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quality management degree online.docx

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Quality management degree online
In this file, you can ref useful information about quality management degree online such as
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I. Contents of quality management degree online
==================
Value
The University of Minnesota-Crookston receives this award thanks to its affordable and highly
regarded education. According to PayScale.com, the 30-year net return on investment of a
University of Minnesota degree is 8.8%. Additionally, Crookston has been ranked among the
best value public schools by the Princeton Review and one of the best schools in the Midwest by
U.S. News & World Report.
Flexibility
This award goes to the University of Minnesota-Crookston, where distance learning is 100%
online, and where students can choose how many courses they want to take a semester, study at
their convenience, and access their courses 24/7. Students can also choose to take courses both
online and on campus if they desire.
Career Support
The University of Minnesota-Crookston wins this award for its convenient online career
resources. Students can find a range of online workshops devoted to strengthening job search
skills, with topics including resume writing, cover letters, and interviewing. They can also reach
out to alumni, join a mentor program, and look for jobs and internships.
Disability Support
University of Minnesota-Crookston receives the award best online quality management program

for disability services. UM-Crookston’s disability services department offers several resources
for distance education services. Approved students may utilize taped textbook and reading
assignments, receive peer tutoring assistance, adapted testing, or closed circuit television
offerings for low vision students. The disability services department also offers individual
counseling or support group options, specialized computer software, large print text, and
resources and links through its website.
Faculty Credentials
The award for best quality management program faculty credentials is presented to the
University of Minnesota-Crookston, a public university that ranked among the best public
schools by the Princeton Review and listed as 49th in Midwestern regional colleges by U.S.
News & World Report. Online students benefit from a dedicated mentor program with
personalized attention focused on your strengths and weaknesses. UM-Crookston faculty also
offer several internship networking options and resource opportunities for advanced program
work.
Faculty Accessibility
The University of Minnesota-Crookston receives this award thanks to its online practices.
According to U.S. News & World Report, the school is among the best online programs for
student engagement and assessment. At the school, students can expect an instructor response
within 24 hours, and instructors hold office hours for five hours each week. Additionally, the
student-to-faculty ratio is 24-1, according to the National Center for Education Statistics, and
classes of that size help facilitate faculty accessibility.
Prior Learning Options
This award goes to the University of Minnesota-Crookston, which offer several ways for
students to gain academic credit before they start. One opportunity is by transferring previous
college credit earned at a regionally accredited institution. Students can get an unofficial
evaluation before they start to get an idea of what may transfer. Another opportunity is through a
Competency Assessment Portfolio, wherein students can prepare a portfolio containing materials
and documentation describing professional training and experiential learning, such as
professional licenses, certificates, seminars and workshops, which can then be evaluated for
college-level equivalency. A maximum of 30 credits can be awarded through prior learning.
New Online Student Orientation
The University of Minnesota-Crookston receives this award thanks to its online orientation class.
New students are required to take Orientation to Online Programs, a one-credit course offered
every semester that introduces learners to the school’s policies, email, virtual private network,
online library resources, writing scholarly reports, APA referencing, netiquette, networking in an
online environment, and group work in an online environment.
==================

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:






2. Control chart

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

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 Quality management degree online
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quality management systems
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