Competitive Environment And Maintaining Customer...

Competitive Environment And Maintaining Customer...
1.0 Introduction
In today's competitive environment, maintaining customer relationships is a key to business success
because customers are considered as important strategic resources of a business. However, all
business markets contain many subgroups of customers that behave differently, that have different
ambitions, and have different purchasing behaviours. Hence, each subgroup must be treated
differently in order to build strong customer relationships. To compete with rival businesses, it is
vital for a business to know enough about their customers and to know the wants and needs of the
customers.
Britain 's largest employee–owned retailer, the John Lewis Partnership owns Waitrose, a chain of
British supermarkets, which comprise the ... Show more content on Helpwriting.net ...
This is twice as the penetration in the US.
Due to these new challenges, Waitrose is interested in analysing their customer data to gain an
overall understanding about their online customers, to group them into categories and to identify the
attributes of the high value online customers for their business. This helps them to be able to better
optimize marketing programs, satisfy customers and to increase profits. The main objective of this
analysis is to use the data mining techniques to analyse the customer purchasing data and to discover
patterns, solutions and to find implicit but potentially useful information to answer business
questions of Waitrose. In this analysis, these data mining techniques will be used to define the "High
value customer profile" for Waitrose.
1.1 About Waitrose
Waitrose is a chain of British supermarkets, which creates the food retail division of the John Lewis
Partnership, which is the biggest employee–owned retailer in Britain. Its head office is situated in
Bracknell, Berkshire, England.
Waitrose has established an influential position in the UK's retail food industry and it is extensively
acknowledged as the prominent supermarket wine merchant and as a pioneer in the area of food
traceability and organic products. Newspapers as like The Telegraph and The Guardian has been
describing Waitrose, as having an
... Get more on HelpWriting.net ...

Visualization In Data Mining Essay
1 ABSTARCT
Visualization in data mining is a new methodology for exploring and analyzing a huge data sets,
data visualization techniques and joining traditional data mining strategies. It is used for large
amounts of data sets and information. Visualization of model–fitting, data and results play a very
important role, but vast data sets are distinctive and new techniques of a data display needed for
managing and dealing with large data sets. With the help of this paper to learn the importance of
visualization techniques, approaches and methods used in data mining . The strategy used to achieve
this objective is writing an literature review of many books, journal articles and conference
proceeding which are written by experts.
Contents
2. Introduction 3
3. Literature Review 4
4. Conclusion 5
5. Reference 5
2 INTRODUCTION
Data mining is the procedure of identifying new hidden, insights and unexpected patterns in large
data. Analyzing and exploring the huge volume of data sets and information becomes increasingly
very difficult. Visualization of data mining helps to deal with a flood of data and information. As a
volume of a large set of data stored and collected in databases develops, there is a developing need
to provide a data summarization through visualization. The benefit of visual information
investigation is that the client directly involves in the process of

Facing The Database Conundrum : Sql, Hbase, Hive, And The...
FACING THE DATABASE CONUNDRUM SQL, HBASE, HIVE, OR SPARK: WITH THE
SATURATION OF THE ENVIRONMENT WHICH DO YOU CHOOSE SAMANTHA MOHR
UNIVERSITY OF MARYLAND UNIVERSITY COLLEGE SPRING 2015 ABSTRACT There is
currently a conundrum facing experts in the field of Big Data. The struggle is the ability to perform
large–scale data analysis and the impracticality of using relational database processing languages to
handle the information that is collected/processed. Specifically, the growth of data, the sheer volume
that must be stored in databases, processed by cloud analytic and queried by applications has led to a
growth in the data capacity the needs to be handled. Unfortunately, this exponential growth has
exceeded the hardware and ... Show more content on Helpwriting.net ...
Are dynamic columns something you require support for, then you should choose Cassandra. Do
you do batch analytic modeling on your data, Hadoop may be the choice for you. For live streaming
analytic modeling abilities, Apache Spark is a much better choice. So you want to work with your
data as if it were SQL, then you should try Hive. This paper will provide you with a detailed
knowledge of how by choosing the correct database processing and query language you are able to
mitigate the processing capacity problems that are involved with the vast growth of data recently.
This will help to show that while there may be no one size fits all answer, there is a fit for the
problem at hand based on the storage, processing, and query needs that are to be met.
INTRODUCTION BACKGROUND As a result of the appearance of big data in our world,
conventional data warehousing and data analysis methods no longer have the process power needed.
What is Big Data you may ask and why is it such a big deal. NIST defines big data as anywhere "
[...] data volume, acquisition velocity, or data representation limits the ability to perform effective
analysis using traditional relational approaches [...]" (Mell & Cooper, n.d.). 1 (Gong, 2012, p.15)
Today's analyst is inundated by an ever growing number of data being created by social media,
mobile phones, climate sensors, digital pictures, etc. The volume being generated is staggering (2.7
Zettabytes of data in the digital universe).While

Personal Distress Emergency Room Nurses Experience Based...
Working in a high volume, fast paced and stressful work environment can lead to high stress on the
nurses. Directors from the Emergency Nurses Association, a professor of nursing research, incarnate
word, a director and assistant professor of nursing conducted a qualitative study titled ?It?s a burden
you carry?: Describing Moral Distress in Emergency Nursing. The purpose of this study was to
survey moral distress emergency room nurses experience based off of how they describe their
response. Themes from data included adaptive/maladaptive coping, being overwhelmed, and
dysfunctional practice arena (Wolf et al., 2016).
Research Design
Qualitative research design is an evolving process that continues to expand as the research
continues. Multiple types of study designs can be used when researching dependent on the
information the researchers are trying to obtain. In the article, the researchers described the design
as exploratory using semi–structured focus groups for data collection. Recordings were made of the
interviews and then analyzed for similar themes. This type of design is called descriptive
phenomenology which participants describe lived experiences through detailed interview or
conversational process and then researchers bracket the opinions in their study (Polit & Beck, 2014).
The method of design was appropriate for the research question and objectives as it allowed for the
understanding of feelings and identity. The themes of questioning allowed for

Essay On Analytics
Organizations are dealing with a tsunami of data from businesses, humans, and now machines
through the Internet of Things. Generating timely insights and predictions has become crucial to
capitalize on fast–moving business opportunities. With business analytics now focused on the future,
predictive and prescriptive analytics are taking a front seat. As a Lead Assistant Manager with EXL
Services Decision Analytics, I have gained essential skills in model development and monitoring
while enabling the business to predict and mitigate losses. I aspire to extend my knowledge on
multiple technical and business domains through an intense MS program. Information has been my
strength throughout my student life. Grasping data from different ... Show more content on
Helpwriting.net ...
Eagerness to learn more and the ability to grasp concepts quickly earned me a position in Risk
Analytics team with a premier UK– based multinational bank. With an objective to address
Economic Capital Charge as per IAS39 accounting standards, I designed a loss forecasting model
based on historical recovery trend. To incorporate bulk recoveries from debt sale, I evolved an
overlay methodology addressing multiple types of debt sale strategies. Having generated a
favourable financial impact , I received Mastermind award as a part of Rewards and Recognitions at
EXL. In addition, I worked on model monitoring projects focused on improving the accuracy and
robustness of the risk models. Within one and a half year at EXL, Management reco gnized me as a
star performer with excellent analytical, programming and communication skills and promoted me
to the post of Assistant Manager. Post promotion, I led the rebuild of Probability of Default
Impairment Forecasting Model under the IAS39 accounting standards for one of the high materiality
credit card portfolios. Exploratory analysis of portfolio default trends revealed that the existing
models were underpredicting the impairment loss. We revised the model methodology and generated
an impact of over 100 million USD on impairment provision of the banks. Here, I leveraged my
leadership skill to train team members, technical skills to design efficient SAS code automating
model development process across products, and

