Week 5 DiscussionDiscussion Topic Overdue – Dec 31, 2021 12:59 AMDiscussion
The discussion assignment provides a forum for discussing relevant topics for this week based on the course competencies covered.
For this assignment, choose one of the following questions and post your initial response to the Discussion Area by the due date assigned.
To support your work, use your course and text readings and also use outside sources. As in all assignments, cite your sources in your work and provide references for the citations in APA format.
Start reviewing and responding to the postings of your classmates as early in the week as possible. Respond to at least two of your classmates. Participate in the discussion by asking a question, providing a statement of clarification, providing a point of view with a rationale, challenging an aspect of the discussion, or indicating a relationship between two or more lines of reasoning in the discussion. Complete your participation for this assignment by the end of the week.
Question One: Forecasting Models and Types of Data
There are different types of forecasting models that can be used in business research. Each model is suitable for a type of historical demand data. Some data may have a trend, may be without a trend, or may be seasonal.

How can trendless data be evaluated?
How does a trailing-moving average compare to a centered-moving average?
When should exponential smoothing be used for data? Explain with an example.
In exponential smoothing, what type of smoothing constant should be chosen for little smoothing compared with moderate smoothing?
Question Two: Research Process
The research process is a well-structured methodology that aids the manager to make an educated business decision. The most important element of this process is the source of data used. The better the data, the better the result. Data must come from a sample that is random and large enough.

What are the six stages in a research process?
Which stage is the most difficult to complete? Why?
Which stage is the most important? Why?
How important is it to have accurate data?
Justify your answers using examples and reasoning. Comment on the postings of at least two peers and whether you agree or disagree with their views.

Goodness-Of-Fit Tests

Download: Video Transcript (PDF 156.38KB) (media/transcripts/SU_W5L1.pdf?


Goodness-Of-Fit tests are considered to be tests that allow for the analysis of categorical data. For example, are all of the soft drinks of a

particular beverage company equally likely to sell? One key observation here is that we are no longer limited to comparing two values to

one another. Rather we can now look at a collection of statistics simultaneously. In the soft drinks example, if 500 people were to



purchase one of �ve soft drinks, we might hypothesize that there is no difference in the number of people who will order particular soft

drinks. With this logic, we would expect approximately 100 people to order each of the �ve available soft drinks.

If 375 people order the same soft drink, 75 more order the second soft drink, and the other 50 people are divided among the remaining

three soft drinks, we would probably suspect that the number of people purchasing each soft drink would not be the same for each soft
drink. However, how do we statistically determine this to be the case? In other words, how do we prove (statistically) that the rate at

which soft drinks are purchased is not the same for all of the �ve beverages?

While the data given in the preceding paragraph makes it seem fairly obvious that there are differences in the rates at which the soft

drinks are purchased, what if each of the �ve soft drinks was purchased by somewhere between 80 and 120 people (out of the 500 that

we are considering)? Would we still be able to conclude that there were differences in the numbers of people who ordered the particular

soft drinks? Goodness-Of-Fit tests allow us to draw statistically valid conclusions about such questions.

Goodness-Of-Fit tests are an example of nonparametric tests, which are tests that do not require the data to follow a distribution with

which we have experience working (like the normal, binomial, Poisson, Student’s t, etc.) These methods are particularly use for working

with data that are classi�ed or categorized in some way.


One common use of organizational data is the creation of forecasts of future demand from information about prior demand. For example,

if a shoe manufacturer has seen demand surge by 35% in each of the last four Augusts, due to parents purchasing new shoes for their

children to wear at the beginning of the school year, then the company may predict that demand for the coming August will surge by 35%.

While there is no guarantee that the future will be like the past, there are many environments in which the future does behave like the

The use of historical demand data to forecast future demand is accomplished through a set of techniques known as time series

forecasting. Time series forecasts tend to recognize and exploit four different properties that can be seen by looking at historical demand

data. These four properties are seasonality, cyclicality, trend, and irregular patterns.

Time series models can be either additive or multiplicative. In additive models, each component of the model is simply added to all of the

other components of the model, treating each component as if it independent of all other models. In multiplicative models, all factors are
multiplied by one another, meaning that each component impacts and is impacted by all of the other factors in the model.

There are three basic trend models that attempt to exploit patterns identi�ed in past demand data. These three models are known as the

linear trend model, the exponential trend model, and the quadratic trend model.

The success of any business depends on its future estimates. On the basis of these estimates, a businessperson plans for things such as

production, sales, and the additional funds involved in those things. Forecasting is a method of foretelling the course of business activity

based on the analysis of past and present data.

Additional Materials

View a Pdf Transcript of  Forecasting Methods  (media/week5/SUO_BUS3059%20W5%20L2%20Forecasting%20Methods.pdf?



Forecasting Methods
Time Series

Classification of Time Series Data

There are four major categories that can be used to classify time series data: trend, seasonal, irregular, and cycle. However, remember, time series
data might not have all these characteristics.
The following examples will help you identify these categories:

• Trend
Time series data shows a general pattern of change over the observed years. This is an example of time series data showing a trend. A trend is a
gradual movement over a period of years. Some trends are fairly predictable.

