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Content
What is software quality management
In this file, you can ref useful information about what is software quality management such as
what is software quality managementforms, tools for what is software quality management, what
is software quality managementstrategies … If you need more assistant for what is software
quality management, please leave your comment at the end of file.
Other useful material for what is software quality management:
• qualitymanagement123.com/23-free-ebooks-for-quality-management
• qualitymanagement123.com/185-free-quality-management-forms
• qualitymanagement123.com/free-98-ISO-9001-templates-and-forms
• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of what is software quality management
==================
This article gives an overview of Software Quality Management and various processes that are a
part of Software Quality Management. Software Quality is a highly overused term and it may
mean different things to different people. You will learn What is Software Quality Management?,
What does it take to Manage Software Quality?, Quality Planning, Quality Assurance, Quality
Control, Importance of Documentation and What is Defect Tracking?
The definition of the ISO 8204 for quality:
“Totality of characteristics of an entity that bears on its ability to satisfy stated and implied
needs.”
This means that the Software product delivered should be as per the requirements defined. We
now examine a few more terms used in association with Software Quality.
Quality Planning:
In the Planning Process we determine the standards that are relevant for the Software Product,
the Organization and the means to achieve them.
Quality Assurance:
Once the standards are defined and we start building the product. It is very important to have
processes that evaluate the project performance and aim to assure that the Quality standards are
being followed and the final product will be in compliance.
Quality Control:
Once the software components are built the results are monitored to determine if they comply
with the standards. The data collected helps in measuring the performance trends and as needed
help in identifying defective pieces of code.
What is Software Quality Management?
Software Quality Management simply stated comprises of processes that ensure that the
Software Project would reach its goals. In other words the Software Project would meet the
clients expectations.
The key processes of Software Quality Management fall into the following three categories:
1) Quality Planning
2) Quality Assurance
3) Quality Control
What does it take to Manage Software Quality?
The Software Quality Management comprises of Quality Planning, Quality Assurance and
Quality Control Processes. We shall now take a closer look at each of them.
1) Quality Planning
Quality Planning is the most important step in Software Quality Management. Proper planning
ensures that the remaining Quality processes make sense and achieve the desired results. The
starting point for the Planning process is the standards followed by the Organization. This is
expressed in the Quality Policy and Documentation defining the Organization-wide standards.
Sometimes additional industry standards relevant to the Software Project may be referred to as
needed. Using these as inputs the Standards for the specific project are decided. The Scope of the
effort is also clearly defined. The inputs for the Planning are as summarized as follows:
a. Company’s Quality Policy
b. Organization Standards
c. Relevant Industry Standards
d. Regulations
e. Scope of Work
f. Project Requirements
Using these as Inputs the Quality Planning process creates a plan to ensure that standards agreed
upon are met. Hence the outputs of the Quality Planning process are:
a. Standards defined for the Project
b. Quality Plan
To create these outputs namely the Quality Plan various tools and techniques are used. These
tools and techniques are huge topics and Quality Experts dedicate years of research on these
topics. We would briefly introduce these tools and techniques in this article.
a. Benchmarking: The proposed product standards can be decided using the existing
performance benchmarks of similar products that already exist in the market.
b. Design of Experiments: Using statistics we determine what factors influence the Quality or
features of the end product
c. Cost of Quality: This includes all the costs needed to achieve the required Quality levels. It
includes prevention costs, appraisal costs and failure costs.
d. Other tools: There are various other tools used in the Planning process such as Cause and
Effect Diagrams, System Flow Charts, Cost Benefit Analysis, etc.
All these help us to create a Quality Management Plan for the project.
2) Quality Assurance
The Input to the Quality Assurance Processes is the Quality Plan created during Planning.
Quality Audits and various other techniques are used to evaluate the performance of the project.
This helps us to ensure that the Project is following the Quality Management Plan.
The tools and techniques used in the Planning Process such as Design of Experiments, Cause and
Effect Diagrams may also be used here, as required.
3) Quality Control
Following are the inputs to the Quality Control Process:
- Quality Management Plan.