Results and Conclusion of the Research Process
Results and Conclusion of the Research Process HCS/465
Results and Conclusion of the Research Process The title of this research study is Stressful life
events and the tripartite model: Relations to anxiety and depression in adolescent females. The study
was investigated by Jeremy Fox, Leslie Halpern, Julie Ryan, and Kelly Lowe (2011). This paper will
expand on previous information reported, which included the background and methodology of the
research study. This paper will include the results: including data collection methods and data
analysis procedures, and the conclusion: including the study findings, weaknesses, strengths,
limitations, and whether the findings support the hypotheses.
Results: ... Show more content on Helpwriting.net ...
Data Analysis Procedure: Ways the data analysis procedures are appropriate for testing the
hypotheses The researchers proposed three hypotheses: 1) High stressful life events and high NA
should be related to symptoms of both anxiety and depression. Low PA, however, should be
uniquely related to symptoms of depression. 2) NA should moderate the relationship between
stressful life events and symptoms of both anxiety and depression. 3) PA should moderate the
relationship between stressful life events and symptoms. (Fox et al., 2010, p. 46) After translating
the collected data into facts and studying the association between the variables, the hypotheses were
tested. It was found that the data analysis procedures to be appropriate to help answer the
hypotheses. However, it was found there was mixed evidence for the first hypotheses, which was
concerning tripartite models structure. The data supported the other two hypotheses (Fox et al.,
2010).
Data: Key distinctions between qualitative and quantitative data Qualitative data deals with
descriptions, which it can be observed not measured. Qualitative data analysis is more for
exploratory purposes and usually is done through interviews (Henninger, 2009). Quantitative data
deals with numbers, data that can be measured. Qualitative data analysis allows researchers to test
their hypotheses and is done through surveys (Henninger, 2009). This was a quantitative research
study. However,

The World Testifies Of Data And Our Understanding Of It Essay
1.1 Introduction
(Ian, Frank & Hall 2011) The world testifies to the increasing gap between the development of data
and our understanding of it. Data mining is defined as the analysis of big observational data sets to
establish unsuspected relationships that summarize the data in a novel, understandable and useful
way to the data owner (David, Heikki & Padhraic 2001). These relationships and summaries are
referred to as models or patterns. Patterns comprise sets of co–occurring attribute values referred to
as item sets or more complex patterns that include sequences, which consider explicit precedence
relationships that are either positional or temporal, and graphs considering arbitrary relationships
between points (Mohammed and Wagner 2014). Other types of additional summarised relationships
include rules, clusters, tree structures among others.
Lying hidden in all this data is information– potentially useful information–that is rarely made
explicit or taken advantage of. Data mining activity for pattern deployment is achieved
automatically or semi automatically (Ian, Frank & Hall 2011). The realised patterns lead to some
economic advantage. Patterns can also be expressed or as a transparent box constructed to reveal the
structure of the pattern with the assumption that the realised patterns make good predictions.
Data mining is used in variety of fields and applications (Galit, Stiumueli, Natin & Peter 2010). This
includes the military for purposes of intelligence,

Managing And Analyzing Big Data
Managing and analyzing big data is a huge task for all organizations of all sizes and across all
industries. If a business's plan to implement a data management tools there is a need for a more
realistic way of capturing information about their customers, products, and services. Mining data is
often in the terabytes and organizations need to be able to quickly analyze that data and then pull
appropriate information needed to make managerial decisions. Further, with the insurgence of social
media, smart devices and click–stream, data is generated daily on global networks through
interactions. The use of data management technologies allow a company to interface unstructured
data and structured data to gleam information that is usable for business managers to make sound
business decisions, improve sales and to decrease operating costs. Big data integration and analysis
has evolved for organizations to store, manage, and manipulate vast amounts of data then provide
the appropriate information when it's needed to meet business objectives. Big data is an element that
allows companies to leverage high volume data effectively and not in isolation. Big data needs to be
quickly accessible and have the ability to be analyzed. Data stores or warehouses are one way data is
managed that is persistent, protected and available as long as the data is needed. The forefather to
data stores is relational data bases, relational data bases put in place decades ago are still in use
today

Exploratory Data Analysis Sample
Exploratory data analysis is a process that allows investigation of each variable to be examined for
the population or events of interest (Sylvia & Terhaar, 2014). Descriptive variables are utilized to
describe the characteristics of a population or collection of events (Sylvia & Terhaar, 2014). The
descriptive variables utilized in the case study regarding older community–dwelling patients with
diabetes are age, gender, race, morbidity level, and chronic disease conditions. The descriptive data
provides a detailed image of the population of participating in the study. The average age of patients
participating in the study was 74.7 years, with the age of 73 being the most frequent. Of the three
hundred ninety–two patients participating in the study, a little over half of the study participates
were white men. Ninety–seven percent of the participates reported a "high risk" or "very high risk"
morbidity level. Last, the descriptive variable assessing chronic health conditions reflected that
100% of participates have diabetes. Greater than 20 % of the patients reported hypertension, lipid
disorder, degenerative joint disease, ischemic heart ... Show more content on Helpwriting.net ...
An additional method to determine an outlier is to consider the distribution of the outlier to the
median as opposed to the mean. Based on this principle, a value is considered an outlier if
"positioned 1.5 times the value of the 75th percentile in a distribution" (Sylvia & Terhaar, 2014). In
the case study, the boxplot figure displays values above and below the 75th percentile or the
whiskers (Sylvia & Terhaar, 2014). However, the principal investigator of the study determined
these values should be included as they provide meaning to the final results. In some situations
when the outliers cause a skew in the distribution, other solutions or techniques should be pursued
for analysis(Sylvia & Terhaar,

A Research Study On Data Collection Method
Data Collection Method According to (Swanson, 2005), qualitative research demands a lot from the
researcher, it would also be important for the researcher to acknowledge the words studied in the
analysis are influenced by ourselves. This would be the case since the researcher is using surveys to
conduct the research. The independent variable in the case of this research would be the affordable
housing programs because the researcher wants to determine if there is enough housing and are
there changes. The dependent variable would be the behavioral changes that would take affect if
people were able to successfully gain housing and be able sustain themselves. The results would be
better health, maintaining employment and children doing better in schools.
The researcher has developed the following procedures and methods that will look to gain a more
insightful look at understanding and the importance of the research topic. The goal of the method is
to gather information that will provide more detailed information and the importance in addition to
bringing more awareness of building, rehabilitating and maintaining affordable housing.
Participants
The participants that would be included for the study will be those that live in various communities
that have affordable housing whether it is a home or apartment complex, individuals and families
that have previously lived in some type of affordable housing.
Materials
To conduct the research for this topic the researcher will look at

