Quantitative Analysis For Business Practice Test
Define quantitative analysis, including its business Technology gives businesses access to tremendous amounts of data—often
purpose: more data than companies can manage. Sales figures, time management
techniques, production costs, shipping routes—the possibilities for data are
nearly limitless in today's business environment. Managers and workers may
ask, can any of this data help move the company forward in helpful ways?
Quantitative analysis provides methods to analyze large or small amounts of
data to look for patterns, trends, and relationships. Mathematical analysis can
help managers make strategic decisions and find statistically supported solutions
to business questions.
Data may be categorized as either subjective or objective. Subjective data,
obtained through surveys and interviews, are considered non-measurable,
though marketing research has developed ways to study the intensity of opinions
that guide consumer behavior. Subjective data typically include personal
perceptions, such as likes, dislikes, attitudes, and opinions. Objective data are
measurable and typically arise from observation or testing in business areas like
sales, operations, manufacturing, and logistics. Data must be valid: that is, the
data must accurately represent the true business relationship at hand. Further,
the data must be reliable: if we sought to characterize a particular business
relationship by gathering data several different times (different samples), the data
would reflect the relationship the same way with every sample.
Quantitative analysis generally depends upon simple to complex mathematics
and statistical modeling to discover important information about collected data.
Companies analyze data to solve problems, and make decisions based upon
objective data to gain an economic or competitive advantage. Examples of
quantitative analysis include cost-benefit analysis, inventory analysis, logistical
analysis, and forecasting revenue.
The quantitative analysis approach defines a problem and then develops a
mathematical model to represent the particular business situation. The model
allows managers to make inferences regarding the data. For example, if
Company XYZ makes $3 revenue from each book sold and p represents total
revenue earned and q represents the number of books sold, then the
mathematical model to determine the company profit would be p=3q. As long as
the assumptions behind the model p=3q hold true, Company XYZ can use that
equation to forecast its revenue from a particular book. But if the economic
outlook changes and the company cannot say with confidence that each book
contributes $3 to the bottom line, they will need to create a new model or
equation. Perhaps the economy declines and students can no longer afford new
books, or a competitor offers an alternative book with a more popular approach.
The business world and the data available change constantly and thus data tell
an ever-evolving story. For a particular period of time, then, mathematical models
featuring valid and reliable data can guide managers to make rational decisions
about pricing, distribution, manufacturing, and other functional areas.
Understandably, gathering data for a complex problem can be time-consuming
and difficult. Input data must be both relevant and accurate to achieve optimal
results. The output from the model can then provide optimal solutions. Used
correctly, model outputs can be predictive, helping managers understand future
business outcomes if they change practices based on the results of sound
analysis. Assumptions and solutions must be tested and analyzed prior to
implementation.
Cause and effect demonstrate a relationship between events where one event
(effect) is the result of the other (cause). Cause and effect use independent and
dependent variables. A dependent variable is the variable that is being
measured, or affected. The independent variable is free to change in a given
model. The dependent variable is affected by the changes in the causing
independent variable. Although only one dependent variable is considered, many
independent variables can have an effect.
Quantitative research and analysis aim to determine if there is a relationship
between one event (an independent variable) and another (a dependent
variable). For example, when business managers are looking at quarterly
performance that either exceeded expectations or fell short, they are attempting
to find a relationship between events in order to explain or interpret the results.
To find the relationship, it is necessary to determine the dependent and
independent variables.
If a pharmaceutical company wants to study the impact of a chemotherapy drug
on a group of patients, the independent variable is the administration of the drug,
how often, how much. The dependent variable is the result that the drug has on
the cancer. In a math equation, such as y=3x+2, x is the independent variable,
and y is the dependent variable. The y value is dependent on the x value.
Cause and effect show how one event affects another. For example, when water
is heated to a certain temperature (the independent variable or cause), the water
boils (the dependent variable or effect). The following diagram provides an
example graphical representation of the many different causes that could
contribute to a problem. By using quantitative analysis, businesses can review a
problem mathematically and statistically to determine the root cause(s).
