Analyzing Market Trends And Developments

Analyzing Market Trends And Developments
Interpret market trends and developments
From: eswarripradha0607@gmail.com
To: ling@barklycollege.com
Subject: Analysing the market report for TOP Take Away Restaurant
Dear Ling,
Referring to the matter, kindly find this email with the report of market analysis for your further
review. Kindly drop comments for any areas that need to be improve.
Thank You.
Kind regards,
EswarriPradha
Assessment Task 2: Project – Market analysis
Introduction
Restaurant businesses has always been known to be a competitive industry with many variations,
which range from small, family owned to large inter–chain franchises throughout the world with
many years of experience. Today I would like to make a short review on one of the business which
is related to ... Show more content on Helpwriting.net ...
Analyse the market performance of existing and potential competitors on business and their
products and services to determine the possible opportunities or threats in market.
Analyse the market performance and information from all extents of the business to decide the
accomplishment of marketing exercises.
Identify over–performing and under/low performing goods and services that need to considered for
redevelopment or withdrawal for better performance.
Estimate and forecast existing and developing business sector needs based on the data that available
using any forecasting methods.
Access a range of both internal and external data relevant to the business you have chosen and to
assist you in identifying market trends and development such as changes in technology,
demographic trends, economic trends, government activities environmental trends, social and
cultural factors and any other relevant trends and developments. The data you access can come from
a range of internal and external sources and may include graphs, charts or spreadsheets or any other
relevant information.
Company marketing research report:
The company's internal data and market knowledge is vital, the most essential data which a a

company need is the knowledge of the right product and services that a client needs. Hence time to
time market and customer research is required for new item thoughts and in addition expected
change in procedures. This sort of data can be costly to assemble
... Get more on HelpWriting.net ...

Psy 270 Week 4 Staffing Question Paper
that describes how each of these predictors has affected staffing levels in the past. This equation is
used to predict future staffing levels. This is an example of ______________: a) regression analysis
b) ratio analysis c) trend analysis d) Markov analysis
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Question 2 5 / 5 points
Affirmative action plans and programs do not originate from ________. a) voluntary employer
efforts b) court–imposed remedies for discriminatory practices c) consent agreements d)
international treaties
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To have a high probability of being acceptable in the eyes of the Supreme Court, an organization's
AAP should __________. a) not necessarily interfere with the job status of ... Show more content on
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a) a type of behavior that is observed on the job b) an underlying characteristic of an individual that
contributes to job or role performance c) a latent component of the job characteristics matrix d) a
compilation of the tasks, duties, and responsibilities that make up a job
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Which of the following is a good definition of a job category? a) A grouping of elements to form an
identifiable work activity that is a logical and necessary step in the performance of a job b) A
grouping of jobs, usually according to function c) A grouping of jobs according to generic job title
or occupation d) A grouping of positions that are similar in their tasks and task dimensions
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It is critical than when employees are interviewed about their reward preferences, the content of the
interviews is _____________. a) made public so managers can match employee preferences
immediately b) kept confidential so employees can report honestly c) developed through an
informal process so employees feel comfortable d) generally less important than the process used in
asking questions
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An interdependent collection of employees who share responsibility for achieving a specific goal is
called a ______. a) project

Variables Of The Independent Variable Time
The time series is a group views sorted by time (and often time periods equal and successive periods
vary according to the nature of this phenomenon). And time series have important applications in
many areas, including economic, trade and population statistics. As time–series models are typically
used to predict the variable values. If the variable to be studied is known determinants, and the
factors that affect it, is also used in the case of variable is subject to the expectations of its clients,
which is reflected in the future based on what happened in the past. Mathematically: we say that the
independent variable time (t) and the corresponding values him dependent variable (y) and that each
value at time t corresponding values of y variable y is a function of time t in which: y = F (t), The
time series analysis of statistical methods task method, which has evolved a lot, and it was possible
to use it for the purpose of expectation for the future supply and demand for a commodity or
service. And supports time–series analysis to track the phenomenon style (or variable) over a certain
time (several years, for example), then expect for the future based on different values that have
emerged in the time series and the pattern of growth in values; and this is superior to the
conventional method, since the method traditional calculates the difference in value between the
only two date ranges of the time series and builds future expectation on the basis of which, without

Forecasting Methods
Introduction
All businesses are confronted with the general problem of having to make decisions under
conditions of uncertainty. Management must understand the nature of demand and competition in
order to develop realistic business plans, determine a strategic vision for the organization, and
determine technology and infrastructure needs. To address these challenges, forecasting is used.
According to Makridakis (1989), forecasting future events can be characterized as the search for
answers to one or more of the following questions:
„X What new economic, technical, or sociological forces is the organization likely to face in both
the near and long term?
„X When might these forces impact the firm¡¦s objective environment?
„X Who is ... Show more content on Helpwriting.net ...
Economists relay on this type of forecasting model to forecast business cycles and related
developments. This method could prove inaccurate if the forces that drove past events are no longer
present.
„X Market Research Forecasting: This forecasting method collects data in a variety of ways such as
surveys, interviews and focus groups to evaluate the purchase patterns and attitudes of current and
potential buyers of a good or service. Designers of goods and services use this method to understand
their current customers and the buyers they would like to serve.
„X Dlephi Method: The Delphi method compiles forecasts through sequential, independent
responses by a group of experts to a series of questionnaires. The forecaster compiles and analyses
the respondents¡¦ input and develops a new questionnaire for the same group of experts. This
sequence works towards consensus that reflects input from all of the experts while preventing any
one individual from dominating the process (Chase, 2005).
Quantitative Techniques Quantitative forecasting techniques transform input in the form of
numerical data into forecasts using methods in one of three categories. Each category of quantitative
forecasting methods assumes that past events provide an excellent basis for enhancing the
understanding of likely future outcomes.
„X Time Series Analysis: Time series analysis is based on

Is Walmart Safe?
Is Walmart Safe?
The Effects of Established Supercenter Walmarts to Property Crime Rates within Dekalb and
Gwinnett County from 1999–2010
Class: Economics & Finance Modeling
Professor: Doctor Derek Tittle
Dream Team Group Members:
Alexandra E Steingaszner
Brian–Paul Gude
Kristopher Bryant
Norman Gyamfi
Samantha Gowdy
|
Disclaimer
This report has been created in the framework of a student group project and the Georgia Institute of
Technology does not officially sanction its content.
Executive Summary
Every year, Walmart is accused of increasing crime in areas within which it builds Walmart
Supercenters. Yet, research and data analyses largely disprove these claims, as they reveal that other
factors such as ... Show more content on Helpwriting.net ...
Iterations of analysis eliminated data points that were listed as "unusual observations," or any data
point with a large standardized residual. After 5 iterations, the analysis showed improved residual
plots. Randomness in the versus fits and versus order plots means that the linear regression model is
appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the
data, and a bell shaped curve in the histogram illustrates the normality of the data.
Because of the method of monthly data collection, absolute randomness could not be obtained;
however, it was decided that 5 iterations was sufficient because the sixth iteration showed a decrease
in the quality of the residual plots. The first test performed was the p–value test of the individual
variables. A p–value is the probability, ranging from 0 to 1, of obtaining a test statistic similar to the
one that was actually observed. The only input that did not have a p–value less than 0.05, which was
the chosen significance level, was the "Number of Walmarts" variable; the number of Walmarts has
no specific effect on the output, property crime rate. The R2 of the analysis, or the coefficient of

determination, provides a measure of how well future outcomes are likely to be predicted by the
model. R2 values range from 0 to 100% (or 0 and 1) and the