Apache Spark Resilient Distributed Datasets
Table of Contents
Abstract 1
1 Introduction 1
2 Spark Core 2
 2.1 Transformations 2
 2.2 Actions 2
3 Spark SQL 3
4 Spark Streaming 4
5 GraphX 4
6 MLlib Machine Learning library 4
7 How to Interact with Spark 5
8 Shared Variables 5
8.1 Broadcast Variables: 5
8.2 Accumulators: 5
9 Sample Word Count Application 6
10 Summary 8
References 8
Abstract
Cluster computing frameworks like MapReduce has been widely successful in solving numerous
Big data problems. However, they tend to use one well none map and reduce pattern to solve these
problems. There are many other class of problems that cannot fit into this closed box which may be
handed using other set of programming model. This is where Apache Spark comes in to help solve
these ... Show more content on Helpwriting.net ...
Spark 's RDDs function as a working set for distributed programs that offers a (deliberately)
restricted form of distributed shared memory. [3]
The availability of RDDs facilitates the implementation of both iterative algorithms that visit their
dataset multiple times in a loop, and exploratory data analysis, which is the querying of data
repeatedly. The latency of applications builds with spark compared to Hadoop, a MapReduce
platform may be reduced by several orders of magnitude. [3]
Another key aspect of apache spark is that it makes writing code easy and quickly. This is as a result
of over 80 high level operators included in with spark library. This is evidence as spark comes with a
REPL an interactive shell. The REPL can be used t0 test the outcome of each line of code without
coding the entire job. As a result, ad–hoc data analysis is possible and code is made much shorter.
Apache Spark is well complemented with set of high–level libraries that can be easily used in the

same application. These include Spark SQL, Spark Streaming, MLlib and GraphX which we will
explain in details in this paper. 2 Spark Core
Spark core is at the base of the apache spark foundation. It provides distributed task dispatching,
scheduling, and basic I/O capabilities which is exposed through a common API (JAVA, Scala,
Python and R) centered on the RDD concept. [1] As the core it provides the following:
 Memory management and fault recovery.
 Scheduling,

Security Issues Of Hadoop Services
SECURITY ISSUES IN HADOOP SERVICES 11
Security Issues in Hadoop Services
Jogendra Chowdari Achanta
Adv Web App Using Web Services – CS 525
Professor Kihyun Kim
04/10/2016
Running head: SECURITY ISSUES IN HADOOP SERVICES 1
Abstract
Big Data is creating great opportunities for businesses, companies and many large scale and small
scale industries. Hadoop is an open–source cloud computing and big data framework, is increasingly
used in the IT world. The rapid growth of Hadoop and Cloud Computing clearly indicates its
importance as a Big Data enabling technology. Due to the loopholes of security mechanism, the
security issues introduced through adaptation of this technology are also increasing. Hadoop
services do not ... Show more content on Helpwriting.net ...
In this current era, many organizations are using the technology to store and analyze petabytes of
data that are related to their company, business and their customers for future requirement. Because
of this classification of data becomes even more important. Techniques such as encryption, logging,
and security measures are required for securing this big data. Usage of the Big data for fraud
detection looks very interesting and profit making for many organizations. Big data style analyzing
of data can solve the problems like advanced threats, cyber security related issues and even
malicious intruders. With the use of more sophisticated pattern analysis and with the use of multiple
data sources it is easy to detect the threats in early stages of the project itself. Many organizations
are fighting with the remaining issues like private issues with the usage of big data. Data privacy is a
liability; thus companies must be on privacy defensive. When compared to security, Privacy should
consider as profit making asset because it results in the selling of unique product to customers which
results in making money. We need to maintain balance between data privacy and national security.
Visualization, controlling and inspection of the network links and ports are required to ensure
security. Thus there is a need to put ones in insight the loop

Questions On Financial Review Boards
Instrumentation Salkind (2010) defined instrumentation as "the tools or means by which
investigators attempt to measure variables or items of interest in the data–collection process" (para.
1). In this study will use an interview questions as guidance through the focus group. The
instrumentation will include open–end questions to give the participants freedom to explain the
issue or the reason without leading them to the answer. The interview questions will seek to answer
this study research question.
Confidentiality of Participants and Data (IRB requirements) The participants' personal information
will be totally anonyms to protect their identity.
Institutional review boards (IRB) is the board that is responsible to seek permission for the studies
that involve human subjects (Creswell, 2013). This study will seek IRB approval before begins the
data collection to make sure there is no harm to the participants.
Data Collection Procedures The planned data collection method is a focus group, which emerged in
the 1940s by Merton and Fiske (Cohen & Crabtree, 2006). This study will use focus group as a
primary data collection (Millward, 2012). Cohen and Crabtree (2006) defined focus groups as "data
collection method, [where] data are collected through a semi–structured group interview process."
According to Greenbaum (1993), the focus group has been used since the late1960s. A focus group
is "a qualitative research (...) you do not obtain results with percentages, statistical

An Analysis on the Effectiveness of Malaysia Current...
1.0 Introduction
1.1 Background
Macmillan English Dictionary defines recycling as "the process of treating waste materials such as
newspaper and bottles so that they can be used again". Beside paper and bottles there have lots of
recyclable items such as aluminum cans, scrap metals, plastic and printer cartridges. Recycling
saves energy and reduces pollution and would help slow down global climate change.
The history of recycling is started nearly 4000 years ago, there was a recovery and reuse system of
bronze scrap in operation in Europe and there is evidence that composting was carried out in China.
In 1898, The Association of Cleansing Superintendents is established, which today has evolved into
the Institute of Wastes Management. ... Show more content on Helpwriting.net ...
2003, p.306). Solid domestic waste is one of the major environmental problems faced by most
municipalities in the country particularly in urban areas. The generation rate of waste is 1.0kg/day
per person and 80 percent of the 230 landfills in the country have only two years of lifespan left.
Despite rigorous campaigns of recycling, the attainment of recycling rate has increased from 1–2
percent in 1997 to merely 5 percent in 2001 (Aini et al. 2002, cited in Said et al. 2003, p.306).
Studies have shown that the most significant factor affecting nature is not the official government
policy but public awareness (Chukwuma 1998, cited in Said et al. 2003, p.306) or public concern for
environment and the readiness to bear the cost of minimizing the adverse impacts of their activities
(Ramsey and Rickson 1976, cited in Said et al. 2003, p.306).
2.2 Malaysia Current Recycling Campaign
Accordingly, various policies and strategies are currently developed and implemented by the
government in order to ensure sustainable development of the nation. The root causes of
environmental problems are related to human activities, consumption and production patterns, many
of which are unaffected by environmental policies. Studies by various scholars have demonstrated
that the quality of the environment depends

The Clustering Is A Data Mining Technique
Abstract: The Clustering is a data mining technique used to place data elements into related groups
without advance knowledge of the group description, which is a division of data into groups of
similar objects. The data representing by fewer clusters necessarily loses certain fine details, but
achieves generalization. It models data by its clusters. The data modeling puts clustering in a
historical perspective rooted in statistics, numerical analysis and mathematics. In this paper
represents the performance of three clustering algorithms such as EM, DBSCAN and
SimpleKMeans are evaluated. The Diabetes dataset is used for estimating and evaluating the time
factor for predicting the performance of the algorithms by using clustering ... Show more content on
Helpwriting.net ...
This paper presents comparison is made to find out which analysis option is the best for clustering
algorithm called EM, DBSCAN and SimpleKMeans. The test option there are four kinds of
parameter like supplied test set, training set, percentage spilt and class to clusters evaluation. The
training set is used to calculate the data set values. This paper uses the Diabetes dataset for
comparison of those algorithms.. The section 2 describes the literature review, Section 3 describes
the methodology for the Diabetes dataset and Section 4 describes the experimental result. Finally
Section 5 gives the Conclusion and Future work. 2. Literature Review: J.M. Pena et al., proposed to
perform the optimization of the BN parameters using an alternative approach to the EM technique.
We provide experimental results to show that our proposal results in a more effective and efficient
version of the Bayesian Structural EM algorithm for learning BNs for clustering [2]. C. Ambroise et
al., choosing a clustering algorithm that is well–suited for dealing with spatial data. In this
algorithm, derivative from the EM algorithm has been designed for penalized likelihood estimation
in situations with unobserved class labels and very satisfactory empirical results lead us to believe
that this algorithm converges [3]. Miin–Shen Yang et al., proposed