,Quantitative Analysis For Business Practice Test
When a business has a serious problem (effect), it is important to evaluate and
study potential causes before determining a solution. A fishbone diagram is
sometimes used to determine the cause of a problem. It is important to look at all
of the potential causes, not just the obvious. The problem statement is written on
the diagram as the "effect." Brainstorming can take place for each category that
might be causing a problem, allowing for a mathematical model to be
constructed. Examples of problem areas might include equipment, process,
management, materials, environment, and people. A completed fishbone
diagram will help to draw out the probable root causes of a problem in business.
Sometimes an additional variable can strengthen or weaken the impact of the
relationship between the dependent and independent variable. A moderating
variable is a third variable that changes the established effect of the independent
variable on the dependent variable. In a moderating relationship, the relationship
between the dependent and independent variables depends on the level of the
moderating variable. For example, higher income levels are associated with
higher levels of education; however, the effect of this relationship is even
stronger for men than for women. Therefore, the strength of the relationship
between income and education depends on gender as a third, moderating
variable.
A mediating variable explains the relationship between the dependent and
independent variables. Mediating variables are intervening factors that can
change the impact of the independent variable on the dependent variable. For
example, having a personal trainer helps gym members stick with an exercise
routine. The mediating variable might be that the trainer motivates the members,
which in turn causes them to stick with their exercise routine. The motivation is
the mediating variable and explains why people with personal trainers stick with
their exercise routines more than those who do not. A very motivated person
without a personal trainer would also stick with the routine. An additional example
would be if a business was offering stock options as part of their employee
compensation package. Each employee would receive between zero and 50
stock options based on their annual performance appraisal. The mediating
variable is the awarding of stock options based on performance. The stock
options are the motivation and explains why employees might receive more
options than those underperforming employees.
Scatter diagrams are used to graph pairs of numbers to determine the
relationship. A coffee shop might keep track of how the temperature outdoors
impacts the coffee sales. If the sales increased when the temperature decreases,
then the business could assume that there is a negative linear relationship
between these two variables. We could also say that the variables are correlated.
The line that shows the general direction of the relationship of points over time is
called the trend line. If the trend line moves downward as we progress from left
to right, there is a negative correlation between the two variables.
What happens today in business can help managers make predictions as to what
might happen in the future. Researchers and analysts look for specific patterns in
the current and past business data and then make forecasts. Forecasting helps
businesses make adjustments to the current business environment to encourage
better business outcomes in the future.
Forecasting can help businesses make better or more accurate predictions about
customer demand. For example, a fast-food restaurant can use forecasting to
predict when more employees are needed to service customers. By studying
previous and current weeks of customer demand and then making a prediction,
businesses can then provide adequate staffing for faster, higher quality customer
service. Another example of using mathematical forecasting is in manufacturing.
Forecasting can assist manufacturing managers with decisions about increases
or decreases in production capacity.
Forecasting presents a number of choices and the likelihood of occurrence.
When managers are presented with a forecast, they can select the option that
will best maximize the likelihood of success.
Although no one can be 100 percent sure of what will really happen in the future,
forecasting helps to reduce the uncertainty and increase the likelihood of
successful planning. Forecasting does not provide a guarantee of success in the
future, but it certainly increases the likelihood of positive outcomes.
Quantitative analysis uses math and statistics to make business decisions and
evaluate business problems, strategies, and investments. Quantitative analysis is
used to create models for pricing, risk management, inventory, stock trading
strategies, credit analysis, and more. All of these activities involve numbers and
formulas.
Mathematical models can accurately represent business problems and help a
decision-maker solve the problems. Mathematical models are helpful and are
used to quantitatively analyze the impact of changes on business performance
and the evaluation of risk. The models show mathematical relationships in the
form of equations or inequalities, such as averages and totals.
, Quantitative Analysis For Business Practice Test
Statistics is the gathering, organizing, and interpreting of numerical data. It
provides tools to analyze important numerical information through organizing,
analyzing, and interpreting large amounts of data. There are two types of
statistics: descriptive and inferential.