Associative and Time Series Forecasting Models
Forecasting Models: Associative and Time Series
Forecasting involves using past data to generate a number, set of numbers, or scenario that
corresponds to a future occurrence. It is absolutely essential to short–range and long–range
planning.
Time Series and Associative models are both quantitative forecast techniques are more objective
than qualitative techniques such as the Delphi Technique and market research.
Time Series Models
Based on the assumption that history will repeat itself, there will be identifiable patterns of
behaviour that can be used to predict future behaviour. This model is useful when you have a short
time requirement (eg days) to analyse products in their growth stages to predict short–term
outcomes.
To use ... Show more content on Helpwriting.net ...
This can be analysed using either the multiplicative or additive method. In the additive version,
seasonality is expressed as a quantity to be added to or subtracted from the series average. For the
multiplicative model seasonality is expressed as a percentage (seasonal relatives or seasonal
indexes) of the average (or trend). These are then multiplied times values in order to incorporate
seasonality.
Associative Models
Also known as "causal" models involve the identification of variables that can be used to predict
another variable of interest. They are based on the assumption that the historical relationship
between "dependent" and"independent" variables will remain valid in future and each independent
variable is easy to predict. This form of analysis can take several months and is used for medium–
term forecasts for products in their growth or maturity phase.
The procedure for this model is to collect several periods of history relating to the independent and
dependent variables themselves, establish the relationship that minimizes mean squared error of
forecast vs actual using linear or non–linear and singular or multiple regression analysis.

So you first predict the independent variable, then look at the established relationships between that
independent variable and the dependent ones to predict what the dependent variables will be. You
then develop an equation that summarizes the effects of predictor variables.
To do this you will need aggregate data which

Neural Networks : An Important Component Of Determining...
Neural Networks in Finance
2600 Words
By Maria L. Vicente
University of Hawaiʻi at Hilo
QBA 362
Fall 2016
Introduction
Predictions are an important component of determining the financial progress of a business.
Business decisions rely on forecasting techniques to predict things such as price movements or
overall success in markets. In the attempt to forecast market predictions, it must be assumed that
future occurrences may be partly based on present and past data (Abu–Mostafa, Yaser S 1996).
Further assumptions must be made to conclude that there is a predictable pattern in past data. There
is evidence for both the idea that financial market forecasting is futile due to the unpredictable
nature of finance, as well as for the idea that financial markets are predictable to an extent. The
consequences of financial decision–making imply an inherent need for the use of forecasting tools
in making predictions about future occurrences. The issue resides in the fact that there is an
abundance of data and information that must be organized and interpreted. A number of techniques
may be used to manage present and past data in order to create a forecast prediction, though with
more research and trials, neural networks have been shown to be superior in performance.
Traditional Techniques Neural networks provide an alternative solution to the traditionally used
statistical methods of forecasting. Traditional method models include variances of linear

Time Series Analysis
The existing literature proposes various methodologies and procedures to predict GHG emissions in
the transport sector. In general, these studies utilize time series analysis, regression analysis,
decomposition, and optimization models, as explained below:
Time series analysis
(Sultan 2010) introduces the use of co–combination of pay per capita and fuel price (FP) to measure
transport fuel consumption (FC), while (Bekhet, H & Yasmin 2013), (Bekhet, HA & Yusop 2009),
(Ang 2008), (Ediger & Akar 2007), and (Wang, SS et al. 2011) discover a relationship between
vitality utilization and CO2 discharges. (Begum et al. 2015) consider the impact of GDP, FC, and
concentration of population on the CO2 emissions. (Ivy–Yap, LL & Bekhet 2015) ... Show more
content on Helpwriting.net ...
Strategic planning tools
Greaves (2009) evaluates the impacts of air quality and GHG reduction using a strategic–level
modeling tool that considers freight travel, the characteristics of the fleet, and the factors related to
GHG and non–GHG emissions.
Linear programming models
Researchers have also proposed a number of optimisation models to predict GHG emissions,
especially from the energy sectors. (Börjesson & Ahlgren 2012), (Bai & Wei 1996), and (Wang, C et
al. 2008) explore the cost–effectiveness of conceivable CO2 reduction choices for the energy
industries. Furthermore, utilizing a mixed integer linear programming model, (Hashim et al. 2005)
contemplate the impacts of fuel balancing and fuel switching choices on power generation. Their
studies reveal that FE and fuel switching are the best choices to decrease CO2 discharges. (Tan et al.
2013) utilize mixed integer linear programming analysis to perfectly arrange waste to the level of
vitality that best minimizes electricity generation costs and CO2 emissions.
Generally, after a period of time, the road transport sector's GHG outflows demonstrate a pattern.
Therefore, through the use of statistical forecasting techniques, researchers and planners can
anticipate future outflows. After (Brown 1957) and

Supply Chain Management and Time Series Methods
Executive summary 1. Introduction 2. Background 3.1 Costco history (departments, countries,
organization, structure of Costco) 3.2 Why enter Australia? 3.3 Cash & Carry 3.4 Retail
logistics (pallets in store) 3.5 Wholesale & retail 3. Discussion 4.6 Product (Quyen) * Target
customers: for women at big events such as: Mother Day n Christmas. * Peak season at Oz – cold
weather from May until Xmas. * Another high season on Valentine Day in Feb. 4.7 Diagram
(Quyen) GANTT chart 4.8 Forecast (Quyen) 4.9.1 Reason –
Supply chain management decisions are based on forecasts that define which product will be
required, in what amount, and when they ... Show more content on Helpwriting.net ...
Regardless of the approaches employed, the overall objective of the evaluation process should be to
reduce purchase risk and maximize overall value to the purchaser.
An organization must select suppliers it can do business with over an extended period. The degree
of effort associated with the selection relates to the importance of the required goods or services.
Depending on the supplier evaluation approach used, the process can be an intensive effort requiring
a major commitment of resources (such as time and travel). This section addresses the many issues
and

A Study on S&P 500 Index Stock Return and Volatility Using...
A Study on S&P 500 Index Stock Return and Volatility using ARIMA and GARCH Modeling
Kaiyuan Song, Di Wu Summary
In this project we first checked consistency and seasonality of S&P500 index stock performance by
splitting its recent twenty years historical data into ten two year data and built ARIMA and GARCH
models for each sub–period. We found that the models are considerably consistent before 2007–
2008 sub–period, and there exists some minor seasonality in several subperiods, but no particular
pattern can be identified for the whole period. We then tried to predict future return, volatility and
VaR using the model we built for the last sub–period based on rolling forecast procedure. Though
the fitted values of 10th sub–period model are ... Show more content on Helpwriting.net ...
All of the fitted returns are very close to zero as expected and all fitted volatilities vary according to
fluctuations in actual returns: it goes up when there were large fluctuations and vice versa. Two
sample plots are shown below:
Our prediction model, the tenth period model, fitted the data especially well as illustrated below.
Not only the volatility prediction were accurate, the mean part also provides considerably nice fit.
With the excellent fit from our prediction model, we expect our predictions to be fairly dependable.
Nonetheless, when comparing actual future return with our predicted return and volatility obtained
from 5–day ahead rolling forecast procedure, the results were rather unsatisfactory. All of the
predicted volatilities were considerably high and did not move along with real fluctuations in return
series, which resulted in very significant value at risk. In addition, the return predictions were no
much better than just using sample means, which were all very close to zero, to predict future return.
The prediction vs actual return plot for 60 days is shown below.
To improve our predictions, particularly for volatility part, one–step ahead rolling predictions were
computed, and its prediction vs actual return plot is illustrated below:
Due to the return predictions made by ARIMA were similar to one–step results and not much
better than sample mean prediction, we focused on volatility part and found that one–step