Exploratory Spatial Data Analysis Paper
1. Introduction
The escalating liberalization of international trade that occurred during the decades following World
War II under the impulse of various multilateral agreements and organizations has brought about a
dramatic change in the geographic scope of logistics and freight transportation systems. While new
trade ties have emerged with East Asia, long–time trading partners such as the United States and
European nations have also intensified their trade relationships, to the point that the European Union
is the largest trading partner of the United States and this trade represents 4% of U.S. gross domestic
product (BEA, 2010).
The intensification of long–haul trade routes has reinforced the critical role of seaports, as gateways
to ... Show more content on Helpwriting.net ...
We look at competition and positioning of these ports with respect to shippers and their geographic
location on the European continent. We separately consider their linkages with U.S. ports of entry;
finally, we also look at port traffic from the angle of the types of commodity handled. Each analysis
produces a functional space of European forwarding ports from one of the three perspectives. All
three perspectives are also brought together in a synthetic analysis of inter–port competition.
Commodity flow databases constitute a rich and multidimensional data source, but this information
will not be useful unless some rules and patterns are extracted from data in the form of functional
space, for instance. Exploratory spatial data analysis methods play an increasing role in analyzing
huge databases. It helps to understand, summarize, and classify the huge unorganized data, and,
more importantly, provides new hypotheses about data that can be subsequently applied in spatial
modeling. Multivariate statistical methods such as factor analysis, principle component analysis,
multidimensional scaling, and self–organizing maps deal with data reduction and have been used
effectively comprehend functional space, either implicitly or explicitly. Berry (1966) applied factor
analysis approaches to identify India's salient commodity flow patterns. R–mode analysis identifies
clusters of destination locations with similar profiles of incoming flows,

Outline For Qualitative Research Essay
Qualitative Research is a systematic process by which we attempt to answer a question or resolve a
problem of a phenomenon. Qualitative research is non–experimental in its approach to conducting
research. Qualitative research stays within the phenomenological paradigm meaning it is subjective
in nature, uses inductive analysis method. Its purpose is to explore, explain, and describe a problem
and uses multiple strategies of inquiry to collect data. Also, Qualitative research is concerned with
finding the answers to questions that begin with why? How? In what way? For example; 1) Why
people behave the way they do 2) How opinions and attitudes are formed 3) How people are
affected by the events that go on around them.
The three purposes/types ... Show more content on Helpwriting.net ...
Explanatory – to explain the patterns related to the phenomenon in question. Identify any plausible
relationships shaping the phenomenon. It explain the following general research questions: a) What
events, briefs, attitudes, or policies shape the phenomenon? b) How do these forces interact to result
in the phenomenon?
3. Descriptive – to document and describe the phenomenon of interest. It describe the following
general research questions a) What are the most important actions, events, beliefs, attitudes, and
social structures and processes occurring in this phenomenon?
The three qualitative research design is the blueprint for the collection, measurement, and analysis
of data.
1. Qualitative (Phenomenological – is the study of subjective experiences which are personal
experiences, interpretation, points of views, emotions and judgments. Exploring and understanding
the meaning of individuals or groups ascribe to as a social or human problem. It involves emerging
questions and procedures, data is collected in the participants setting, data analysis inductively
building from particular to general themes, researcher makes interpretation of the meaning of the
data. Qualitative is a non–experimental data collection

Using Data Analysis For Mixed Methods
There are many general guidelines that can help researchers in mixed methods designs to conduct an
effective data analysis. Data analysis strategy depends on the type of mixed methods design.
Creswell (2007) suggested number of essential considerations should be in account when
conducting mixed methods data analysis: 1. The analysis strategy should be used in order to answer
the research question(s) being asked. The questions are different according to the research design as
discussed earlier in this paper. 2. Data analysis approach will relate to either concurrent data analysis
(Triangulation and embedded designs) or sequential data analysis (Explanatory, exploratory, and
embedded designs). As discussed before, data may be merged or embedded in concurrent approach
and connected or embedded in sequential approach. 3. In the concurrent approach, the analyses of
quantitative and qualitative date are done separately, while in sequential approach the analysis of the
first step of the study is used to inform the second step. For example, in sequential approach, if the
first phase is qualitative design, then data analysis and the findings will guide data collection in the
second phase (e.g., quantitative phase). 4. Due to lack of available software for concurrent or
sequential approaches, matrix or discussion can be useful for comparing the results from qualitative
and quantitative data in concurrent approach. 5. There is some software available to export
qualitative data into

Hadoop Security Issues And Recommendations
HADOOP SECURITY ISSUES AND RECOMMENDATIONS
ABSTRACT
scale industries. Hadoop, as an open–source cloud computing and big data framework, is
increasingly used in the IT world. The rapid growth of Hadoop and Cloud Computing clearly
indicates its importance as a Big Data enabling technology. Due to the loopholes of security
mechanism, the security issues introduced through adaptation of this technology are also increasing.
Hadoop services do not authenticate users or other services. As a result, Hadoop is subject to
security risks. Big Data is already a prime target for vulnerable attacks due to the valuable
information it holds. In this paper, ... Show more content on Helpwriting.net ...
Because of this classification of data becomes even more important. Techniques such as encryption,
logging, and security measures are required for securing this big data. Usage of the Big data for
fraud detection looks very interesting and profit making for many organizations. Big data style
analyzing of data can solve the problems like advanced threats, cyber security related issues and
even malicious intruders. With the use of more sophisticated pattern analysis and with the use of
multiple data sources it is easy to detect the threats in early stages of the project itself. Many
organizations are fighting with the remaining issues like private issues with the usage of big data.
Data privacy is a liability; thus companies must be on privacy defensive. When compared to
security, Privacy should consider as profit making asset because it results in the selling of unique
product to customers which results in making money. We need to maintain balance between data
privacy and national security. Visualization, controlling and inspection of the network links and
ports are required to ensure security. Thus there is a necessity to invest ones in understanding the
loop holes, challenges, and components prone to attacks with respect to cloud computing, and we
need to develop a platform and infrastructure which is less protected to

Loyalty and Customer Relationship Management in Tesco Plc.
MBA
RESEARCH METHODS
Dissertation Proposal
Submission Date:12/10/2009
LOYALTY AND CUSTOMER RELATIONSHIP MANAGEMENT IN TESCO PLC.
CONTENT
PROVISIONAL TITLE........................................................................4
1. INTRODUCTION............................................................................4 1.1. The Importance Of
Customer Relationship Management.........4 1.2. Retail
Industry.....................................................................5 1.3.
Tesco.................................................................................7
2. LITERATURE OF VIEW..................................................................7 2.1. Customer
service................................................................8 2.2. Tesco's Concept Of Relationship Management
Begins And Ends With The Customer...................................................................8
2.3. Customer Strategies in TESCO..............................................9
2.4. Rhetoric vs. Reality..............................................................9
3. RESEARCH ... Show more content on Helpwriting.net ...
(Effect of CRM)
Loyal Customer can mean a consistent source of revenue over a period of many years. However,
this loyalty cannot be taken for granted. It will continue only as long as the customer feels they are
receiving better value than they would obtain from another supplier. There are many approaches
companies can usefully adopt to measure loyalty and many companies keen to help with various
tools and techniques. A large number of retailers have become so focused on customer basket
analysis, profitability and driving down operating costs that they have forgotten to listen to the real
voice of the customer. Indeed in a Bain Company survey in 2005, 80% of companies believed they
were delivering a superior customer experience; only 8% of their customers agreed.

