Course title: Knowledge Management

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6 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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Course title: Knowledge Management


The presentation based on


http://learn.openscout.net/resource.html?loid=URN%3Ahttp
%3A%2F%2Fwww.referenceforbusiness.com%2Fmanagem
ent%2FSc
-
Str%2FStatistics.html




http://learn.openscout.net/resource.html?loid=ESPOL%3A1
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=)