Factors Model For The Fama And French Three Factor Model
Literature Review Since CAPM was accepted and admitted in fundamental concepts by most people
in financial economics, factor model researching becomes a popular topic in finance. In 1992,
Eugene Fama and Ken French established the empirical foundations for the Fama & French Three–
Factor Model. It is designed to capture the relation between average return and size and the relation
between average return and B/M (price ratios).
The three factors model can be described by the equation below: Rit – RFt = ai + bi(RMt–RFt) +
siSMBt +hiHMLt + eit
Where:
Rit is the return on security or portfolio i for period t
RFt is the risk–free return
Rm–Rf is the return spread between the capitalization weighted stock ... Show more content on
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Fama and French believed that the five–factor model provides better descriptions of average returns
than the three–factor model. However, the value factor, HML becomes redundant in U.S. data
sample for 1963–2013 after adding profitability and investment factors. And if five–factor model
drops HML, the new performs should be equal or very close to the performs of the five–factor
model (Fama&French, 2015).
Estimation Methodology
Data Description: In this research, we use Hongkong stock data to estimate Chinese stock market
because most of the big Chinese business companies are listed in Hongkong stock market
(Hongkong actual return data in the left hand was from Thomson Reuters' Datastream). This actual
return data was gathered for all firms listed in Hong Kong Research List (equity list name is
HGKG). Moreover, there are many different portfolios in this data. We require the security which is
traded on the country's major exchanges and exclude real estate investment trusts, global depository
receipts, and preferred stocks. Also, we decide that the returns from some months are set to missing
if the total return index is unchanged for three months in a row. The reason why we do this is to

Big Data On Oil And Gas Sector Essay
Big Data in Oil & Gas Sector 1. Introduction Big Data in Oil and Gas industry is not something
new. The industry has long dealt with huge amounts of data to make critical decisions over the
period of time. For many years energy companies had invested in seismic software, data
visualization and other digital tools & technologies for planning and optimization purposes. But
now a day, most of the enterprises have started craving a certain desire for better execution of E&P
activities. Since the crude oil prices have gone down significantly over the past few years, which
have effectively brought down the profit margins. Also as world looks towards renewable resources
of energy, companies are supposed to act in more efficient ways they ever had. This calls in for a
digital transformation within the companies enabled by virtual integration through IT. This will of
course require a lot of data gathering and analytics over it to drive the organizations towards success
which will eventually justify investments and efforts made for it. As we know how big E&P
operations are and at the scale they are carried out, there will be huge data points involved and data
gathered will eventually lead into the realm of "Big Data". According to a report from Bain &
Company production can be improved by up to 6–8 % with the implementation of Big Data
Analytics. But first of all let's define Big Data acutely. 2. What is Big Data? Off late there has been a
lot of buzz going around this term "Big

The As A Progressive Series Of Our Time
Bones can be considered a progressive series of our time. It is a forensic anthropologist called
Dr.Temperance Brennan. She is a woman that solves murders with her team at the Jeffersonian. Her
partner is called Agent Booth from the FBI. The TV series can be described as being sexist yet
Misogyny. In this paper, I will deconstruct each character with sociological lenses. It will analyze
how each character reacts to others and events. It will demonstrate how the show has objectified
women and justified objection among strong female characters. The main character of the show,
Dr.Temperance Brennan, is represented as a profoundly intelligent and knowledgeable woman who
has a robot like and emotionless attitude throughout all the ... Show more content on
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She receives support from her co–workers and associates when she describes the issues affecting
women. For instance, in season 2 episode 13 a producer refers to Temperance as "a feminist
crusader out to ruin the all–American fun!" while getting close, she punches him and states "Self–
defense; he assaulted me." Sully, her colleague, responds "Yes, he did." Dr.Temperance Brennan
also argues on how producers view women as objects as seen in figure 3. She is also very
independent and makes it clear she does not belong to anyone (see Figure 4). Temperance is an
independent woman who makes it clear on several occasions that she challenges the gender norms.
The second main character is Seeley Booth a typical FBI agent. Booth embodies all norms of
masculinity to which men strive and does not challenge the hegemonic male. He is depicted as a
"tough guy." As James lull describes in "hegemony is the power or dominance that one social group
hold over others"(Lull,1995). Booth is male dominance over women, and his "gut" holds more
valued than Dr.Temperance Brennan countless degrees and years of schooling. In the show, he has
"saved the day" on many occasions. Seeley Booth is always seen as the "hero" of the show even if
Dr.Temperance Brennan did all the analytical work. By being the "hero," he is claiming his
dominance over Dr. Brennan. The voice of reason is always Agent Booth, despite the fact
Dr.Temperance Brennan is more qualified. Booth is

Nichols Company Casae Study
Nichols Company Case Study
OSC 301
Nichols Company Case Study
Joe Williams is the president of Nichols Company (NCO), which manufactures three primary
products and has over 355 employees. In addition, NCO has been having some issues with their
supply chain in the past few months and it has affected their customer service. This paper will
summarize the case study, determine NCO 's appropriate forecasting technique, discuss the impact
of aggregate planning, weigh NCO 's various cost factors associated with carrying inventory, and
make recommendations for improvement. Mr. Williams was approached by his Director of
Marketing, Mr. Barney Thompson, and announced that they had lost a large order due to a
backorder of tubing, which is ... Show more content on Helpwriting.net ...
Furthermore, aggregate planning affects NCO 's production schedule in much of the same way
because the aggregate plan defines the product production and duration.
There are various costs factors associated with carrying inventory. Chase, Jacobs, and Aquilano
(2003) stated that such costs include "storage facilities, handling, insurance, pilferage, breakage,
obsolescence, depreciation, taxes, and the opportunity cost of capital" (pg. 546). Furthermore, in
NCO 's case, they have an issue with carrying excessive inventory on products that do not get
immediately sold; consequently, they have an issue with alleviating the excess inventory. However,
high holding costs tend to favor low inventory levels and frequent replenishment.
NCO should make a few changes to improve their current supply chain troubles. They should
examine how NCO 's marketing department derives their forecasting numbers, and they should try
to run a more effective Just–in–Time (JIT) manufacturing process. First, NCO 's marketing section
should adopt a combination of the Time series analysis and Qualitative, Grass roots forecasting
techniques. If they use historical data along with current market trend inputs from NCO 's sales
force, they will be able to prepare a more accurate forecast. Second, NCO should redesign their
current manufacturing processes and utilize JIT manufacturing principles. Introducing these new
processes should help NCO to produce the correct

Training Process in Sas
Onsite training
SAS Onsite Training Service provides you the opportunity to bring SAS software training directly to
your organization. With education experts from SAS, you can train your employees in a familiar
environment, saving your money for training instead of travel.
On–site training is designed specifically for your organizations when you need: * group SAS
software training * flexible training schedules * lower employee travel costs
On–site courses combine lectures, software demonstrations, hands–on computer workshops, and
course notes that result in the best learning experience possible. In addition, we will provide a copy
of the course notes to each attendee.
Data Manager * SAS Programming Introduction: Basic ... Show more content on Helpwriting.net ...
ration for Data Mining DMDP * Exploratory Analysis for Large and Complex Problems BEAP *
Managing SAS Analytical Models Using SAS Model Manager MMUS
Forecaster
* Business Forecasting Using SAS: A Point–and–Click Approach FETS * Forecasting Using SAS
Software: A Programming Approach FETSP * Forecasting Using SAS Forecast Server Software
FSTU21 * Using SAS High–Performance Forecasting Software HPF * Modeling Trend, Cycles, and
Seasonality in Time Series Data Using PROC UCM LWBARS * Stationarity Testing and Other
Time Series Topics LWBADD
Market Research * Applied Clustering Techniques CLUS92 * Design of Experiments for Direct
Marketing DOEF92
Operations Researcher * Building and Solving Optimization Models with SAS/OR OROP92 *
Statistical Process Control Using SAS/QC Software SPCQC9
JMP Analyst * JMP Software: Statistical Data Exploration JDEX7 * JMP Software: ANOVA and
Regression JANR7 * JMP Software: Classic Design of Experiments JDRS7 * JMP Software:
Custom Design of Experiments JMDOE7 * JMP Software: Analysis of Dose–Response Curves with
JMP JDOSE7 * JMP Software: Introduction to Categorical Data Analysis JCAT7 * JMP Software:
Statistical Quality Control JSQC7 * Mixture Design of Experiments Using JMP BJMX7
–––––––––––––––––––––––––––––––––––––––––––––––––
SAS Solution Lines
Activity–Based Management * Activity–Based Management Concepts * ABC Modeling Using SAS
Activity–Based