[pic]
Figure 1.1. Effect of CRM
1.2. Retail Industry
Though global retail industry began humbly, it went onto become a business worth billions. Trade
was more of a barter system once, where the surplus produce was swapped for another. Cretans were
the earlier traders, who carried on the trade in the Mediterranean region. Then came the Phoenicians,
who not only traded but also distributed the goods across various regions. Romans followed and
they developed a sophisticated form of retailing. Many small shops came up and it is said that the
Romans built the first department store. It is these people who developed the retailing methods we
know today. But with the fall of Roman Empire, retailing

Which Areas Of London Are Positive?
Which areas of London are Positive? – New A Spatio –Temporal study of Positive Word use in
tweets Vidyaa Shyam, MSc Data Science, City University, August 2016 Abstract–This study
analyses the positive word usage in social media tweets and investigate if the tweets originate from
areas where the people are more happy and satisfied. It aims to find the distribution of these tweets
in both space and time in order to uncover recurrent themes & abnormal behavior. This is achieved
by analysing a large spatio–temporal dataset of London tweets using scatter plots, frequency graphs,
linked interactive visualisation and computional methods. It makes use of traditional spatio–
temporal analysis methods and clustering to group data by similarity. It compares the pattern against
a survey to find any themes. Index Terms–twitter, tweets, spatio–temporal, visual analytics,
Happiness score, positive words INTRODUCTION Twitter is an online social media platform that
allows people to create and share information and messages. Currently there are 310 million active
twitter users with 1 billion unique visitors every month [1]. Each information posted is a tweet
which has a maximum length of 140 characters. According to internet live stats more than 500
million tweets are posted every day worldwide. This vast source of human generated data is being
used for various applications such as Traffic analysis and management, Disaster analysis and
recovery, Urban development, spread of diseases, event

Techmetrics Of Dat Project Code And Designs
Exploratory impacts show that the calculation has unbelievable general execution and adaptability,
and that it's far a healthy contrasting option to established connection manage mining calculations
while the utilizer need to control the assortment of guidelines engendered [5]. Full complete flow of
this algorithm would be just like explain in figure 5.2 Figure 5.2. Process Flow 6. Project Code &
Designs Following are the steps that we follow in order to generate graphs of data: 6.1. Code Code
that we used in R language. Load CSV
tab=read.csv(file.choose(),header=T,sep=",",stringsAsFactors=FALSE) Put CSV File In Data Table
tabs <– as.data.table(tab) Top Calling ggplot(tabs[,. N, by=calling] [order(–N)] [1:20], aes (reorder
... Show more content on Helpwriting.net ...
Graphs Following are the graphs that were generated from R language: Graph 1: Top Amount Graph
2: Top Called Numbers Graph 3: Top Called Operators Graph 4: Top Calling Numbers Graph 5: Top
Group Name 7. Result Analysis As per result analysis of proposed algorithm, input support value
0.1, 0.2, 0.3 and 0.4 with the pattern length 3, 5, 10 provides good set of positive and negative
mining rules. In case of fortification value 0.5, we got result set of negative rules only. As 0.5
thresholds value has been set for class value to dissever positive and negative rules. Results of
proposed algorithm are as: Figure 7.1. Time analysis with support constant 0.1 Time Analysis
Support Pattern Length Time (Without AJAX) Time (With AJAX) 0.1 3 69ms 17ms 0.1 5 19ms
16ms 0.1 10 23ms 16ms Figure 7.2. Time analysis with support constant 0.2 Time Analysis Support
Pattern Length Time (Without AJAX) Time (With AJAX) 0.2 3 69ms 17ms 0.2 5 17ms 16ms 0.2 10
23ms 16ms Figure 7.3. Time analysis with support constant 0.3 Time Analysis Support Pattern
Length Time (Without AJAX) Time (With AJAX) 0.3 3 69ms 17ms 0.3 5 17ms 16ms 0.3 10 23ms
16ms Figure

Elmina Corporation Case Study
Introduction Elmina Corporation is a $25 million–dollar data collection and analysis company that
has been in business for less than two years. With expected growth by over 60% in the next eighteen
months, the company is looking for ways to leverage the collected data but wants to ensure that the
information technology (IT) infrastructure will support the expected growth that the company is
anticipating. The information technology will have to be redesigned to support the growth. With a
limited technology infrastructure to back the expected growth, all its IT must be redesigned for
support. The solution must be implemented in either a hosted solution, on–site solution, or a hybrid
model. Various technologies and partners are to be ... Show more content on Helpwriting.net ...
The goal is to encourage specific products to those customers most likely to acquire them, and to
determine which products a specific customer is most likely to buy. This helps to raise the
percentage of revenue to marketing costs. A description of current outsourcing and offshoring
activities. Offshoring web analytics is the current trend in outsourcing. Outsourcing web analytics is
possible since it's websites, the website's popularity and business performance are intertwined.
Outsourcing web analytics can be done ways, offsite and onsite web analytics. Most American
companies are choosing to offshore to India due to it being an English speaking, as well as it's cheap
labor (Singh, 2012). Offsite analytics allows you to analyze and quantify web activities. Since the
data needed for web analytics are created online, thus analysis can be conducted from any location.
Hence, clients that decide to send web analytics jobs pick low cost outsourcing destinations, as the
raw data can be transferred swiftly to any location through email or File Transfer Protocol (FTP).
When a client opts to outsource web analytics, various measures that ensure the level of security that
a client needs are adopted. Varied levels of security can be applied at both hardware and software
levels, utilizing firewalls, intrusion detection systems (IDS), intrusion prevention systems, along
with many more. The outsourcing team can have a small team working from the client's