- Quality Standards defined for the Project
- Actual Observations and Measurements of the Work done or in Progress
The Quality Control Processes use various tools to study the Work done. If the Work done is
found unsatisfactory it may be sent back to the development team for fixes. Changes to the
Development process may be done if necessary.
If the work done meets the standards defined then the work done is accepted and released to the
clients.
Importance of Documentation:
In all the Quality Management Processes special emphasis is put on documentation. Many
software shops fail to document the project at various levels. Consider a scenario where the
Requirements of the Software Project are not sufficiently documented. In this case it is quiet
possible that the client has a set of expectations and the tester may not know about them. Hence
the testing team would not be able test the software developed for these expectations or
requirements. This may lead to poor “Software Quality” as the product does not meet the
expectations.
Similarly consider a scenario where the development team does not document the installation
instructions. If a different person or a team is responsible for future installations they may end up
making mistakes during installation, thereby failing to deliver as promised.
Once again consider a scenario where a tester fails to document the test results after executing
the test cases. This may lead to confusion later. If there were an error, we would not be sure at
what stage the error was introduced in the software at a component level or when integrating it
with another component or due to environment on a particular server etc. Hence documentation
is the key for future analysis and all Quality Management efforts.
Steps:
In a typical Software Development Life Cycle the following steps are necessary for Quality
Management:
1) Document the Requirements
2) Define and Document Quality Standards
3) Define and Document the Scope of Work
4) Document the Software Created and dependencies
5) Define and Document the Quality Management Plan
6) Define and Document the Test Strategy
7) Create and Document the Test Cases
8) Execute Test Cases and (log) Document the Results
9) Fix Defects and document the fixes
10) Quality Assurance audits the Documents and Test Logs
Various Software Tools have been development for Quality Management. These Tools can help
us track Requirements and map Test Cases to the Requirements. They also help in Defect
Tracking.
What is Defect Tracking?
This is very important to ensure the Quality of the end Product. As test cases are executed at
various levels defects if any are found in the Software being tested. The Defects are logged and
data is collected. The Software Development fixes these defects and documents how they were
fixed The testing team verifies whether the defect was really fixed and closes the defects. This
information is very useful. Proper tracking ensures that all Defects were fixed. The information
also helps us for future projects.
The Capability Maturity Model defines various levels of Organization based on the processes
that they follow.
Level 0
The following is true for “Level 0” Organizations There are no Processes, tracking mechanisms, no plans. It is left to the developer or any person
responsible for Quality to ensure that the product meets expectations.
Level 1 – Performed Informally
The following is true for “Level 1” Organizations –
In Such Organizations, Typically the teams work extra hard to achieve the results. There are no
tracking mechanisms, standards defined. The work is done but is informal and not well
documented.
Level 2 – Planned and Tracked
The following is true for “Level 2” Organizations –
There are processes within a team and the team can repeat them or follow the processes for all
projects that it handles.
However the process is not standardized throughout the Organization. All the teams within the
organization do not follow the same standard.
Level 3 – Well-Defined
In “Level 3” Organizations the processes are well defined and followed throughout the
organization.
Level 4 – Quantitatively Controlled
In “Level 4” Organizations –
- The processes are well defined and followed throughout the organization
- The Goals are defined and the actual output is measured
- Metrics are collected and future performance can predicted
Level 5 – Continuously Improving
“Level 5” Organizations have well defined processes, which are measured and the organization
has a good understanding of IT projects affect the Organizational goals.
The Organization is able to continuously improve its processes based on this understanding.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
Who filled out the check sheet
What was collected (what each check represents,
an identifying batch or lot number)
Where the collection took place (facility, room,
apparatus)
When the collection took place (hour, shift, day of
the week)
Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific event.
[1][2] Common uses of the Ishikawa diagram are product
design and quality defect prevention, to identify potential
factors causing an overall effect. Each cause or reason for
imperfection is a source of variation. Causes are usually
grouped into major categories to identify these sources of
variation. The categories typically include
People: Anyone involved with the process
Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
Machines: Any equipment, computers, tools, etc.
required to accomplish the job
Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
Measurements: Data generated from the process
that are used to evaluate its quality
Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
III. Other topics related to What is software quality
management (pdf download)
quality management systems
quality management courses
quality management tools
iso 9001 quality management system
quality management process
quality management system example
quality system management
quality management techniques
quality management standards
quality management policy
quality management strategy
quality management books
In this file, you can ref useful information about what is software quality management such as
what is software quality managementforms, tools for what is software quality management, what
is software quality managementstrategies … If you need more assistant for what is software
quality management, please leave your comment at the end of file.