Time Management Is The Predictable Control And Individual...
Time Management is the predictable control and individual can exercise over a series of events
(Tracy, B (2014). Paragraph 5). A number of men and women, even some of the highest–performing
professionals in every field, consistently undervalue and under–appreciate what can actually be
accomplished in 10 or 15 minutes of uninterrupted work. You might not be able to finish a big
presentation, but you could take the first steps, like making an outline of the first few slides, sending
an e–mail information request, or writing an introduction.
Life is full of opportunities to get a few minutes of productivity in, rather than feeling bored or
wasting time, if we only take advantage of them. The key is to find and recognize them, while
breaking some of our worst habits at the same time. Have you ever thought about how much time
you spend doing nothing during an average day? Usually this is not a situation where you planned to
do nothing...it just happened. Think about all of the things you could accomplish if you could make
use of this time (Tracy, B. (2014) Paragraph 5). For instance:
Time you spend commuting on a train or bus
Time you wait at the doctor or dentist office for your appointment
Time you spend on a plane, waiting for your plane, or the time you spend waiting for your baggage
Time you spend "on hold" on the telephone
Time you spend when you arrive at work or at a meeting earlier than you had anticipated
If you can manage your time more effectively, you

Ratio Analysis: Understanding The Nature Of Ratio Analysis
To understand the information contained in financial statements with a view to know the strength or
weaknesses of the firm and to make forecast about the future prospects of the firm and thereby
enabling the financial analyst to take different decisions regarding the operations of the firm.
RATIO ANALYSIS:
There are various methods or techniques used in analyzing financial statements, such as
comparative statement, trend analysis, common– size statement, schedule of changes in working
capital, fund flow analysis, cost – volume profit analysis. The ratio analysis is one of the most
powerful tools of financial analysis. It is the process of establishing and interpreting various ratios
(quantitative relationship between figures and groups of ... Show more content on Helpwriting.net ...
12,00,000 & credit sales are Rs. 30,00,000. so the ratio of credit sales to cash sales can be described
as 2.5 [30,00,000/12,00,000] or simply by saying that the credit sales are 2.5 times that of cash
sales.
C] As a percentage: In such a case, one item may be expressed as a percentage of some other items.
For example, net sales of the firm are Rs.50,00,000 & the amount of the gross profit is Rs.
10,00,000, then the gross profit may be described as 20% of sales [ 10,00,000/50,00,000]
NATURE OF RATIO ANALYSIS:
Ratio analysis is a technique of analysis and interpretation of financial statements. However ratio
analysis is not an end in itself. It is only a mean of better understanding of financial strengths and
weaknesses of a firm. Calculation of mere ratio does not serve any purpose, unless several
appropriate ratios are analyzed and interpreted. There are number of ratios which can be calculated
from the information given in the financial, statements, but the analyst has to select the appropriate
data and calculate only a few appropriate ratios from the same keeping in mind the objective of
analysis.
STEPS INVOLVED IN RATIO ANALYSIS:
1) Selection of relevant data from the financial statement depending upon the objective of the

Demand & Inventory Management
Forecasting demand and inventory management using Bayesian time series
T.A. Spedding University of Greenwich, Chatham Maritime, Kent, UK K.K. Chan Nanyang
Technological University, Singapore
Batch production, Demand, Forecasting, Inventory management, Bayesian statistics, Time series
Keywords
Introduction
A typical scenario in a manufacturing company in Singapore is one in which all the strategic
decisions, including forecasting of future demand, are provided by an overseas office. The forecast
model provided by the overseas office is often inaccurate because the forecasting is performed
before the actual production schedule and it is based on marketing survey results and historical data
from an overseas research team. This ... Show more content on Helpwriting.net ...
Bayesian dynamics time series and forecasting techniques can be used to solve inventory problems
because Bayesian inference statistics has the analogue idea that posterior knowledge (actual sales
demand) can be derived from prior knowledge (such as the manager's experience) and the likelihood
(the similar or expected trend) of the product demand (Box and Tioa, 1973; Jeffreys, 1961; Lee,
1988; Press, 1989). In many real life forecasting problems (for example when previous demand data
are not available for newly launched products), there is little or no useful information
This work was carried out while the author was Associate Professor in the School of Mechanical
and Production Engineering at Nanyang Technical University in Singapore.
Integrated Manufacturing Systems 11/5 [2000] 331±339 # MCB University Press [ISSN 0957–
6061]
[ 331 ]
T.A. Spedding and K.K. Chan Forecasting demand and inventory management using Bayesian
time series Integrated Manufacturing Systems 11/5 [2000] 331±339
available at the time when the initial forecast is required. Hence, the early forecast must be based
largely on subjective considerations (such as the manager's experience and the general demand of a

similar or comparable product). As the latest information (actual sales demand) becomes available,
the forecasting model is modified with the subjective estimation in the presence of the actual data.
This

Production Planning Report
Production Planning
Introduction
The intention of this project is to demonstrate the function of production planning in a non –
artificial environment. Through this simulation we are able to forecast, with a degree of certainty the
monthly requirements for end products, subassemblies, parts and raw materials. We are supplied
with information that we are to base our decisions on. The manufacturer depicted in this simulation
was actually a General Electric facility that produced black and white television sets Syracuse, New
York. Unfortunately this plant is no longer operational, it was closed down and the equipment was
shipped off to China. One can only wonder if the plant manager would have taken Professor Moily's
class in ... Show more content on Helpwriting.net ...
Next we calculate the labor that goes into transforming these parts into a viable end product. We get
a total of six hours of running man hours/unit and an hourly labor rate of $8.50, which gives us a
total of fifty–one dollars. This gives a minimal total cost of
$101 to produce product one. This number is useful in determining how much a unit actually cost to
manufacture and what we must minimally sell the product for to make a profit. We can than analyze
if a product costs to much to make or the sum of the parts is more than the price of the end product.
Product eight had the lowest direct minimum cost ($89.50) and four had the highest minimal direct
cost. From a purely economic stand point, it would be beneficial to use as much of raw material
twenty–three ($5 unit) and as little of raw material twenty–two ($30 unit). This does not consider
that raw material twenty–two may actually be more valuable than raw material twenty–three.
Perhaps raw material twenty two may be gold or silver and raw material twenty–three may be sand
or glass. I also converted all information in the sales history per month (figure four of the
MANMAN packet). The purpose of this step was so that I could sort and add the sales numbers to
chronicle the past twenty four months. Clearly product one was the best–selling apparatus, and
product three, four and five where sales laggards. Entering the information into spreadsheet form
was also necessary to