Steps in Quantitative Analysis 1. Define problem.
2. Develop mathematical model.
3. Prepare and input data.
4. Find best solution.
5. Test solution.
6. Analyze results.
7. Implement solution.
Using the steps in quantitative analysis can be helpful in finding solutions thereby
saving time and money by accurately representing a situation, solving complex
problems, and providing insight to business decision makers.
Appropriate and useful quantitative analysis ensures the Valid and Measurable
use of
The hallmark of quantitative analysis is ensuring the use of only valid data to
Hint, displayed below-Select-budget and begin with. That is, there needs to be some assurance that the underlying data
financialsamples and sampling techniquesvalid and are a true reflection of reality. Also, the data used are measurable, that is,
measurableobjective and subjective-Select- objective and countable. Subjective data that rely on opinions and tastes require
different kinds of analysis. For quantitative analysis to be meaningful, it must rest
data to understand a business situation. on the ability to quantify the phenomenon being studied, whether Internet hits,
dollars, miles, minutes, or some other discrete information.
When is quantitative analysis most useful? When the problem being evaluated can be represented by discrete data that are
valid, objective, and reliable.
In order to use the tools of quantitative analysis, data must be valid, reliable, and
objective. Quantitative data are number-based data.
Under what circumstances could quantitative analysis be When the problem being analyzed can be numerically represented and valid and
used successfully? reliable data are available
There are many techniques available in quantitative analysis, but they all require
valid and reliable numeric information to begin with.
Which of the following accurately describes quantitative Ensuring the use of valid, reliable, and objectively measurable data in order to
analysis? understand a phenomenon
The hallmark of quantitative analysis is ensuring the use of only valid, objective,
and reliable data to begin with. That is, we must ensure that the underlying data
are a true and repeatable reflection of reality. Also, the data used must be
measurable in nature: that is, objective and not subjective. The phenomenon
being evaluated must be quantifiable.
What would be the best reason for not wanting to use When the problem being evaluated lacks the presence of objective, valid,
quantitative analysis? reliable, and measurable data to use for the analysis
Without quantitative data, quantitative analysis cannot be done. In addition, it is
assumed that the quantitative data being used are credible, that is, believable,
and that the data represent a true reflection of reality.
What would be the best application of quantitative A marketing director wants to determine the average household income of the
analysis for the situations below? company's key customers.
This question suggests the use of metrics to collect and evaluate data. This
information could come from a variety of sources, such a customer survey or
focus groups. The information is numeric in nature.
In defining a cause and effect relationship, the variable Independent variable
that drives some change in another variable is called
what? The independent variable is the factor that causes change to the dependent
factor. In other words, the movement of the dependent factor is driven by the
movement of the independent factor.
In defining a cause and effect relationship, the variable Dependent variable
that is changed as a result of the action of some other
variable is called what? The independent variable is the factor that causes change to the dependent
factor. In other words, the movement of the dependent factor is driven by the
movement of the independent factor.
, Quantitative Analysis For Business Practice Test
f there is a linear relationship between variables, we can There is an association between the variables but not proof that a change in one
say they are correlated. Determining that variables are causes a change in the other.
correlated is very important; however, to draw specific
conclusions about the cause and effect relationship will Establishing the basic relationship does not, in itself, establish proof of the effects
take additional advanced analytical techniques to further of the relationship. It takes other advanced analytical techniques to further test
test the relationship in order to exclude other contributory the relationship in order to exclude other contributory factors that could have
factors that could have caused the change. caused the change.
When we have established a positive relationship
between two variables, what can we conclude?
In determining relationships between cause and effect, That there is a positive correlation between the variables
what does it mean if one variable increases as the other
variable also increases? The positive relationship means that the increase of the independent variable will
cause the dependent variable to increase proportionately.
In studying cause and effect relationships, what can we When the independent variable increases, the dependent variable will increase
say when there is a positive relationship between the at a proportionate rate.
variables?
