles, he visits another website that have article on the topic in which he is
interested. He copy article, changes the titles and the authors' names to his name and posts the
article on his own website.

2. Patchwork plagiarism
The second kind of plagiarism is similar to copying and is perhaps the second most common
type of plagiarism: patchwork plagiarism. This occurs when the plagiarizer borrows the "phrases
and clauses from the original source and weaves them into his own writing" (McConnell Library,
Radford University) without putting the phrases in quotation marks or citing the author.
Example
A writer decides that he wants to create an Internet website to generate ad revenue. Instead of
writing his own articles, he visits twenty other websites that have articles on the topic in which he
is interested. He copies each of the articles, changes the titles and the authors' names to his name
and posts the articles on his own website.
Paraphrasing Plagiarism
The third type of plagiarism is called paraphrasing plagiarism. This occurs when the plagiarizer
paraphrases or summarizes another's work without citing the source. Even changing the words a
little or using synonyms but retaining the author's essential thoughts, sentence structure, and/or
style without citing the source is still considered plagiarism.
Example
Now, had the "writer" of website used footnotes or parenthetical citations to acknowledge
someone other work, he or she would have been in the clear? However, since the "writer" acts like
these ideas are his or her own, and does not acknowledge else work, it's plagiarism.
Unintentional
The fourth type of plagiarism is called unintentional plagiarism -- it occurs when the writer
incorrectly quotes and/or incorrectly cites a source they are using. How is this plagiarism, if the
author didn't mean to do it?
Example
Incorrect usage of another’s work, whether it’s is intentional or not, could be taken for real
plagiarism
How can we avoid Plagiarism?
Avoiding plagiarism is quite simple. The best methods for avoiding are
 Be honest when you've used a source in your paper
 Give credit where it's due.
 Acknowledge the author of the original work you've used.
 Use your own work as often as possible.
 Quote and/or cite your sources properly.

3. Proper Quotations
In order to properly quote your sources, you should consult the style manual that would be
appropriate for the research. In most cases, your professor will tell you which style manual would
be preferred. If your professor doesn't indicate which manual to use, be sure to ask.
The following examples are formatted in MLA, APA, and Chicago (Turabian is similar to
Chicago) formats. The text is taken from the passage we saw earlier from Zimbardo.
MLA Quotations
 Indirect: Some researchers note that "children are totally insensitive to their parents'
shyness" (Zimbardo 62).
 Direct: Zimbardo notes that “children are totally insensitive to their parents’ shyness” (62).
 Paraphrasing: Some researchers have observed that children seem unaware that their
parents are considered bashful (Zimbardo 62).
APA or Chicago Quotations
 Indirect: Some researchers note that "children are totally insensitive to their parents'
shyness" (Zimbardo, 1977, p.62).
 Direct: Zimbardo (1977) notes that “Children are totally insensitive to their parents’
shyness” (p. 62).
 Paraphrasing: Some researchers have observed that children seem oblivious to their
parents’ bashfulness (Zimbardo, 1977).
Proper Citations
In order to properly cite your sources, you should also consult the style manual that would be
appropriate for the research. The following examples are formatted in MLA, APA, and Chicago
(Turabian is similar to Chicago) formats. The citation is related to the passage we saw earlier from
Zimbardo.
MLA Citations
Books
Zimbardo, Philip G. Shyness: What It Is, What To Do About It. Cambridge, Mass.: Perseus Books,
1977 Print.
Essay/Chapter in a Book
Swanson, Gunnar. "Graphic Design Education as a Liberal Art: Design and Knowledge in the
University and the 'Real World.'" The Education of a Graphic Designer. Ed. Steven Heller. New
York: Allworth Press, 1998. 13-24. Print.
Article