Other useful material for what is software quality management:
• qualitymanagement123.com/23-free-ebooks-for-quality-management
• qualitymanagement123.com/185-free-quality-management-forms
• qualitymanagement123.com/free-98-ISO-9001-templates-and-forms
• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of what is software quality management
==================
This article gives an overview of Software Quality Management and various processes that are a
part of Software Quality Management. Software Quality is a highly overused term and it may
mean different things to different people. You will learn What is Software Quality Management?,
What does it take to Manage Software Quality?, Quality Planning, Quality Assurance, Quality
Control, Importance of Documentation and What is Defect Tracking?
The definition of the ISO 8204 for quality:
“Totality of characteristics of an entity that bears on its ability to satisfy stated and implied
needs.”
This means that the Software product delivered should be as per the requirements defined. We
now examine a few more terms used in association with Software Quality.
Quality Planning:
In the Planning Process we determine the standards that are relevant for the Software Product,
the Organization and the means to achieve them.
Quality Assurance:
Once the standards are defined and we start building the product. It is very important to have
processes that evaluate the project performance and aim to assure that the Quality standards are
being followed and the final product will be in compliance.
Quality Control:
Once the software components are built the results are monitored to determine if they comply
with the standards. The data collected helps in measuring the performance trends and as needed
help in identifying defective pieces of code.
What is Software Quality Management?
Software Quality Management simply stated comprises of processes that ensure that the
Software Project would reach its goals. In other words the Software Project would meet the
clients expectations.
The key processes of Software Quality Management fall into the following three categories:
1) Quality Planning
2) Quality Assurance
3) Quality Control
What does it take to Manage Software Quality?
The Software Quality Management comprises of Quality Planning, Quality Assurance and
Quality Control Processes. We shall now take a closer look at each of them.
1) Quality Planning
Quality Planning is the most important step in Software Quality Management. Proper planning
ensures that the remaining Quality processes make sense and achieve the desired results. The
starting point for the Planning process is the standards followed by the Organization. This is
expressed in the Quality Policy and Documentation defining the Organization-wide standards.
Sometimes additional industry standards relevant to the Software Project may be referred to as
needed. Using these as inputs the Standards for the specific project are decided. The Scope of the
effort is also clearly defined. The inputs for the Planning are as summarized as follows:
a. Company’s Quality Policy
b. Organization Standards
c. Relevant Industry Standards
d. Regulations
e. Scope of Work
f. Project Requirements
Using these as Inputs the Quality Planning process creates a plan to ensure that standards agreed
upon are met. Hence the outputs of the Quality Planning process are:
a. Standards defined for the Project
b. Quality Plan
To create these outputs namely the Quality Plan various tools and techniques are used. These
tools and techniques are huge topics and Quality Experts dedicate years of research on these
topics. We would briefly introduce these tools and techniques in this article.
a. Benchmarking: The proposed product standards can be decided using the existing
performance benchmarks of similar products that already exist in the market.
b. Design of Experiments: Using statistics we determine what factors influence the Quality or
features of the end product
c. Cost of Quality: This includes all the costs needed to achieve the required Quality levels. It
includes prevention costs, appraisal costs and failure costs.
d. Other tools: There are various other tools used in the Planning process such as Cause and
Effect Diagrams, System Flow Charts, Cost Benefit Analysis, etc.
All these help us to create a Quality Management Plan for the project.
2) Quality Assurance
The Input to the Quality Assurance Processes is the Quality Plan created during Planning.
Quality Audits and various other techniques are used to evaluate the performance of the project.
This helps us to ensure that the Project is following the Quality Management Plan.
The tools and techniques used in the Planning Process such as Design of Experiments, Cause and
Effect Diagrams may also be used here, as required.
3) Quality Control
Following are the inputs to the Quality Control Process:
- Quality Management Plan.