A positive relationship is defined as one in which the dependent variable will
increase as the independent variable increases. Conversely, as the independent
variable decreases, the dependent variable will decrease proportionately.
Correlations between dependent and independent This upward movement to the right side of a chart represents the basic positive
variables can be graphically displayed in a scatter relationship between an independent variable and a dependent variable. If there
diagram using spreadsheet software. Trend lines show is a downward movement of the trend line from left to right, then this represents a
as either an upward movement or a downward negative relationship.
movement. If the line shows upward movement, from the
bottom left to the upper right of the chart,there is a
positive linear relationship. If there is a downward
movement of the trend line, from upper left to lower right,
then there is a negative linear relationship.
Which of the following graphs best demonstrates a trend
line with a positive correlation?
Although there are many benefits to forecasting, the Current
primary purpose of forecasting is to control the
Forecasting is used in order to help make better decisions in the current
Hint, displayed below-Select-currentfuture-Select- environment that may help the future, but this is not certain.
situation in order to make better decisions.
Forecasting is used in order to help establish the ___ of Probability
a future event.
Forecasting is not a guarantee of future results. Forecasting, though, seeks to
identify the most and least probable outcomes of some future event.
One purpose behind business forecasting it to estimate Assets
future demand. This is particularly helpful for an
operations manager to manage the use of today's ___ in The operations manager may need to adjust the current work plan to align the
order to succeed in the future. right amount of assets to meet the projected demand.
One purpose behind business forecasting is to estimate 1. Choices: Forecasting proposes to present a method to create a number of
future demand. Forecasting gives planners the ability to choices and their likelihood of occurrence. This enables the manager to
make prudent ___ among/for ___ in order to maximize maximize their likelihood of success.
their likelihood of success.
2. Several Alternatives: Forecasting generally views several possible future
states and their likelihood, so there are multiple alternatives to consider. In
forecasting, the planner will identify various criteria with which to provide some
relative quantitative measures among the alternatives. Planners make their
choice based on the weight of the evidence.
Define quantitative analysis, including its business Technology gives businesses access to tremendous amounts of data—often
purpose: more data than companies can manage. Sales figures, time management
techniques, production costs, shipping routes—the possibilities for data are
nearly limitless in today's business environment. Managers and workers may
ask, can any of this data help move the company forward in helpful ways?
Quantitative analysis provides methods to analyze large or small amounts of
data to look for patterns, trends, and relationships. Mathematical analysis can
help managers make strategic decisions and find statistically supported solutions
to business questions.
Data may be categorized as either subjective or objective. Subjective data,
obtained through surveys and interviews, are considered non-measurable,
though marketing research has developed ways to study the intensity of opinions
that guide consumer behavior. Subjective data typically include personal
perceptions, such as likes, dislikes, attitudes, and opinions. Objective data are
measurable and typically arise from observation or testing in business areas like
sales, operations, manufacturing, and logistics. Data must be valid: that is, the
data must accurately represent the true business relationship at hand. Further,
the data must be reliable: if we sought to characterize a particular business
relationship by gathering data several different times (different samples), the data
would reflect the relationship the same way with every sample.
Quantitative analysis generally depends upon simple to complex mathematics
and statistical modeling to discover important information about collected data.
Companies analyze data to solve problems, and make decisions based upon
objective data to gain an economic or competitive advantage. Examples of
quantitative analysis include cost-benefit analysis, inventory analysis, logistical
analysis, and forecasting revenue.
The quantitative analysis approach defines a problem and then develops a
mathematical model to represent the particular business situation. The model
allows managers to make inferences regarding the data. For example, if
Company XYZ makes $3 revenue from each book sold and p represents total
revenue earned and q represents the number of books sold, then the
mathematical model to determine the company profit would be p=3q. As long as
the assumptions behind the model p=3q hold true, Company XYZ can use that
equation to forecast its revenue from a particular book. But if the economic
outlook changes and the company cannot say with confidence that each book
contributes $3 to the bottom line, they will need to create a new model or
equation. Perhaps the economy declines and students can no longer afford new
books, or a competitor offers an alternative book with a more popular approach.