4. Bagchi, Alaknanda. "Conflicting Nationalisms: The Voice of the Subaltern in Mahasweta Devi's
Bashai Tudu." Tulsa Studies in Women's Literature 15.1 (1996): 41-50. Print.
Article from a Database
Langhamer, Claire. “Love and Courtship in Mid-Twentieth-Century England.” Historical Journal
50.1 (2007): 173-96. ProQuest. Web. 27 May 2009.
Entire Website
The Purdue OWL Family of Sites. The Writing Lab and OWL at Purdue and Purdue U, 2008.
Web. 6 September 2012.
Page on a Website
"How to Make Vegetarian Chili." eHow.com. eHow, n.d. Web. 24 Feb. 2012.
APA Citations
Book
Zimbardo, P.G. (1977). Shyness: What it is, what to do about it. Cambridge, Mass.: Perseus Books.
Essay/Chapter in a Book
O'Neil, J. M., & Egan, J. (1992). Men's and women's gender role journeys: Metaphor for healing,
transition, and transformation. In B. R. Wainrib (Ed.), Gender issues across the life cycle (pp. 107-
123). New York: Springer.
Article
Scruton, R. (1996). The eclipse of listening. The New Criterion, 15(30), 5-13.
Article from a Database
APA does not require that a citation for an article in a database document that fact. You can cite
an article you find in a database the same way you’d cite a regular print article, as in the example
above.
Website
Lowe, M. (2012). Megan Lowe @ ULM. January 29, 2012, from http://www.ulm./edu/~lowe.
Item Without Author
Merriam-Webster's collegiate dictionary (10th ed.).(1993). Springfield, MA: Merriam-Webster.
Many of these examples came from the OWL at Purdue

5. 15. What is data Analysis? What tools have you used for analysis? Discussion
features of each tool in detail.
Data analysis is a method in which data is collected and organized so that one can derive helpful
information from it. In other words, the main purpose of data analysis is to look at what the data
is trying to tell us. For example, what does the data show or do? What does the data not show or
do?
Tool Used for Data analysis:
1. R Programming
R is the leading analytics tool in the industry and widely used for statistics and data modeling. It
can easily manipulate data and present in different ways.
Features
 R is a well-developed, simple and effective programming language which includes
conditionals, loops, user defined recursive functions and input and output facilities.
 R has an effective data handling and storage facility,
 R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
 R provides a large, coherent and integrated collection of tools for data analysis.
 R provides graphical facilities for data analysis and display either directly at the computer
or printing at the papers.
2. Tableau Public:
Tableau Public is a free software that connects any data source be it corporate Data Warehouse,
Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards etc. with
real-time updates presenting on web.
Features
 Patented technology from Stanford University.
 Toggle view and drag-and-drop.
 List of native data connectors.
 Highlight and filter data.
 Share dashboards.
 Embed dashboards within.
 Mobile-ready dashboards.
 Data notifications.
 Tableau Reader for data viewing.
 Dashboard commenting.
 Create “no-code” data queries.
 Translate queries to visualizations.
 Import all ranges and sizes of data.

6.  Create interactive dashboards.
 String insights into a guided story.
 Metadata management.
 Automatic updates.
 Security permissions at any level.
 Tableau Public for data sharing.
 Server REST API.
3. Python
Python is an object-oriented scripting language which is easy to read, write, maintain and is a free
open source tool. It was developed by Guido van Rossum in late 1980’s which supports both
functional and structured programming methods.
Features
 Easy to Learn and Use
 Expressive Language
 Interpreted Language
 Cross-platform Language
 Free and Open Source
 Object-Oriented Language
 Extensible
 Large Standard Library
 GUI Programming Support
 Integrated
4. SAS:
SAS is a programming environment and language for data manipulation and a leader in analytics,
developed by the SAS Institute in 1966 and further developed in 1980’s and 1990’s.
Features:
Features
 SAS syntax is easy-to-learn.
 Strong Data Analysis Abilities
 Flexible 4 Generation Programming Language (4GL)
 It contain all the necessary packages required for analyzing and reporting data
 Reduced coding for common application with its inbuilt libraries.
 It is an interactive language
 Easily accessible from any device
 Instructive in nature

7. 5. Apache Spark
The University of California, Berkeley’s AMP Lab, developed Apache in 2009.
Features:
 Apache Spark is a fast large-scale data processing engine and executes applications in
Hadoop clusters 100 times faster in memory and 10 times faster on disk.
 Spark is built on data science and its concept makes data science effortless.
 Spark is also popular for data pipelines and machine learning models development.
 Spark also includes a library – MLlib that provides a progressive set of machine algorithms
for repetitive data science techniques like Classification, Regression, Collaborative
Filtering, Clustering, etc.
6. Excel
Excel is a basic, popular and widely used analytical tool almost in all industries. Excel becomes
important when there is a requirement of analytics on the client’s internal data.
Features:
 It analyzes the complex task that summarizes the data with a preview of pivot tables
that helps in filtering the data as per client requirement.
 Excel has the advance business analytics option which helps in modelling capabilities
which have prebuilt options like automatic relationship detection, a creation of DAX
measures and time grouping.
7. RapidMiner:
It is a powerful integrated data science platform developed by the same company that performs
predictive analysis and other advanced analytics like data mining, text analytics, machine
learning and visual analytics without any programming.
Features:
 Graphical user interface
 Analysis processes design
 Multiple data management methods
 Data from file, database, web, and cloud services
 In-memory, in-database and in-Hadoop analytics
 Application templates
 -D graphs, scatter matrices, self-organizing map
 GUI or batch processing
 Integrates with in-house databases
 Interactive, sharable dashboard
8. KNIME
KNIME Developed in January 2004 by a team of software engineers at University of Konstanz.
Features:
 KNIME is leading open source,