• Seasonal
Time series data shows a repetitive pattern of change within a year. This is an example of time series data showing a seasonal pattern. A seasonal
pattern is a cycle that repeats itself during a year. For example, a landscape business may see increased sales during the summer season compared to
that in the winter season.

• Irregular
Time series data shows a random disturbance that follows no pattern. This is an example of time series data showing an irregular pattern. Irregular
time series data has no real pattern. In order to make short-term forecasts with irregular time series data, moving averages can be used.

• Cycle
Time series data shows a repetitive pattern of change around the trend over several years. This is an example of time series data showing a cycle. A
cycle occurs over several years and is a movement around a trend. For example, there are peaks and troughs in the business cycle. The economy also
moves in cycles.

Two Types of Time Series Models

Let’s assume that Y denotes the time series variable, T denotes the trend, S denotes the seasonal component, C denotes the cycle, and I denotes the
irregular component. The two types of time series models can then be represented by the following two equations:

• Additive Model: Y = T + S + C+ I

This equation represents the additive model. This model assumes that all the categories of the time series are independent of one another.
Accordingly, it adds the values of the four categories (irregular, seasonal, cycle, and trend) to make a forecast. This model is useful in making
predictions about time series data that do not exhibit a trend. This model is adequate in the short run because the values of the four categories do not
change much. However, for observations over longer periods of time, the multiplicative model is preferred.

• Multiplicative Model: Y = T × S × C × I

This equation represents the multiplicative model. This model assumes that there is a multiplicative relationship between the four categories of time
series data. Accordingly, it multiplies the values of the four major categories (irregular, seasonal, cycle, and trend) to make a forecast. It is useful in
making predictions about time series data that exhibit a trend.

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Conducting a Business Research Project

As has been discussed throughout this course, the key purpose of a business research project is to

provide information for those who will make decisions in an organizational environment. With this in

mind, business research projects are only undertaken when a problem motivates the undertaking of

such a project.

Pilot testing of a research design will allow the organization to ensure that the data that will be

collected at a later time will be of high quality. It is during this pilot testing phase that weaknesses in

the methodology can be identi�ed and corrected, so that a large amount of research work is not


After collecting data and conducting a well-designed statistical test on that date, the researcher needs

to communicate the �ndings of the research in a clear and well-organized manner. The communication
of the �ndings of the research, both to the subjects of the research and to the organization that

commissioned the research, is the �nal phase of the research process.

Communication of �ndings usually begins with an executive summary, which is a short (approximately

one page or less) summary of the purpose of the research and the conclusions drawn from the

research. The executive summary usually is followed by an overview of the research, which explains

what was done and how it was done. Perhaps in a separate document, the researcher may recommend

steps to solve the problem that originally motivated he report, including how the proposed steps will
remedy the issue. Finally, all data that can be revealed should be included in an appendix, along with all

of the statistical outputs on which the conclusions were based.

When developing, analyzing, and reporting a business research project, researchers need to perform

complex calculations and solve statistical problems. It would take too long to calculate various

statistics with just a calculator and a pen and paper. Using statistical software package, including those

capabilities found inside Microsoft Excel, can ease the process of performing these calculations and
help in solving complex research problems.

Additional Materials

View a Pdf Transcript of Business Research Projects 




Business Research Projects
From Problem to Potential Solutions

Research Project: An Example

A business research project is typically undertaken because there is a problem at a company that needs to be solved. By effectively conducting
business research projects, companies should be able to find solutions to problems that they may be experiencing. Let’s look at an example.

A company’s current sales from a year ago are down by 20 percent. The company undertakes a research project to determine why the sales are
lower. There are many reasons why the sales may be lower. These reasons are as follows:
• The company may find that the economy is not doing well.
• The company may find that the competitor’s prices are lower.

Pilot Test
To gain proper insight into the problem, the company created a research design. A research design aids in achieving the research goals and acts as a
template for answering the research questions. After the research project is designed, the company conducted a pilot test. If the pilot test goes well,
more efforts should be conducted in accumulating the appropriate amount of data to thoroughly study the problem. If the pilot study does not go
well, the research project should be redesigned.

Through business research, the company found out that the sales for a particular company product are lower than the same time period a year ago
because the company has not been sufficiently advertising the product. The company can use this business research to ask the advertising
department to spend more money advertising the product.

Parts of a Research Report

A research report is prepared to communicate the research results to the business decision maker. Therefore, it is important the researcher adjust the
style and organization of the report according to the requirement of the decision maker. A research report may consist of various parts and can be
communicated through a written document, a conference call, a letter, an oral presentation, or a combination of these methods. However, a standard
research report should contain four parts: an executive summary, an overview of the research, implementation strategies, and an appendix.

Executive Summary
The executive summary, or abstract, should contain a summary of the studied problem, results found, and recommendations to solve the problem. It
is important to note that executives, or business decision makers, are busy individuals; they may not have time to read the entire report. The
executive summary may be the only thing they read; thus, it is important to succinctly present information.

The overview of the research, or the process description, should contain an explanation of why the topic was studied, a literature review of various
journal articles, an explanation of the process used to conduct the research, and a description of the research findings.

Implementation Strategies
A separate part of the report should recommend steps to resolve the problem.

The appendix should contain all the information that is needed to recreate the project. The appendix can include the data used in the project and any
computer programs used to calculate statistics.

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