The business world and the data available change constantly and thus data tell
an ever-evolving story. For a particular period of time, then, mathematical models
featuring valid and reliable data can guide managers to make rational decisions
about pricing, distribution, manufacturing, and other functional areas.
Understandably, gathering data for a complex problem can be time-consuming
and difficult. Input data must be both relevant and accurate to achieve optimal
results. The output from the model can then provide optimal solutions. Used
correctly, model outputs can be predictive, helping managers understand future
business outcomes if they change practices based on the results of sound
analysis. Assumptions and solutions must be tested and analyzed prior to
implementation.
Cause and effect demonstrate a relationship between events where one event
(effect) is the result of the other (cause). Cause and effect use independent and
dependent variables. A dependent variable is the variable that is being
measured, or affected. The independent variable is free to change in a given
model. The dependent variable is affected by the changes in the causing
independent variable. Although only one dependent variable is considered, many
independent variables can have an effect.
Quantitative research and analysis aim to determine if there is a relationship
between one event (an independent variable) and another (a dependent
variable). For example, when business managers are looking at quarterly
performance that either exceeded expectations or fell short, they are attempting
to find a relationship between events in order to explain or interpret the results.
To find the relationship, it is necessary to determine the dependent and
independent variables.
If a pharmaceutical company wants to study the impact of a chemotherapy drug
on a group of patients, the independent variable is the administration of the drug,
how often, how much. The dependent variable is the result that the drug has on
the cancer. In a math equation, such as y=3x+2, x is the independent variable,
and y is the dependent variable. The y value is dependent on the x value.
Cause and effect show how one event affects another. For example, when water
is heated to a certain temperature (the independent variable or cause), the water
boils (the dependent variable or effect). The following diagram provides an
example graphical representation of the many different causes that could
contribute to a problem. By using quantitative analysis, businesses can review a
problem mathematically and statistically to determine the root cause(s).
,Quantitative Analysis For Business Practice Test
When a business has a serious problem (effect), it is important to evaluate and
study potential causes before determining a solution. A fishbone diagram is
sometimes used to determine the cause of a problem. It is important to look at all
of the potential causes, not just the obvious. The problem statement is written on
the diagram as the "effect." Brainstorming can take place for each category that
might be causing a problem, allowing for a mathematical model to be
constructed. Examples of problem areas might include equipment, process,
management, materials, environment, and people. A completed fishbone
diagram will help to draw out the probable root causes of a problem in business.
Sometimes an additional variable can strengthen or weaken the impact of the
relationship between the dependent and independent variable. A moderating
variable is a third variable that changes the established effect of the independent
variable on the dependent variable. In a moderating relationship, the relationship
between the dependent and independent variables depends on the level of the
moderating variable. For example, higher income levels are associated with
higher levels of education; however, the effect of this relationship is even
stronger for men than for women. Therefore, the strength of the relationship
between income and education depends on gender as a third, moderating
variable.
A mediating variable explains the relationship between the dependent and
independent variables. Mediating variables are intervening factors that can
change the impact of the independent variable on the dependent variable. For
example, having a personal trainer helps gym members stick with an exercise
routine. The mediating variable might be that the trainer motivates the members,
which in turn causes them to stick with their exercise routine. The motivation is
the mediating variable and explains why people with personal trainers stick with
their exercise routines more than those who do not. A very motivated person
without a personal trainer would also stick with the routine. An additional example
would be if a business was offering stock options as part of their employee
compensation package. Each employee would receive between zero and 50
stock options based on their annual performance appraisal. The mediating
variable is the awarding of stock options based on performance. The stock
options are the motivation and explains why employees might receive more
options than those underperforming employees.
Scatter diagrams are used to graph pairs of numbers to determine the
relationship. A coffee shop might keep track of how the temperature outdoors
impacts the coffee sales. If the sales increased when the temperature decreases,
then the business could assume that there is a negative linear relationship
between these two variables. We could also say that the variables are correlated.