8.  Reporting, and integrated analytics tools that allow to analyze and model the data through
visual programming
 It integrates various components for data mining and machine learning via its modular
data-pipelining concept.
9. QlikView
Features:
 QlikView has many unique features like patented technology and has in-memory data
processing, which executes the result very fast to the end users and stores the data in the
report itself.
 Data association in QlikView is automatically maintained and can be compressed to almost
10% from its original size.
 Data relationship is visualized using colors – a specific color is given to related data and
another color for non-related data.
10. Splunk:
Splunk is a tool that analyzes and search the machine-generated data.
Features:
 Splunk pulls all text-based log data and provides a simple way to search through it,
 A user can pull in all kind of data,
 Perform all sort of interesting statistical analysis on it,
 Present it in different formats.
16. Why do you prefer MS-Excel for analysis? Discussion in details?
Analyzing and storing data
One of the best uses of MS Excel is that we can analyze larger amounts of data to discover trends.
With the help of graphs and charts, we can summarize the data and store it in an organized way so
that whenever we want to see that data then we can easily see it. It becomes easier for us to store
data and it will definitely save a lot of time for us. Once the data is stored in a systematic way, it
can be used easily for multiple purposes. MS Excel makes it easier to implement various operations
on the data through various tools that it possesses.
Mathematical formulas of MS Excel make things easier
MS Excel is that it makes easy solve complex mathematical problems in a much simpler way
without much manual effort. There are so many formulas in MS Excel and by using these formulas
we can implement lots of operations like finding sum, average, etc. on a large amount of data all
at once. Therefore, people use MS Excel whenever they have to solve complex mathematical
problems or they need to apply simple mathematical functions on tables containing larger data.

9. Data recovery and spreadsheets
Another best use of MS Excel is that if our data gets lost then we can recover it without much
inconvenience. Suppose, there is a businessman who has stored his important data in MS Excel
and somehow it gets lost or the file gets damaged then he must not worry as with the new MS
Excel XML format one can restore the lost or damaged file data. The next important use is that
there are spreadsheets in MS Excel which also makes our work easy and with the help of new
Microsoft MS Excel XML format we can reduce the size of the spreadsheet and make things
compact easily.
Security
MS Excel is that it provides security for excel files so people can keep their files safe. All the files
of MS Excel can be kept password-protected through visual basic programming or directly within
the excel file. People store their important data in the MS Excel so that they can keep their data in
an organized way and save their time as well. Almost every person wants his files to be password
protected so that no one is able to see them or ruin them so here MS Excel solves this problem
very efficiently.
17. What is data visualization? What are different types and uses of Graph?
Data Visualization
Data visualization is the process of displaying data or information in graphical charts, figures and
bars.
Examples of temporal data visualization include:
 Scatter plots
 Polar area diagrams
 Time series sequences
 Timelines
 Line graphs
Types and Uses of Graph
There are several different types of charts and graphs. The four most common are probably line
graphs, bar graphs and histograms, pie charts, and Cartesian graphs. They are generally used for,
and best for, quite different things.
Bar graphs:
To show numbers that are independent of each other. Example data might include things like the
number of people who preferred each of Chinese takeaways, Indian takeaways and fish and
chips.
Pie charts:

10. To show you how a whole is divided into different parts. You might, for example, want to show
how a budget had been spent on different items in a particular year.
Line graphs:
Show you how numbers have changed over time. They are used when you have data that are
connected, and to show trends, for example, average night time temperature in each month of the
year.
Cartesian graphs:
Have numbers on both axes, which therefore allow you to show how changes in one thing affect
another. These are widely used in mathematics, and particularly in Algebra.