What Is The Examination Of Variables Of Interest And...
1. The research designed used in this article is the descriptive research design. The study involves
the examination of variables of interest and conducting trend analysis.
2. The data type used to conduct the analysis is qualitative. This article consisted of both dependent
and independent variables. The dependent variables consisted of the data frequency of individual
migrant deaths that were gathered from multiple sources. The independent variables helped to
account for the changes in the volume of illegal immigration over time.
3. The sampling procedure that was applied to this study involved using the control area and the
buffer area. The control area is used to determine whether other similarly situated sectors that were
not ... Show more content on Helpwriting.net ...
The effects of the LRP and BORSTAR were not large enough to have an impact on the overall rate
of migrant deaths in the time series analysis. Both the BORSTAR and the LRP seem to have been
successful in saving migrant lives.
6. Limitations of the study include threats to the validity and reliability because I believe there was a
lack of recorded series of migrant death data which limits more complete understanding of BSI
impact. The impact analysis of the BSI program was then forced to rely on two separate sources of
data. You should always use caution when interpreting results from a single analysis of data that has
been collected from two separate collection processes.
Article 2:
Kovandzic, T., Sloan, J., and Vieraitis, L. (2004. "'Striking Out' as Crime Reduction Policy: The
Impact of 'Three Strikes' Laws on Crime Rates in U.S. Cities." Justice Quarterly, 21(2): 207–239.
1. The research designed used in this study, I believe was the casual design study because this
research is used to measure the impact a specific change will have on an already existing
assumption. This study also uses the multiple time–series design. The multiple time–series design is
also considered one of the strongest quasi–experimental research designs used for assessing impact
of criminal justice policy when more thorough experimental control is not

Time Series : A Data Measured With The Passage Of Time
Time Series it is a collection of data measured with the passage of time. Examples of time series
stand out in a number of areas, ranging from engineering to economics. The analysis of time series
data constitutes an important area of statistics. A time series is a sequential set of data points,
measured typically over successive times. It is mathematically it is defined as a group of vectors x
(t), t = 0, 1, 2, where t represents the time elapsed [John H. Cochrane,1997]. The variable x t is
treated as a random variable. The measurements taken during an event in a time series are arranged
in a proper chronological order. A time series containing records of a single variable is termed as
invert. But if records of more than one variable are considered, it is termed as multivariate. A time
series can be continuous or discrete. In a continuous time series notes are measured in each case of
time, whereas a discrete time series includes observations measured at discrete points of time. For
example, temperature readings, flow of a river, concentration of a chemical process etc. can record
as a continuous time series. On the other hand population of a particular city, production of a
company, exchange rates between two different currencies may represent discrete time series.
Usually in a discrete time series the consecutive observations are recorded at evenly spaced time
intervals such as every hour, daily and weekly, monthly or yearly time separations. [K.W. Hipel,
1994], the

Statistics Purpose Statement
Statement of Purpose
Junru Xia
I first discovered my interest in statistics in my sophomore year at the University of California,
Berkeley. In a statistics lecture, the professor explained how statistics can be applied in real life by
giving an example that Amazon often promotes products by emailing its customers and
recommending products according to sophisticated analysis of customers' search records, as well as
their transaction data. Previously having been immersed in the world of abstract statistics, I realized
that in addition to being an essential tool to confirm theories already proposed, data itself can lead to
entirely new ideas. The data patterns can be interpreted from many different perspectives, which
triggered my eagerness to explore the stories hidden in data and improve my understanding of the
world through data analysis.
With a strong urge to enhance my quantitative foundation, I finished all my courses in mathematics
with high scores, and learned Python and Data Scientists' Toolbox through Codecademy and ...
Show more content on Helpwriting.net ...
My job in the personal loan center was to assist in maintaining the database and generating
evaluations. One project which I was involved in was using personal credit scoring model to make a
risk forecast and detect bank fraud based on loan documentation in the database. During the project,
I realized that rather than just about complicated methods, statistics was about simplifying and
making sense. Sufficient data and in–depth analysis can not only help companies make valuable
decisions, but also enable us to understand real life in a more direct and precise way. As the trend of
information explosion is inevitable, I am sure that the power of data will become increasingly
overwhelming, and the role of data analysis will be indispensable. Therefore, along with my goal to
pursue graduate studies in statistics, my desire to pursue a career in the field of data analysis was

The Time Series By Patricia March
SBA Task 3
Conclusive Research Essay
Chanelle Botha 12k
An investigation on the use of ink and charcoal as a medium, as well as movement over time, in the
Time Series by Patricia March.
The works of Patricia March are predominantly charcoal works, with smaller aspects of ink, whilst
portraying insight of character, as well as how the body moves over a period of time. March uses the
perception of a figure moving, portraying the figure on one picture plane; but moving, as if this
were over a period of time. Figures appear walking or picking up an object and this is portrayed as
one character becoming three. In each different movement the figure is in a different position, as if
moving like in a real life situation. This reinforces the name of her series, Time, as it literally
perceives a figure or a few figures and their movements over a period of time. March studied a
master in cinematography. She was interested in movement and time, so since, she tried to apply her
vision about cinema into drawings. The movement of the people drawn, reminds me her some way
to profundity of the human form ad how it ... Show more content on Helpwriting.net ...
The scope of the creative experience was influenced mainly by Patricia March as her ideas
influenced the idea of charcoal and ink drawings, as well as left–hand techniques. Her works
allowed the exploration of new mediums and techniques, of which I had not used before. March also
played a big role in influencing visual diary work and the direction of which I took when
researching other artists.
It is seen in resolution, that Patricia March, as well as her Time Series, created in 2011, have been a
great influence, when involving aspects such as similarities, differences, technique, medium, the
depth and scope pf creative process, on the creation of my own Self and Other year work

The Relationship Between Bivariate Correlations And Linear...
The data has been graphed in previous studies (Lauristen & Hiemer, 2009); however, authors have
stated that future research will benefit from tests of statistical significance (Lauristen & Hiemer,
2009). Bivariate correlations and linear regression are common methods of estimating association
between variables, linear trend (slope), and statistical significance in time series data (Baumer &
Lauristen, 2010; Hashima & Finkelhor, 1999; Lauristen, Rezey & Heimer, 2013).
The many caveats associated with time series data, trend analysis, and linear regression were
accounted for preliminary to analysis. It is common for time series data to be highly auto–correlated
and thus contain residuals lacking independence (Asteriou & Hall, 2011). The Durbin–Watson
statistic was used to confirm residual correlation was not problematic for reported findings (Fields,
2005). Data normality, linearity, homoscedasticity, and presence of outliers were assessed by
evaluation of skewness and kurtosis, the Shapiro–Wilks test, and visual inspection of plots (West,
Finch, & Curran ,1995).
Multicolinearity, often difficult to overcome in time–series data (Asteriou & Hall, 2011), was
assessed through insuring bivariate correlations of independent variables were less than .9 in
strength, variance inflation factor values did not exceed 10, and tolerance levels were above .10
(Tabachnick & Fidell, 2001). Spurious regression is possible with non–stationary data or when two
variables are trending overtime.