Hadoop Security Issues
SECURITY ISSUES IN HADOOP SERVICES 10
Security Issues in Hadoop Services
Jogendra Chowdari Achanta
Adv Web App Using Web Services – CS 525
Professor Kihyun Kim
04/10/2016
Running head: SECURITY ISSUES IN HADOOP SERVICES 1
Abstract
scale industries. Hadoop is an open–source cloud computing and big data framework, is increasingly
used in the IT world. The rapid growth of Hadoop and Cloud Computing clearly indicates its
importance as a Big Data enabling technology. Due to the loopholes of security mechanism, the
security issues introduced through adaptation of this technology are also increasing. Hadoop
services do not authenticate ... Show more content on Helpwriting.net ...
With the use of more sophisticated pattern analysis and with the use of multiple data sources it is
easy to detect the threats in early stages of the project itself. Many organizations are fighting with
the remaining issues like private issues with the usage of big data. Data privacy is a liability; thus
companies must be on privacy defensive. When compared to security, Privacy should consider as a
profit making asset because it results in the selling of unique product to customers which results in
making money. We need to maintain balance between data privacy and national security.
Visualization, controlling and inspection of the network links and ports are required to ensure
security. Thus, there is a need to put the ones in insight the loop holes, conflicts, and the contents
ready for attacks with respect to cloud computing, and we need to develop a platform and
infrastructure which are less protected in attacks.
Literature Work
Hadoop is one of the open source frameworks, is used as extension to big data analytics framework
which are used by a large group of vendors. This type of framework makes work easy for the
companies how they?re going to store and can use the data within the digital products as well as
physical products (James, M. et al. 2011). We can analyze data using Hadoop, which is emerging as
solution to

Research Methodology: Two Types Of Research Methodology
1.4 Research Methodology This section is mainly about the study of the method that will be used to
examine the research problem. Method acts as a tool or instrument for measuring variable stated in
the objectives or the way of the study being conducted. Once researcher have chosen the scientific
method as the means of inquiry, researcher must make some decisions about how researcher are
going to gather all information (Kenneth & Bruce, 2005). There are two types of research method
can be used to collect data either quantitative method or qualitative method. The term of quantitative
research is numerical in nature and can be mathematically computed. Quantitative data measure uses
different scales, which can be classified as nominal scale, ordinal scale, interval scale and ... Show
more content on Helpwriting.net ...
The study of social fact influenced by many variables, thus qualitative research method is the best
measure to conduct this research. The term refer to any type of qualitative research study that
produces findings not arrived by the way of statistical procedures or other means of quantification
(Strauss & Corbin, 1990). The qualitative method, naturalistic approach is use to observe and
interpret reality with the purpose of developing a theory that will explain what was experienced. The
qualitative examination reflect some type of individual phenomenological point of view. Douglas
(1976) and Greertz (1973) believe that numerous substances exist and various elucidation are
accessible from distinctive people that are all similarly legitimate. Reality is a social construct. If
one function from this perspective, how one conducts the study and what decision a researcher
makes from a study are extensively not quite the same as those a scientist originating from a
quantitative position (Isadore & Carolyn,

Essay on Math 533
AJ DAVIS is a department store chain, which has many credit customers and wants to find out more
information about these customers. A sample of 50 credit customers is selected with data collected
on the following five variables:
LOCATION (Rural, Urban, Suburban)
INCOME (in $1,000's – be careful with this)
SIZE (Household Size, meaning number of people living in the household)
YEARS (the number of years that the customer has lived in the current location)
CREDIT BALANCE (the customers current credit card balance on the store's credit card, in $).
The data appears below, and is available in Doc Sharing Course Project Data Set as an EXCEL file:
LOCATION INCOME($1000) SIZE YEARS CREDIT BALANCE($) Urban 54 3 ... Show more
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Rather what I want you do is to highlight what you see for three individual variables (no more than
1 graph for each, one or two measures of central tendency and variability (as appropriate), and two
or three sentences of interpretation). For the 10 pairings, identify and report only on three of the
pairings, again using graphical and numerical summary (as appropriate), with interpretations. Please
note that at least one of your pairings must include Location and at least one of your pairings must
not include Location.
All DeVry University policies are in effect, including the plagiarism policy.
Project Part A report is due by the end of Week 2.
Project Part A is worth 100 total points. See grading rubric below.
Submission: The report from part 4 including all relevant graphs and numerical analysis along with
interpretations.
Format for report:
Brief Introduction
Discuss your 1st individual variable, using graphical, numerical summary and interpretation
Discuss your 2nd individual variable, using graphical, numerical summary and interpretation
Discuss your 3rd individual variable, using graphical, numerical summary and interpretation
Discuss your 1st pairing of variables, using graphical, numerical summary and interpretation
Discuss your 2nd pairing of variables, using graphical, numerical summary and interpretation
Discuss your 3rd pairing of variables, using graphical,

The Patient Population Of Interest Essay
Data was collected in the health center, after the permission to conduct the study with the patient
population of interest was secured from the administrator of the health center. Data was collected
from January 5th, 2017 to March 5th, 2017. Patients in the waiting room were handed flyer
invitation to participate by the researcher, who goes over the contents individually with prospective
participants to determine eligibility for the study based on the criteria. The flyer explains the
purpose of the study, the population of interest, including how to participate, and once a patient is
eligible he or she is given a cover letter written in both English and Spanish that explains that
participation is voluntary, and that submitting the survey amounts to giving a consent to participate
in the study. The cover letter also explains that there are no benefits to the study, but the results will
be posted in the health center where the patients were recruited. After reading the cover letter and
agreeing to participant in the study, the patient is handed paper versions of both bilingual surveys
along with writing instruments. When a participant is done and has submitted their completed
surveys, he or she will be given a copy of correct responses to the DKQ–24 in both English and
Spanish.
Variables
The variables in this study are diabetes education preference, knowledge, and ethnicity. There are
two dependent variables and one independent variable. Education preference is the first

Presentation Chapters 3 5
Nicole Fiamingo's
Presentation
MKT 730 – Marketing Research
February 17,
Dell Printer: "The New Kid On The
Block"
http://www.youtube.com/watch?v=42mNRePWIME
Chapter 3
Research Design
Marketing Research Proposal
1.
2.
3.
4.
Executive Summary
Background
Problem Definition
Approach to the problem
5. Research Design
6. Fieldwork/ Data Collection
7. Data Analysis
8. Reporting
9. Cost and Time
10.Appendices
What the Research Design Includes
1. Define the information needed

2. Design the exploratory, descriptive, and/or causal phases of the research 3. Specify the
measurement and scaling procedures
4. Construct and pretest a questionnaire/ data collection
5. Specify the sampling process and sample size
6. Develop a plan of data ... Show more content on Helpwriting.net ...
http://www.businessinsider.com/2009/1/dell–losing–market–share–in–anemic–pc–market–dell
Dell Running Case
1. Search the internet to find information on the latest U.S market share of Dell and other PC
marketers.
2. Search the Internet to obtain information on Dell's marketing strategy. Do you agree with Dell's
marketing strategy? Why or why not? 3. Visit the U.S Census Bureau at www.census.gov. As Dell
seeks to increase its penetration of U.S households, what information available from the U.S Census
Bureau is helpful?
4. What information available from syndicated firms would be useful to
Dell as it seeks to increase its penetration of U.S households?
Dell's Current Marketing Strategy
"Dell is committed to delivering new dimensions in entertainment, mobility and gaming and is
leading the industry in advancing new technologies like 4G solutions and 3D–capable laptops to
provide the best entertainment and mobile experiences ever imagined," said Steve
Felice, president of Dell's Consumer, Small and Medium Business unit,
In support of the company's "Power To Do More" brand positioning, Dell recently introduced its
first–ever Consumer marketing campaign, "You Can Tell It's Dell." "The Power To Do More"
captures Dell's belief that technology serves an important purpose in helping every one of our
customers –– from large enterprises to public institutions, specialized technology services clients,
small and medium–sized business and consumers ––

