Course title: Knowledge Management
The presentation based on
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ent%2FSc

Str%2FStatistics.html
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29316

fen
http://www.slideshare.net/siddharth4mba/quality

control

analysis

of

data
http://www.slideshare.net/Al.Simard/knowledge

management

value

chains
Course title
:
Knowledge Management
Target group:
Higher education (students in the University)
Age group
: Bachelors, masters, exchange students (average age is 20

25 years)
Topic:
Statistics and its role in Knowledge Management
Learning outcomes:
By the end of this course, students will be able to:
•
have an understanding of
the definition of
statistics,
its
purposes,
the scope of using, the
types of
statistics
and the importance of
it in Knowledge Management
•
to
give examples
of each type
of statistics
•
to analyze the data and compile statistics
•
to know the arithmetic mean of statistics
•
analyze statistical data
Method & activities:
Methods of teaching: explaining, demonstrating, collaborating and learning by teaching.
Activities:
Lectures and Demonstrations
Exercises and Exam
Discussions
Group and Individual work
Seminars
Assessment:
The ECTS credit allocation scheme is as following:
•
The student may receive 5 credits for the course
•
Then the student may get another 0

3 credits based on the quality and quantity of his/her exersise results
Knowledge Management
Do you know what it is
?
Please, suggest your own definition=)
Total knowledge is increasing; half

life is
decreasing
Knowledge can be in more than one place at one
time
Knowledge may be permanent or time sensitive
Knowledge can be used without being
consumed
Selling does not reduce supply nor ability to sell
again
Buyers only purchase knowledge once
Once disseminated, knowledge cannot be
recalled
Knowledge
is a the sum of
what is known,
it is the mixture of the facts,
information, descriptions,
and skills acquired through
experience or education.
Knowledge that has been formally expressed and
transferred in a tangible form; intellectual property.
databases, statistics, collections
books, publications, reports, documents, correspondence
photographs, diagrams, illustrations
computer code, expert systems, decision

support systems
presentations, speeches, lectures
recorded experiences, stories
materials for education, teaching, and training
laws, regulations, procedures, rules, policies
embedded into products
Intangible personal
knowledge gained through
experience and self

learning.
It is influenced by beliefs,
perspectives, and values.
awareness
skills
mental models
expertise
judgement
wisdom
corporate memory
Value is very difficult to measure
Value is extracted when knowledge is used
Sharing increases the value of knowledge
Value increases with abundance
Buyer cannot judge value in advance
Value can be added by filtering knowledge
Value is not well related to acquisition cost
Information
Management
Decision

making
Knowledge
Management
Data
Management
Acquisition
Data
Wisdom
Information
Knowledge
Inputs
sensing
facts
meaning
understanding
judgement
Knowledge creation is a
precursor to everything else
Developing organizational capacity and
processes to capture, preserve, share,
and integrate data, information, and
knowledge to support organizational
goals, learning, and adaptation.
Talking (real, virtual)
E

mail (individuals, list servers, distribution lists)
Chat rooms, forums, discussion groups
Communities of interest, informal networks
Groupware (teams, working groups)
Conferences, workshops, knowledge fairs
Data bases, information bases, knowledge bases
Digital libraries (repositories, search, retrieval)
Information & knowledge markets
Analysis Of Data
Do you know what it is
?
Please, suggest your own definition=)
Data analysis
is a process in which raw data
is ordered, explored and organized so that
useful information can be extracted from it.
Need tools for data management and
analysis
Basic statistics skills
Manual methods
Graph paper
Calculator
Computer helpful
Spreadsheet
Important skills for laboratory personnel
All values are symmetrically distributed
around the mean
Characteristic “bell