Data And Data Source Analysis
4. Data source analysis
Data is one of the important factors in data forecasting studies because data represents the whole
source of the business purpose of the study.
There are several reasons that the difference of data source makes it hard to compare prediction
accuracy from each other.
First, the result of a prediction model may differ with different data sources. Theoretically, the more
data we test, the more accurate result we can get, however, in real–world, it is often hard to collect
as much data as desired. So a potential difficult question for all data prediction studies is that how
much data is enough.
Second, the quality of data source is crucial for prediction studies. Apparently, false data or noisy
data is not useful for ... Show more content on Helpwriting.net ...
With the similar target, Gordiievych and Shubin (2015) did not give any description of their data.
4.1.2. Different range and size of data
For airline prices prediction studies, it is a common practice to use time series data like airline ticket
prices. The date ranges of data used by different studies vary from several months to as long as 18
years.
For example, Chen et al. (2015) used 110 days of data in their study, and the other study from Zhang
et al. (2010) used 18 years of data to perform the experiment. Some studies did not specify their date
range of the data explicitly, such as Wohlfarth et al. (2011), Gordiievych and Shubin (2015) and
Cao, Ding, He, and Zhang (2010). Other studies chose other data lengths. Laik, Choy, and Sen
(2014) used one year of data, as the same length as Liu, Tan, and Zhou (2016). Yuan, Xu, and Yang
(2014), Ghomi and Forghani (2016), and An et al. (2016) used three years, six years, and ten years
respectively.
One reason these studies chose different ranges of data for analysis is very likely the fact that data
collection for a long time range is difficult. Those studies used data ranges longer than a year were
mostly using historical data either from proprietary

Exchange Rate Volatility Measure And Relative Price
3.2 Exchange Rate Volatility measure and relative price An important issue in this topic is how to
choose the appropriate technique to estimate the exchange rate volatility. However, wide variety of
measures have been discussing in the literature, but there is no right or wrong measure of exchange
rate volatility. Mckenzie (1999) provides a brief over–view of different methods to measure
exchange rate volatility, such average absolute difference between the previous forward and current
spot rate, variance of the spot exchange rate around its trend, absolute percentage change of the
exchange rate and the moving average of the standard deviation of the exchange rate. A moving
standard deviation of nominal or real exchange rate seems to be the most commonly used method in
the empirical literature. Hence, we will construct the moving average standard deviation of the
monthly real exchange rate volatility with the same spirit as Serenis and Tsounis (2014) and a
moving standard deviation of real exchange rate can be expressed as:
Where R_t is logarithm of nominal or real exchange rate and m is the number of periods which can
be range from 4 to 12.In this paper, we will use the moving average of the standard deviation of
exchange rate as the measure of exchange rate volatility by using the real exchange rate and the
order m is set to be 12. Koray and Lastrepes (1989) have shown that the moving average of the
standard deviation of the exchange rate captures the variation in the

New Zealand Is An Export Driven Competitive Economy Essay
Problem statement
New Zealand is an export–driven competitive economy with exports accounting for about 30% of
GDP, one of the primary indicators used to gauge the health of a country 's economy. Auckland
Airport handles 85% of air cargo exported from New Zealand, making it a hub for businesses which
conduct trade.
These businesses want to know whether New Zealand's economy is continuing to benefit from these
exports. I predict that this is true due to the high OEC ranking of New Zealand, which indicated
economic complexity. The question that must then be posed is: What is the long term trend in
exports from Auckland Airport? This question is of interest to businesses and consumers alike
because international trade affects the price of domestic goods due to changes in supply and
demand.
An investigation will be carried out into the export of goods coming from Auckland Airport to
investigate and analyse trends and to make a forecast. Further investigation into both imports and
exports were also conducted to gauge the economic situation of New Zealand. The data provided is
sourced from Statistics New Zealand Infoshare, recorded from 1988 to 2015 and records quarterly
gross weight of exports in tonnes.
Plan
Using the data provided, I will use NZ Grapher to plot a time series and a decomposition on
Auckland Airport Exports from 1988 to 2015. The data will be interpreted taking into account the
raw data, trend, residuals, and seasonal effect. A forecast will be made using the

A Concise Statistical Analysis Report
Introduction As an Operations Manager we have been tasked with submitting a forecasting report
that shows the sales of the number of transformers required to produce voltage regulators. For the
purpose of explanation, these voltage regulators main purpose is to protect refrigerators from power
surges and electrical catastrophes. Throughout the course of this paper elements will be strategically
place to develop a concise statistical analysis report to cover the following: Any quantifiable factors
that may be affecting the performance of operational processes. An explanation of how these
quantifiable factors may be affecting the operational processes. What is the history and problem?
And finally who are the key internal and external ... Show more content on Helpwriting.net ...
As sales began to plunge, leaders began to reevaluate the policy in regards to stocking spares and
components in its factory store. The thought of retaining a massive stock of said parts was
debatable. Although it was, in essence, necessary to retain some parts for the transformer
management began to contemplate just how much enough was. Another issue that surfaced was the
price consistency. A–Cat Corp essentially had only one supplier. So with such minute sources there
is a lots of room for suppliers to raise prices which in turn places a strain on the business. Although
A–Cat Corp is still generating consistent revenue growth as far a profit, sales have been faltering in
relation to competitors. The original method for forecasting how many transformers they will need
to meet said demand was to examine the sales figures of the preceding months as well as the
previous two years around the same time and they would hypothesize how many transformer they
would need. Although this method proved plausible in previous testing phases there have been
instances of under or over stocking. Supplier issues are also beginning to heightened concerns in
regards to inconsistent ordering approaches. Ratnaparkhi, Head of Operations, has been asked to
develop an analysis of the data submitted to him and to present a report with

Financial Forecasting
Time Series Models for Forecasting New One–Family Houses Sold in the United States
Introduction
The economic recession felt in the United States since the collapse of the housing market in 2007
can be seen by various trends in the housing market. This collapse claimed some of the largest
financial institutions in the U.S. such as Bear Sterns and Lehman Brothers, as they held over–
leveraged positions in the mortgage backed securities market. Credit became widely available to
unqualified borrowers during the nineties and the early part of the next decade which caused
bankers to act predatorily in their lending practices, as they could easily sell and package subprime
mortgage loans on leverage. This act caused a bubble that would later ... Show more content on
Helpwriting.net ...
Figure 2
12–period plot of autocorrelation functions (ACF) for NHS
Now that we have verified the presense of a trend in the data we will look to verify the seasonality
we saw earlier represented by regularly reoccurring fluctuations in the levels of data in accordance
with the calendar seasons. To do this we will use an autocorrelation function for the first differenced
new home sales data. We will use a larger sample, in this case 24 months, so that we can see the
regularly reoccurring fluctuations from one year to the next. When we look at the graph in Figure 3
we notice great increases with lag 12 and lag 24. The jumps seen in lags 12 and 24 confirms the
presense of seasonality as they are above the upper limit representing statistical significance.
Figure 3
24–period plot of autocorrelation functions (ACF) for first differenced NHS
Time Series and Regression Models for New One–Family Houses Sold
Since the NHS data has been shown to have trend and seasonality we will evaluate the data using
four different time series models and compare the results of each to see which model is the most
accurate. The models we are going to use are the Modified Naïve model, Winters Exponential
Smoothing model, Time Series Decomposition, and Autoregressive Integrated Moving Average
(ARIMA).
We will also test a multiple regression model to attempt to forecast future NHS, while taking