The line that shows the general direction of the relationship of points over time is
called the trend line. If the trend line moves downward as we progress from left
to right, there is a negative correlation between the two variables.
What happens today in business can help managers make predictions as to what
might happen in the future. Researchers and analysts look for specific patterns in
the current and past business data and then make forecasts. Forecasting helps
businesses make adjustments to the current business environment to encourage
better business outcomes in the future.
Forecasting can help businesses make better or more accurate predictions about
customer demand. For example, a fast-food restaurant can use forecasting to
predict when more employees are needed to service customers. By studying
previous and current weeks of customer demand and then making a prediction,
businesses can then provide adequate staffing for faster, higher quality customer
service. Another example of using mathematical forecasting is in manufacturing.
Forecasting can assist manufacturing managers with decisions about increases
or decreases in production capacity.
Forecasting presents a number of choices and the likelihood of occurrence.
When managers are presented with a forecast, they can select the option that
will best maximize the likelihood of success.
Although no one can be 100 percent sure of what will really happen in the future,
forecasting helps to reduce the uncertainty and increase the likelihood of
successful planning. Forecasting does not provide a guarantee of success in the
future, but it certainly increases the likelihood of positive outcomes.
Quantitative analysis uses math and statistics to make business decisions and
evaluate business problems, strategies, and investments. Quantitative analysis is
used to create models for pricing, risk management, inventory, stock trading
strategies, credit analysis, and more. All of these activities involve numbers and
formulas.
Mathematical models can accurately represent business problems and help a
decision-maker solve the problems. Mathematical models are helpful and are
used to quantitatively analyze the impact of changes on business performance
and the evaluation of risk. The models show mathematical relationships in the
form of equations or inequalities, such as averages and totals.
, Quantitative Analysis For Business Practice Test
Statistics is the gathering, organizing, and interpreting of numerical data. It
provides tools to analyze important numerical information through organizing,
analyzing, and interpreting large amounts of data. There are two types of
statistics: descriptive and inferential.
Steps in Quantitative Analysis 1. Define problem.
2. Develop mathematical model.
3. Prepare and input data.
4. Find best solution.
5. Test solution.
6. Analyze results.
7. Implement solution.
Using the steps in quantitative analysis can be helpful in finding solutions thereby
saving time and money by accurately representing a situation, solving complex
problems, and providing insight to business decision makers.
Appropriate and useful quantitative analysis ensures the Valid and Measurable
use of
The hallmark of quantitative analysis is ensuring the use of only valid data to
Hint, displayed below-Select-budget and begin with. That is, there needs to be some assurance that the underlying data
financialsamples and sampling techniquesvalid and are a true reflection of reality. Also, the data used are measurable, that is,
measurableobjective and subjective-Select- objective and countable. Subjective data that rely on opinions and tastes require
different kinds of analysis. For quantitative analysis to be meaningful, it must rest
data to understand a business situation. on the ability to quantify the phenomenon being studied, whether Internet hits,
dollars, miles, minutes, or some other discrete information.
When is quantitative analysis most useful? When the problem being evaluated can be represented by discrete data that are
valid, objective, and reliable.
In order to use the tools of quantitative analysis, data must be valid, reliable, and
objective. Quantitative data are number-based data.
Under what circumstances could quantitative analysis be When the problem being analyzed can be numerically represented and valid and
used successfully? reliable data are available
There are many techniques available in quantitative analysis, but they all require
valid and reliable numeric information to begin with.
Which of the following accurately describes quantitative Ensuring the use of valid, reliable, and objectively measurable data in order to
analysis? understand a phenomenon
The hallmark of quantitative analysis is ensuring the use of only valid, objective,
and reliable data to begin with. That is, we must ensure that the underlying data
are a true and repeatable reflection of reality. Also, the data used must be
measurable in nature: that is, objective and not subjective. The phenomenon
being evaluated must be quantifiable.