shaped” curve
Assumed for all quality control statistics
Blood Urea mg/dL
0
1
2
3
4
5
29
29.5
30
30.5
31
31.5
32
32.5
33
33.5
34
34.5
35
Value
Frequency
Statistics
Statistical Concepts and Market
Do you know what it is
?
Please, suggest your own definition=)
Statistics is a field of knowledge that enables an
investigator to derive and evaluate conclusions
about a population from sample data. In other
words, statistics allow us to make generalizations
about a large group based on what we find in a
smaller group.
The field of statistics deals with gathering,
selecting, and classifying data; interpreting and
analyzing data; and deriving and evaluating the
validity and reliability of conclusions based on data.
Statistics means different things to different people.
To a baseball fan, statistics are information about a pitcher's
earned run average or a batter's slugging percentage or home
run count.
To a plant manager at a distribution company, statistics are daily
reports on inventory levels, absenteeism, labor efficiency, and
production.
To a medical researcher investigating the effects of a new drug,
statistics are evidence of the success of research efforts.
And to a college student, statistics are the grades made on all
the exams and quizzes in a course during the semester.
Today, statistics and statistical analysis are used in practically
every profession, and for managers in particular, statistics have
become a most valuable tool.
QUANTITATIVE AND QUALITATIVE
STATISTICS
Measurable observations are called quantitative observations.
Examples of measurable observations include the annual salary drawn by a
BlueCross/BlueShield underwriter or the age of a graduate student in an MBA
program. Both are measurable and are therefore quantitative observations.
Observations that cannot be measured are termed qualitative.
Qualitative observations can only be described.
Anthropologists, for instance, often use qualitative statistics to describe how one
culture varies from another.
Marketing researchers have increasingly used qualitative statistical techniques to
describe phenomena that are not easily measured, but can instead be described
and classified into meaningful categories.
Here, the distinction between a population of variates (a set of measured
observations) and a population of attributes (a set of described observations) is
important.
DESCRIPTIVE AND INFERENTIAL
STATISTICS
Managers can apply some statistical technique to virtually every branch of public
and private enterprise.
These techniques are commonly separated into two broad categories: descriptive
statistics and inferential statistics.
Descriptive statistics are typically simple summary figures calculated from a set
of observations. Suppose a professor computes an average grade for one
accounting class. If the professor uses the statistic simply to describe the
performance of that class, the result is a descriptive statistic of overall
performance.
Inferential statistics are used to apply conclusions about one set of observations
to reach a broader conclusion or an inference about something that has not been
directly observed. In this case, a professor might use the average grade from a
series of previous accounting classes to estimate, or infer, the average grade for
future accounting classes.
Frequency distribution allows for the compression of data into a table.
The table organizes the data into classes or groups of values describing
characteristics of the data. For example, students' grade distribution is
one characteristic of a graduate class.
A frequency distribution shows the number of observations from the
data set that fall into each category describing this characteristic. The
relevant categories are defined by the user based on what he or she is
trying to accomplish; in the case of grades, the categories might be each
letter grade (A, B, C, etc.), pass/fail/incomplete, or grade percentage
ranges. If you can determine the frequency with which values occur in
each category, you can construct a frequency distribution.
A relative frequency distribution presents frequencies in terms of
fractions or percentages. The sum of all relative frequency distributions
equals 1.00 or 100 percent.
ARITHMETIC MEAN.
The arithmetic mean is simply the average.
It is obtained by dividing the sum of all variates in the
population by the total number of variates.
The arithmetic mean is used more often than the median
and mode to describe the average variate in the population.
It best describes the values such as the average grade of a
graduate student, the average yards gained per carry by a
running back, and the average calories burned during a
cardiovascular workout.
It also has an interesting property: the sum of the deviations
of the individual variates from their arithmetic mean is
always is equal to zero.
Table 1 illustrates both a frequency distribution and a relative frequency
distribution. The frequency distribution gives a break down of the
number of students in each grade category ranging from A to F, including
"I" for incomplete. The relative frequency distribution takes that number
and turns it into a percentage of the whole number.
Statistics:
word used to refer to data and to the
methods we use to analyze data.
Descriptive statistics:
Used to summarize the
important characteristics of large data sets.
Inferential statistics:
Procedures used to make
forecasts, estimates, or judgments.
Population:
The set of all possible members of a
stated group.
Sample:
A subset of the population of interest.
Nominal scales:
Observations are classified
or counted with no particular order.
Ordinal scales:
Every observation is assigned
to one of several categories, which are
ordered with respect to a specified
characteristic.
Interval scale:
Provides relative ranking.
Ratio scales:
Provide ranking and equal
differences between scale values, and they
have a true zero point as the origin.
Parameter:
Used to describe a
characteristic of a population. Investment
analysis usually utilizes particularly the
mean return and the standard deviation of
returns.
Sample statistic:
Used to measure a
characteristic of a sample.
Frequency Distribution:
A tabular
presentation of statistic data, used in the
analysis of large data sets; assigning data to
specified groups or intervals.
To construct a frequency distribution:
Define the intervals.
Tally the observation.
Count the observations.
Relative frequency:
Calculated by dividing the
absolute frequency of each return interval by
the total number of observations. It is the
percentage of total observations falling within
each interval.
Cumulative absolute and relative frequency:
Computed by summing the absolute or
relative frequencies starting at the lowest
interval and progressing through the highest.
Histogram:
Graphical presentation of the
absolute frequency distribution. It’s a bar
chart of continuous data. It allows to
quickly see where the most of the
observations are concentrated.
Frequency polygon:
The midpoint of each
interval is plotted on the horizontal axis,
and the absolute frequency for the interval
is plotted on the vertical axis.
Thanks for your attention=)
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