The Concepts Of Big Data Analysis Essay
Abstract–The important goal of this paper is to provide an overview on the concepts of big data
analysis. Furthermore the growing emergence and importance of qualitative data analysis in the field
of business intelligence and data science is broadly explained .It also marks out effective tools and
techniques used to obtain prominent qualitative analytic results on the global level. Moreover we
have concluded on the basis of comparison of the tools depending on various factors and parameters
by representing it on a tabular manner. I. INTRODUCTION Today, the data consumption rate is
tremendously expanding, the amount of data generated and stored is nearly imperceivable and
highly growing. Big data that is nothing but a large volume of unstructured or structured data that
runs in and out in to a business on daily basis. This big data is analyzed in order to achieve
prominent business growth and improved business strategies [1]. Every year there is at least 40%
increase in the amount of data growth on global level, leading to which companies have started
adopting new data analytic techniques and tools and also have stepped ahead moving their data
towards the cloud for their big data analytic requirements and for better analysis.[3][2] In big data
analysis it is not the amount of data that is essential but how efficiently we handle, process and
analyze it is the key factor. Big data analysis doesn't revolve around how much data we occupy, it
deals with how well you make use

Exploratory Data Analysis
1. 1 Displaying Distributions with Graphs 1. In statistics, individuals are the objects described by a
set of data, they can be people or animal or things. 2. In statistics, a variable is any characteristic of
an individual. A variable can take different values for different individuals. 3. Exploratory data
analysis is an examination where statistical tools and ideas can help you examine data in order to
describe their main features. 4. A categorical variable places an individual into one of several groups
or categories while a quantitative variable takes numerical values for which arithmetic operations
such as adding and averaging make sense. 5. It is useful to use a bar chart when dealing with counts
6. It is useful to use a pie chart when ... Show more content on Helpwriting.net ...
The median is resistant to extreme observations because its based on the middle one or two
observations of the ordered list, while the mean is nonresistant due to the influence of few extreme
observations. 5. In stats, spread is a way to measure the variability of the observations around the
center. 6. To calculate Q1 and Q3 , you must first arrange the observations in increasing order to find
the median, Q1 is the median of the observations whose position in the list is to the left of the
overall median, Q3 is the median of the observations whose position in the list is to the right of the
overall median. 7. The five–number summary is minimum, Q1, median, Q3, maximum 8. Standard
deviation measures spread. 9. The relationship between variance and standard deviation is that the
standard deviation(s) is the square root of the variance s2. 10. Standard deviation equals zero when
there is no spread, that only happens when all observations have the same value. 11. Standard
deviation is non resistant to extreme observations because strong skewness or a few outliers can
make the standard deviation very large. 2.1 Density Curves and the Normal Distributions A density
curve is a curve that is always above or on the x–axis and one that has its area exactly one
underneath that

NT1210 Unit 1 Assignment 1
3.9 Data Processing and analysis.
The unit of analysis was the individual mobile phone service subscriber. Data were analyzed in two
major stages, that is, through descriptive statistics and specific tests of hypothesis. Cross tabulation
was used to analyze demographic variability of the respondents. Descriptive statistics including
measures of central tendencies, measures of dispersion, frequencies and percentages were calculated
to examine the respondents' characteristics. These statistics showed the basic characteristics of
research variables. The second stage of analysis focused on testing specific hypotheses of this study.
To analyze qualitative data, content analysis was used following the suggestion by SPSS BOSS
(2015). As noted by ... Show more content on Helpwriting.net ...
As noted by Zikmund, Babbin, Carr & Griffin (2013), the importance of editing, coding and
entering the data is to ensure that errors are checked. Saunders et al. (2009), on the other hand, note
that no matter how carefully data is coded and entered, there will always be some errors which need
to be checked and corrected before the data is analyzed. After this initial processing, the data was
analyzed using Statistical Package for Social Science (SPSS) version 20. In the second step, to get a
feel of the data, descriptive statistics was used. As explained by Saunders et al. (2009), descriptive
statistics enable the researcher to numerically describe and compare the variables under study. This
includes response rate; measures of central tendency, notably the mean; measures of dispersion,
particularly the standard deviation for Likert scale variables in the questionnaire (Magutu, 2013;
Gathenya, 2012). Descriptive statistics covered all response variables and the demographic
characteristics of respondents, which were analyzed using cross tabulation. As explained by Magutu
(2013, pp 84 ), 'descriptive statistics provide the basic features of the data collected on the variables
and provide the impetus for conducting further analyses on the

Marketisation Of Home Care
Methodology For describing the research design of the thesis, this chapter aims to explain the
methodology and epistemological approach of my research, to provide the detailed outline of
research design and research process, to address the ethical issues involved in the fieldwork, and to
reflect the limitations. 1. Methodological approach The methodological approach of this study is
guided by its aims and research questions. This section starts from the clarification of research
questions to explain why the qualitative methods are employed. This study aims to examine how the
marketisation process is shaping the development of home care policy and practice for older people
in urban China. The focus of this thesis is how the role of the market is developing; how the other
sectors respond to these changes; and how the care regime is shaped and, in turn, shaping the
practice in urban China. This leads to the research objectives: to examine the rationale behind the
marketisation of care, to understand how home care has been marketised in urban China since the
1990s, to investigate processes of marketisation of home care, and to explore existing and potential
impacts of marketisation of home care on the care system and participants in the care market in
urban China. Among key stakeholders in the care market, namely service users, providers,
purchasers, care workers, and regulators, central to my study is experiences and viewpoints of
service providers and local regulators. The

Research Methodology : Research Methods
3. Research Methodology
3.1 Introduction
This chapter will focus on the research methods application and explain why use these research
methods to accomplish this case study. This study was used the qualitative method to collect various
data in order to get more depth and comprehensive information to explore the questions of this
study. The qualitative method mainly used in exploratory research on objectives, it can be used to
obtain potential causes, ideas, and motivations. Furthermore, the qualitative method can also provide
more in–depth problem–solving methods and explore new ideas in objectives of the study (Cohen,
2006).
3.2 Primary research: Questionnaire and Interview
Primary research is the method that obtain the first–hand data from experiments and investigation. It
contains several different methods such as questionnaire, interviews and surveys (Blaxter, 2006).
Due to changes in China 's luxury consumption is from the beginning of 2014, there are only a few
secondary sources can be found on the Internet. Therefore, this study has mainly focused on gather
first–hand information and resources on the analysis of changes in China 's luxury consumption.
In the questionnaire part, by the time of Easter holidays, I went back to China to do this research. I
have created 120 copies of the questionnaire in order to collect more information from different age,
sex, and occupation, region, and education Chinese consumers. The primary research has conducted
in two major

History Of Retro Back 1970 ( Rb70 )
Introduction
Retro Back 1970 (RB70) is the newest hair care product of the Johnson Product line. The Johnson
Product hair line of products was well known in the 1970's for having the best products for African–
Americans. Johnson Products had two divisions in their hair line. One was Ultra Sheen, the other
was the natural hair line marketed as Afro–Sheen. The slogan was, soul food for the natural.
However, there is not a lot of statistical data to support what is known. The sale of relaxers is on the
decline. Since hair maintenance is not optional, it is safe to assume that if relaxers are on the
decline, then natural hair products are on the rise. This is a booming market. The research firm
Mintel estimates the total sale of Black hair products totaled $2.7 billion in 2015. (Mintel, 2016)
RB70, the new perspective, is the organic hair line that has been designed with concerns of hair
ingredients and health in mind. The research team is composed of cosmetic chemists, and highly
trained trichologists to ensure the best, and safest products are produced. The marketing department
will be working with Ashley Webelhuth to establish our brand. In addition, business analysts have
been interviewed, a team of business analyst have been organized to move forward. The analyst will
bring their expertise from other companies.
The team of business analysts come from various backgrounds. Business analytics will be used to
gather data. This data will include, demographics,