What would be the best reason for not wanting to use When the problem being evaluated lacks the presence of objective, valid,
quantitative analysis? reliable, and measurable data to use for the analysis
Without quantitative data, quantitative analysis cannot be done. In addition, it is
assumed that the quantitative data being used are credible, that is, believable,
and that the data represent a true reflection of reality.
What would be the best application of quantitative A marketing director wants to determine the average household income of the
analysis for the situations below? company's key customers.
This question suggests the use of metrics to collect and evaluate data. This
information could come from a variety of sources, such a customer survey or
focus groups. The information is numeric in nature.
In defining a cause and effect relationship, the variable Independent variable
that drives some change in another variable is called
what? The independent variable is the factor that causes change to the dependent
factor. In other words, the movement of the dependent factor is driven by the
movement of the independent factor.
In defining a cause and effect relationship, the variable Dependent variable
that is changed as a result of the action of some other
variable is called what? The independent variable is the factor that causes change to the dependent
factor. In other words, the movement of the dependent factor is driven by the
movement of the independent factor.
, Quantitative Analysis For Business Practice Test
f there is a linear relationship between variables, we can There is an association between the variables but not proof that a change in one
say they are correlated. Determining that variables are causes a change in the other.
correlated is very important; however, to draw specific
conclusions about the cause and effect relationship will Establishing the basic relationship does not, in itself, establish proof of the effects
take additional advanced analytical techniques to further of the relationship. It takes other advanced analytical techniques to further test
test the relationship in order to exclude other contributory the relationship in order to exclude other contributory factors that could have
factors that could have caused the change. caused the change.
When we have established a positive relationship
between two variables, what can we conclude?
In determining relationships between cause and effect, That there is a positive correlation between the variables
what does it mean if one variable increases as the other
variable also increases? The positive relationship means that the increase of the independent variable will
cause the dependent variable to increase proportionately.
In studying cause and effect relationships, what can we When the independent variable increases, the dependent variable will increase
say when there is a positive relationship between the at a proportionate rate.
variables?
A positive relationship is defined as one in which the dependent variable will
increase as the independent variable increases. Conversely, as the independent
variable decreases, the dependent variable will decrease proportionately.
Correlations between dependent and independent This upward movement to the right side of a chart represents the basic positive
variables can be graphically displayed in a scatter relationship between an independent variable and a dependent variable. If there
diagram using spreadsheet software. Trend lines show is a downward movement of the trend line from left to right, then this represents a
as either an upward movement or a downward negative relationship.
movement. If the line shows upward movement, from the
bottom left to the upper right of the chart,there is a
positive linear relationship. If there is a downward
movement of the trend line, from upper left to lower right,
then there is a negative linear relationship.
Which of the following graphs best demonstrates a trend
line with a positive correlation?
Although there are many benefits to forecasting, the Current
primary purpose of forecasting is to control the
Forecasting is used in order to help make better decisions in the current
Hint, displayed below-Select-currentfuture-Select- environment that may help the future, but this is not certain.
situation in order to make better decisions.
Forecasting is used in order to help establish the ___ of Probability
a future event.
Forecasting is not a guarantee of future results. Forecasting, though, seeks to
identify the most and least probable outcomes of some future event.
One purpose behind business forecasting it to estimate Assets
future demand. This is particularly helpful for an
operations manager to manage the use of today's ___ in The operations manager may need to adjust the current work plan to align the
order to succeed in the future. right amount of assets to meet the projected demand.
One purpose behind business forecasting is to estimate 1. Choices: Forecasting proposes to present a method to create a number of
future demand. Forecasting gives planners the ability to choices and their likelihood of occurrence. This enables the manager to
make prudent ___ among/for ___ in order to maximize maximize their likelihood of success.
their likelihood of success.
2. Several Alternatives: Forecasting generally views several possible future
states and their likelihood, so there are multiple alternatives to consider. In
forecasting, the planner will identify various criteria with which to provide some
relative quantitative measures among the alternatives. Planners make their
choice based on the weight of the evidence.