Relevance And Implications Of Forecasting Retail Deposits
Relevance and Implications of Forecasting Retail Deposits – Philosophy of Forecasting
By: Mihir Tamhankar
Our project is on forecasting retail deposits using macroeconomic drivers. In this project, we aim to
find the most important macroeconomic parameters which have an effect on the deposits which
consumers like you and me like to maintain in banks. This is to be done by extensive data analysis
and statistical tests. Then we would build models for accurately predicting future deposits given the
macroeconomic environment. This effort is for Nomis Solutions for them to incorporate the findings
in their software for banks.
This paper is part of a group effort to analyse and discuss the relevance and implications of deposit
forecasting. ... Show more content on Helpwriting.net ...
Thus, all in all, this paper supplements the idea of providing a reasonable overview of the
implications of forecasting retail deposits in terms of its partition based on social scientific aspects.
Merriam Webster Dictionary online (2015) recognizes the word forecast both as a noun and as a
verb. As a verb it defines it as "to say that something will happen in future" while as a noun it
definition is: "a statement about what you think is going to happen in future". In this project we will
be using the word in the sense of a verb. However, the definition is very strong because of the
implicit notion of the assertion of occurrence of an event with a certain confidence even without
knowledge of the future events and the underlying uncertainty. Moreover, it is equally applicable to
any kind of entity: deposits, weather, market demand or even human life. In fact forecasting is such
a fundamental component of human life that we forecast about self and others all the time. Probably,
it stems from the innate human desire to know all the unknown, especially his future. Consequently,
more and more techniques of forecasting have been developed– from intuition to rigorous complex
mathematical analysis.
So why is forecasting important and what happens if

Forecasting Model Of Forecasting Models
Forecasting is often defined as the estimation of the value of a variable (or set of variables) at some
future point in time (Goodier, 2010). It can be applied to a number of different situations when there
is uncertainty about the future and the data collected can aid in decisions that need to be made
(Armstrong, 2001). In relation to healthcare, forecasting models have been used to aid their sector's
departments to plan staff rota schedules, ensuring that a sufficient amount of senior staff are
available at any given time throughout the day, week, month and year. As explained previously, a
fundamental factor that causes overcrowding is a limited supply of resources to treat patients,
leading to a longer time spent in an Emergency ... Show more content on Helpwriting.net ...
These models can be characterised as consisting of a time trend, a seasonal factor, a cyclical element
and an error term (Kennedy, 2008.) Unlike casual or economic forecasting, where it is assumed
there is a historical relationship between a dependent and an independent variable will be consistent
in the future, time series models assume the historical components of the model will repeat itself.
Research has been undertaken to develop a generalised forecasting model that uses a method that
can accurately predict future the attendees and resources needed at Emergency Departments.
1.3.3 Long Range Forecasting for Future Attendees An early attempt to predict attendees was
conducted by Milner (1988) who's study on a single Emergency Department within the UK
attempted support to healthcare planning by forecasting annual first, return and total attendances at
EDs for Trent districts and the whole of the Trent region. The data of annual first, return and total
attendances were collected over a training period of 10 years and evaluated over a period of 1 year
using an Autoregressive Integrated Moving Average (ARIMA) method for modelling which falls
into time series model category. This method for forecasting this type of data has been supported by
other researchers, who state that ARIMA forecasting techniques should be considered for a time
series that's contains a trend or seasonal or non–stationary data. The results

The Science Of Data Mining
ABSTRACT
Many real life applications require the ability to decide whether a new set of observation is similar
to the same distribution over a time series or not. It is considered for many application domains as a
milestone and a watershed to their decision making process. Business and research sectors such as
medical, financial, IT, cyber security and even crime investigation and terrorism are interested to
invest in this field to have the ability for real time detection of unusual behavior.
We are living in an era were we have zillions of data streams that need to be captured, analysed and
studied to have more knowledge on different aspects of life and their effect on each other. These
data streams are collected and recorded over ... Show more content on Helpwriting.net ...
Real time anomaly detection in streaming data is something valuable in many domains, especially in
environments where there are sensors that produce data streams changing over time. There are
various existing anomaly detection techniques that are developed and experimented across different
industries.. The motivation for partitioning time series into similar motifs is to give better
understanding of the data characteristics.
In this study we will provide state– of–the–art review in the area of anomaly detection based on
non–parametric techniques and will assess different existing techniques and introduce a novel
methodology for anomaly detection using dynamic evolving subsequence clustering.
INTRODUCTION
Time series is a very important factor in business today. Organizations always depend on forecasting
methods for their management decisions. The methodology itself depends on the availably of the
required data and accordingly a judgmental or statistical approach is chosen. Almost every
functional area of the organization makes use of the forecasting, for example financial experts use
forecasting for cash flow analysis, stock price fluctuations and companies' valuations. Also
personnel departments depend on forecast for their recruitment plans. Logistics and supply chain
forecast their inventory levels and their supply and demand. Moreover, there is a huge demand to
utilize time series data in

Different Aspects Of Time Series Design
The objective of this report is to describe different aspects of Time Series Designs which include the
purpose, phase, and data interpretation through the utilization of graphs. Further, two models, the
Multiple–Baseline Design and the Alternating Treatment Design will be presented through an
overview, considerations and, the advantages and the disadvantages of each model. Finally, the
unique characteristics of the Time Series Design versus the Experimental and Predictive Designs
will be discussed in a short synopsis.
Principles of Time Series Designs
Purpose of Design
The purpose of time series design is simple, yet its complexity can be perplexing due to a series of
essential factors necessary which ensure the study adheres to certain ... Show more content on
Helpwriting.net ...
However, Renfro–Michelin and his colleagues posit that to ensure a reliable and valid behavioral
change, the study must follow three guiding principles such as prediction, verification and
replication and focus on the dependent and independent variables (2010). In the case of the group
being studied for behavioral changes, the dependent variable focuses on the groups eating habits and
the independent variable is the actual treatment that impacts the dependent variable. To further
target the issue of the dependent variable, the researcher can monitor the variable in terms of
duration, latency, and ratio (Renfro–Michel et al., 2010). For instance, how many words can a
student type while he is in class? How long does it take for him to type those words? In terms of
latency, the researcher can focus on diabetics' initial dose of insulin and when the patient's
symptoms begin to subside or glucose levels begin to level off. Finally, while the dependent variable
or, the targeted behavior can be measured in different manners, measurements in terms of
percentage appear more concrete.
Phases of Time Series Designs
Base–line phase. The baseline phase in time series design is the measurement standard against
which subsequent fluctuations or adjustments will impact the study; baseline can be shown in a
graph

Information Technology : A New Generation Of Sql
I. Introduction
Information technology continues to revolutionize the interactions of mankind in various ways,
through social media, business, education and other channels. The internet has made it possible to
transmit large data across many networks. These networks have made it possible to store, access and
query billion of data from large databases. Innovation has given rise to special language used to
manage and access all sorts of information within various databases know as SQL. Recently a new
generation of SQL known as NoSQL has been developed. NoSQL store related data in JSON–like,
name–value documents and can store data without specifying a schema. One such type of NoSQL
database that has been developed is the IBM Informix ... Show more content on Helpwriting.net ...
Many organizations use the Informix database capability including DHL and CISCO.
III. NoSQL capability
IBM Informix provides the following NoSQL dimensions (IBM Informix Simply powerful):
Application development flexibility
JavaScript Object Notation (JSON) documents are a fully–supported data type. IBM Informix
provides a rich set of APIs for storing, manipulating and retrieving JSON documents, accessible
from a wide range of programming environments.
All of the enterprise–level capabilities of IBM Informix can be applied to the JSON document
stores, including compression, replication and high availability, transactional consistency, multi–
node scalability and more.
Web developers can now access data without having to write SQL. But that 's not all – the
traditional SQL model can still be applied when needed, such as mission–critical transactional
workloads. The two methodologies
Hybrid automated decision making supports SQL and NoSQL
IBM Informix determines if you are dealing with a JSON Collection or a SQL tables and processes
the operations appropriately. Thus IBM Informix's ability to access JSON documents and/or SQL
tables within the popular MongoDB APIs provides the foundation for a single hybrid application to
span all of the enterprise data.
Enterprise level performance The IBM Informix NoSQL solution gives you ACID principles when
they are needed, along with