- Quality Standards defined for the Project
- Actual Observations and Measurements of the Work done or in Progress
The Quality Control Processes use various tools to study the Work done. If the Work done is
found unsatisfactory it may be sent back to the development team for fixes. Changes to the
Development process may be done if necessary.
If the work done meets the standards defined then the work done is accepted and released to the
clients.
Importance of Documentation:
In all the Quality Management Processes special emphasis is put on documentation. Many
software shops fail to document the project at various levels. Consider a scenario where the
Requirements of the Software Project are not sufficiently documented. In this case it is quiet
possible that the client has a set of expectations and the tester may not know about them. Hence
the testing team would not be able test the software developed for these expectations or
requirements. This may lead to poor “Software Quality” as the product does not meet the
expectations.
Similarly consider a scenario where the development team does not document the installation
instructions. If a different person or a team is responsible for future installations they may end up
making mistakes during installation, thereby failing to deliver as promised.
Once again consider a scenario where a tester fails to document the test results after executing
the test cases. This may lead to confusion later. If there were an error, we would not be sure at
what stage the error was introduced in the software at a component level or when integrating it
with another component or due to environment on a particular server etc. Hence documentation
is the key for future analysis and all Quality Management efforts.
Steps:
In a typical Software Development Life Cycle the following steps are necessary for Quality
Management:
1) Document the Requirements
2) Define and Document Quality Standards
3) Define and Document the Scope of Work
4) Document the Software Created and dependencies
5) Define and Document the Quality Management Plan
6) Define and Document the Test Strategy
7) Create and Document the Test Cases
8) Execute Test Cases and (log) Document the Results
9) Fix Defects and document the fixes
10) Quality Assurance audits the Documents and Test Logs
Various Software Tools have been development for Quality Management. These Tools can help
us track Requirements and map Test Cases to the Requirements. They also help in Defect
Tracking.
What is Defect Tracking?
This is very important to ensure the Quality of the end Product. As test cases are executed at
various levels defects if any are found in the Software being tested. The Defects are logged and
data is collected. The Software Development fixes these defects and documents how they were
fixed The testing team verifies whether the defect was really fixed and closes the defects. This
information is very useful. Proper tracking ensures that all Defects were fixed. The information
also helps us for future projects.
The Capability Maturity Model defines various levels of Organization based on the processes
that they follow.
Level 0
The following is true for “Level 0” Organizations There are no Processes, tracking mechanisms, no plans. It is left to the developer or any person
responsible for Quality to ensure that the product meets expectations.
Level 1 – Performed Informally
The following is true for “Level 1” Organizations –
In Such Organizations, Typically the teams work extra hard to achieve the results. There are no
tracking mechanisms, standards defined. The work is done but is informal and not well
documented.
Level 2 – Planned and Tracked
The following is true for “Level 2” Organizations –
There are processes within a team and the team can repeat them or follow the processes for all
projects that it handles.
However the process is not standardized throughout the Organization. All the teams within the
organization do not follow the same standard.
Level 3 – Well-Defined
In “Level 3” Organizations the processes are well defined and followed throughout the
organization.
Level 4 – Quantitatively Controlled
In “Level 4” Organizations –
- The processes are well defined and followed throughout the organization
- The Goals are defined and the actual output is measured
- Metrics are collected and future performance can predicted
Level 5 – Continuously Improving
“Level 5” Organizations have well defined processes, which are measured and the organization
has a good understanding of IT projects affect the Organizational goals.
The Organization is able to continuously improve its processes based on this understanding.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
Who filled out the check sheet
What was collected (what each check represents,
an identifying batch or lot number)
Where the collection took place (facility, room,
apparatus)
When the collection took place (hour, shift, day of
the week)
Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific event.
[1][2] Common uses of the Ishikawa diagram are product
design and quality defect prevention, to identify potential
factors causing an overall effect. Each cause or reason for
imperfection is a source of variation. Causes are usually
grouped into major categories to identify these sources of
variation. The categories typically include
People: Anyone involved with the process
Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
Machines: Any equipment, computers, tools, etc.
required to accomplish the job
Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
Measurements: Data generated from the process
that are used to evaluate its quality
Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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