Topic Statistics and its role in Knowledge Management by Medvedeva Taisia

1. Course title: Knowledge Management TopicStatistics and its role in Knowledge Managementby MedvedevaTaisia

2. The presentation based on • http://learn.openscout.net/resource.html?loid=URN%3Ahttp%3A%2F%2Fwww.referenceforbusiness.com%2Fmanagement%2FSc-Str%2FStatistics.html • http://learn.openscout.net/resource.html?loid=ESPOL%3A129316-fen • http://www.slideshare.net/siddharth4mba/quality-control-analysis-of-data • http://www.slideshare.net/Al.Simard/knowledge-management-value-chains

3. 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

4. Knowledge Management • Do you know what it is? • Please, suggest your own definition=)

5. Knowledge Attributes • 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

6. Knowledge • 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.

7. Explicit Knowledge • 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

8. Tacit Knowledge • 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

9. Knowledge Value • 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

10. Data Management Information Management Knowledge Management Decision-making Acquisition Knowledge Wisdom Inputs Data Information sensing facts meaning understanding judgement Knowledge Creation Value Chain Knowledge creation is a precursor to everything else

11. Knowledge Management:A Definition Developing organizational capacity and processes to capture, preserve, share, and integrate data, information, and knowledge to support organizational goals, learning, and adaptation.

12. Sharing Knowledge: Technology • 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

13. Analysis Of Data • Do you know what it is? • Please, suggest your own definition=)

14. Data Analyzis • Data analysis is a process in which raw data is ordered, explored and organized so that useful information can be extracted from it.

15. How to carry out analysis of data? • Need tools for data management and analysis • Basic statistics skills • Manual methods Graph paper Calculator • Computer helpful Spreadsheet • Important skills for laboratory personnel

16. Normal Distribution • All values are symmetrically distributed around the mean • Characteristic “bell-shaped” curve • Assumed for all quality control statistics

17. Normal Distribution

18. Statistics • Statistical Concepts and Market • Do you know what it is? • Please, suggest your own definition=)

19. Statistics • 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.

20. Statistics • 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.

21. QUANTITATIVE ANDQUALITATIVE STATISTICS • 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.

22. DESCRIPTIVE AND INFERENTIAL STATISTICS • 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.

23. FREQUENCY DISTRIBUTION • 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.

24. ARITHMETIC MEAN. • 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.

25. 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.

26. Statistical Concepts • 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.

27. Types of Measurement Scales • 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.

28. Statistical Concepts • 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.

29. Statistical Concepts • 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.

30. Relative Frequencies and Cumulative Relative Frequencies • 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.

31. Statistical Concepts • 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.

32. Thanks for your attention=)