Marketing Research For An Auto Spare Parts Company Wants...
Executive Summary
Ted Ralley (Ted), Director of marketing research for an auto spare parts company wants to ensure
the highest level of accuracy for sales projections for the upcoming business year 2008. Ted is aware
that forecasting can be an expensive undertaking if results are inaccurate, as such he utilized the
most accessible work tool, Microsoft Excel time series forecasting method to run several forecasts
using the historical sales data from the previous four years. He was however tentative about the
results, as he is of the view that economic activity and oil prices plays a significant role in auto parts
sales. To test his theory he has decided to generate additional forecasts using econometric variables.
His forecast decisions ... Show more content on Helpwriting.net ...
The report further stated that industry revenue fell during the recession, but has risen in subsequent
years, as growth in the national level of per capita disposable income and corporate profit aided
increased consumer and business spending on auto parts. Director of marketing research for a large
manufacturing company of auto parts, Ted Ralley is tasked with predicting quarterly sales for 2008.
Aware of the cost to the company if an inaccurate forecast is made, Ted is keen on providing the
most accurate predictions. He believes that econometric variables such as oil prices and economic
activities have positive impact auto parts sales, and is of the opinion that these variables are better
indicators of future sales. Historical data were examined to determine whether economic activity
and oil prices have any effect on auto parts sales, and to verify if these factors are in fact better
predictors of auto parts sales. The interpretation of these results will guide the direction of the
company in the next ensuing business year.
Problem
Are economic activity and oil prices better predictors of auto parts sales?
Analysis
The historical auto parts sales data were analyzed using Excel Data Analysis to help predict the
future of auto parts sales, by observing trends and pattern. A line

Strengths And Weaknesses Of Ratio Analysis
The Study of the ratio analysis technique to financial statements offers potential in expanding
insight into specific strengths and weaknesses of a company financial situation. The primary
objective of financial analysis is to provide information useful for decision making.
1.1 INTRODUCTION: RATIO ANALYSIS:
There are various methods or techniques used in analyzing financial statements, such as
comparative statement, trend analysis, common– size statement, schedule of changes in working
capital, fund flow analysis, cost – volume profit analysis. The ratio analysis is one of the most
powerful tools of financial analysis. It is the process of establishing and interpreting various ratios
(quantitative relationship between figures and groups ... Show more content on Helpwriting.net ...
12,00,000 & credit sales are Rs. 30,00,000. so the ratio of credit sales to cash sales can be described
as 2.5 [30,00,000/12,00,000] or simply by saying that the credit sales are 2.5 times that of cash
sales.
C] As a percentage: In such a case, one item may be expressed as a percentage of some other item.
For example, net sales of the firm are Rs.50,00,000 & the amount of the gross profit is Rs.
10,00,000, then the gross profit may be described as 20% of sales [ 10,00,000/50,00,000]
NATURE OF RATIO ANALYSIS:
Ratio analysis is a technique of analysis and interpretation of financial statements. However ratio
analysis is not an end in itself. It is only a mean of better understanding of financial strengths and
weaknesses of a firm. Calculation of mere ratio does not serve any purpose, unless several
appropriate ratios are analyzed and interpreted. There are number of ratios which can be calculated
from the information given in the financial, statements, but the analyst has to select the appropriate
data and calculate only a few appropriate ratios from the same keeping in mind the objective of
analysis.
STEPS INVOLVED IN RATIO ANALYSIS:
1) Selection of relevant data from the financial statement depending upon the objective of the
analysis.
2) Calculation of appropriate ratios from the above

Comparison and Contrast of Forecast Methods
MGT 554
Operations Management
University of Phoenix
Professor Leonard Enger
May 1, 2006
TABLE OF CONTENT
Cover Page .1
Table of Contents ...2
Seasonal Forecasting ..3
Delphi Method 4
Technological Method 5
Time–series forecasting ...6
Company Forecasting Methods ..7
Conclusion ..8
References ..9
There are several different methods that can be used to create a forecast, this paper will compare and
contrast the Seasonal, Delphi, Technological and Time Series method of forecasting. Factors to ...
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http://www.ryerson.ca/~mjoppe/ResearchProcess/841TheDelphiMethod.htm
Technological Method
The Technological Forecasting method is used to analyze the market for the life span of an existing
technology to determine if its close to end of like and to see if a new product or technology is ready
to enter an existing market. It is also used to identify competing new technology and to forecast
sales. Before a new innovative product enters into the market Technology Forecasting is one of
several methods used to determine if customers will buy it. The Technology method should always
be used in conjunction with other tools to identify prospective customers, prototypes, focus groups,
interviews, market testing, internet polls and other tools to get a better understanding of the market.

The major techniques for technological forecasting is numeric data and judgmental. Numeric data–
based forecasting extrapolates history by generating statistical fits to historical data. Judgmental
forecasting can also be based on past projection but like the Delphi method it relies on the
subjective judgment of experts. Keep in mind that technological forecasting is most appropriately
applied to capabilities, not to the specific characteristics of specific devices. Other Numeric data
techniques are Trend Extrapolation, Qualitative Approaches, Growth Curves, Envelop Curves and
Substitution models. Techniques used by Judgment–Base method are Monitoring, Network
Analysis,

Forecasting : Assessment Of Forecasting
Assessment of Forecasting Forecasting is a method of extrapolation of quantitative and qualitative
data to predict future requirements. Qualitative forecasting is subjective, whereas quantitative
forecasting contains projection of historical data. Simply stated, forecasting is a technique utilized in
efforts to match supply with demand. Accurate forecasts are necessary throughout the supply chain
to guide decisions regarding operation activities. "Poor forecasting can result in poor inventory and
staffing decisions, resulting in part shortages, inadequate customer service, and many customer
complaints" (Collier & Evans, 2013, pg.227). Poor forecasting can also result in excess inventory
throughout the supply chain. Since forecasting is such an integeral component of the value chain, it
stands to reason that inadequate forecasting could be the basis for the various quality control issues
General Motors has experienced within its supply chain. In order to analyze forecasting errors and
accuracy, it is essential to understand the basic methods of forecasting. Forecasting methods can be
divided into two broad categories: qualitative and quantitative. The statistical forecasting method is
defined as a quantitative method, "catergorized as time–series methods, which extrapolate historical
time–series data, and regression methods, which extrapolate historical time–series data, but can also
include other potentially casual factors that influence the behavior of the time

The Correlation Between The Value Of Time Series Of...
Autocorrelation
Autocorrelation is defined as the correlation between the value of time series at a specific time and
previous values of the same series (Reference). In other words, with time series what happens in
time t contains information about what will happen at time t+1. Autocorrelation plots are a
commonly–used tool for checking randomness in a data set. This randomness is ascertained by
computing autocorrelations for data values at varying time lags. If random, such autocorrelations
should be near zero for any and all time–lag separations. If non–random, then one or more of the
autocorrelations will be significantly non–zero. The autocorrelation plots can provide answers to
questions such as are the data random? Is an observation related to an adjacent observation? Is the
observed time series white noise, sinusoidal or autoregressive? They help in understanding the
underlying relationship between the data points. The autocorrelation plots of 4 time series of heating
operating system are as follows :
a. Supply temperature setpoint :– The plot starts with a high correlation at lag 1 which is slightly
less than 1 and slowly declines. It continues to decrease until it becomes negative and starts showing
an increasing negative correlation. The decreasing autocorrelation is generally linear with little
noise. Such a pattern in the autocorrelation plot is a signature of "strong autocorrelation", which in
turn provides high predictability if modeled properly. b. System

Analyzing Market Trends And Developments

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