The Stata And Hlm Software
The HSLS data was collected through a stratified two stage sampling process. That is, 944 schools
(including public, private, and charter schools) were sampled in the first stage and in the second
stage 25, 206 students selected in 50 states in the District of Columbia (Ingels et al., 2011, 2014).
There were approximately 27 students selected per school. To cater for the complex sampling, the
data include weights (students weights, parent weights, teacher weights, counselor weights, and
school weights) that should be used to account for the sampling and non–response at the two stages
of the data collecting process. For these reasons STATA and HLM software will be used in the data
analysis process because these software can be used with weights and correction of standard error.
The HSLS has missing data that has been cleaned and the analysis process made easier because the
data includes variables in which imputation methods (weighted sequential hot–deck [WSHD] were
used to replace the missing data (see Ingels et al., 2011, 2014). To answer the first research question
that examines the trends in (i) algebra achievement, (ii) OTL across the three time points analysis
using descriptive statistics to compare differences by race, location, States, SES, and school type
will be used and presented using a combination of line graphs and histograms. The algebra
achievement used will be the probability of proficiency scores, which is criterion referenced, to
show the gains in

Fairever Fruit Fairness Cream
Fairever Fruit Fairness Cream
Date: April 18th, 2008 (Submitted in partial fulfillment of the II semester course
Requirements of MBA 2008–2010 batch)
Submitted by Group NO:10 K. ASWINI NAIR (CBBUP2MBA08043) NANDAGOPAL N
(CBBUP2MBA08059) SINDHUJA DURGA R (CBBUP2MBA08099) SOORYA SOMAN
(CBBUP2MBA08101) SOUMIA S (CBBUP2MBA08103) SREEDEVI AJITKUMAR
(CBBUP2MBA08105) 1
CERTIFICATE
This is to certify that we, the students of Amrita School of Business, are submitting the following
document as part of the partial fulfillment of the Marketing Management course requirements of
second semester MBA for the batch of 2008–10. The report stands genuine to the best of our
knowledge and a tool to be used, only with the objective of academic ... Show more content on
Helpwriting.net ...
4
TABLE OF CO TE TS
1.0 I TRODUCTIO TO THE PROJECT ................................................................... 8 1.1
BACKGROU D OTE– Literature review......................................................... 9 1.2 Limitations of the
research................................................................................... 10 1.3 Structure of the
report.......................................................................................... 11 2.0 Research design
......................................................................................................... 13 2.1 Management Decision
Problem: .......................................................................... 13 2.2 Market Research Problem:
.................................................................................. 13 2.3 APPROACH TO THE
PROBLEM..................................................................... 14 2.4 RESEARCH QUESTIO S A D
HYPOTHESIS.............................................. 15 2.5 EXPLORATORY RESEARCH
.......................................................................... 17 2.5.1 Secondary data
analysis................................................................................. 17 2.5.2 Focus–group
.................................................................................................... 17 2.5.3 Depth Interview

Exploring The Lean Training Strategies That Healthcare...
Organizational managers are using lean and other process improvement strategies to address
inefficiencies or defects through standard work and employee training (Poksinska, 2010). Employee
training is an essential element in the sustainment of process improvements (Dibia et al., 2014).
Bertram et al. (2013) defined training as the understanding, abilities, and mindset to learn and
address tasks or job duties within the workplace environment. Exploring the lean training strategies
that healthcare managers may adopt is the focus of this qualitative exploratory case study.
Section 2 encompasses the project plan of the study. The detailed plan includes the purpose
statement, role of the researcher, purposive sampling to identify participants, research method, and
research design. Ethically conducting research is an important component of any research project.
Section 2 also contains the ethical aspect of identifying the target population, participant sampling
requirements, data collection instrument, data collection techniques, and data analysis. Research
should be reliable and valid. Section 2 contains the process to ensure reliability and validity.
Purpose Statement
The purpose of this qualitative case study is to explore how healthcare managers successfully
implemented lean training strategies to combat escalating costs. The target population will consist of
healthcare managers in a single rural care hospital located in Tennessee who have implemented lean
training strategies

Data Mining Project : Using Customer Segmentation
3.0 Methodology
Easily customisable, CRISP–DM process model was used to implement this data mining project.
With respects to this methodology, this study suggests to identify high value customers and to
profile the customers using customer segmentation techniques and by effectively utilising data
mining methods. There are mainly four phases for this analysis. Figure 3.1 illustrates the research
methodology of this study.
3.1 Business and Data Understanding
Typically, this phase involves gathering business requirements of the retailer and understanding the
available data. As a company which is operating in the retail industry, the retailer for whom the
analysis was performed has to face an immense amount of competition. One of the ... Show more
content on Helpwriting.net ...
For each time that the customer is completing a shopping at the retailer, one record will be added to
the Transaction table. And per each item that the customer is purchasing, records will be added to
the Item table which is linked to the Item Master table that contains the main information about all
the items sold by the retailer. The branch table gives details about the Branches of the retailer.
3.2 Data preparation
Data preparation is one of the most significant and time–consuming phases of data mining projects
(Steinbach et al., 2005; Han et al., 2006; Yau, 2011). The data needs to be prepared to be in an
appropriate state for analysis, maintaining its representativeness of the real world but in a format
that is appropriate for the data analysis tools. Therefore, data pre–processing techniques like data
selection, data cleaning, constructing new data, integrating data and transformation of data were
used in this case study.
3.2.1 Data Selection
As mentioned before, from the relational database of the company five main tables were used for the
analysis. Out of those five tables Customer table, Transaction table and Item table were used as the
main sources of data and Item master table and Branch table were used as reference tables. When
the customer table is considered, there were records for the online customers as well as for the in–
store customers. Some of

Nurses ' Experience Of Power From The Perspectives Of...
Research Topic
Nurses play a very important roles in patient care. They have the power to improve the quality of
care, thus improving patient outcomes. According to Fackler, Chambers, and Bourbonniere (2015),
the phenomenon, which is stated in this study, is that the concept of power is usually viewed by
nurses ' perception of their work environment; the environment that allows them to take proper care
of their patients. Furthermore, based on the social theory that power is defined as the capacity to
achieve goals, the authors believe that if the nurses ' capacity to achieve goals for themselves and
their patients culminates in a positive conclusion, then power becomes an essential phenomenon to
explore (Fackler et al., 2015).
Purpose
It was clearly stated that "the purpose of this study was to explore hospital nurses ' lived experience
of power" (Fackler et al., 2015). Furthermore, "this study identifies the meaning and experience of
power from the perspectives of hospital clinical nurses" (Fackler et al., 2015).
Research Question "The main questions this study sought to answer included: what is the lived
experience of power to hospital clinical nurses; and what is the meaning of power to hospital clinical
nurses in the context of their professional role?" (Fackler et al., 2015). Based on the fact that the
goal of a phenomenological research is to achieve understanding of an experience from the
perspective of the participants (Schmidt & Brown, 2015), I found these

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