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Operation and Information Management & Quantitative Technics

COURSE COVER SLIDE. Operation and Information Management & Quantitative Technics. CODE: 01. 2008-2009 Fall. Prepared By:. Saban Eren, Ph.D., Professor. INTRODUCTION SLIDE. Objectives. CODE: 02.

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Operation and Information Management & Quantitative Technics

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  1. COURSE COVER SLIDE Operation and Information Management & Quantitative Technics CODE: 01 2008-2009 Fall Prepared By: Saban Eren, Ph.D., Professor

  2. INTRODUCTION SLIDE Objectives CODE: 02 The course introduces students to the variety of methods to enable learning about quantitative techniques in business management where insight and problem-solving can be aided by the effective use of quantitative analytical techniques, including statistics.

  3. INTRODUCTION SLIDE Course Outline CODE: 02 Introduction to quantitative methods Presenting data Central tendency Dispersion of the data Probability Distribution Distribution Sampling theory Confidence intervals Hypothesis theory Hypothesis theory Regression analysis Time series

  4. INTRODUCTION SLIDE References CODE: 02 Berenson M.L., Levine D.K. & Krehbiel T.C., “Basic Business Statistics”, 11/e, Prentice Hall, 2009. Burton G., Carrol G. & Wall S., “Quantitative Methods for Business & Economics”, Prentice Hall, 2/e, 2002. Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary/index.html Kay D., “CliffsAP Statistics”, Wiley Publishing, 2005. Kazmier L.J., “Business Statistics”, 4/E, Schaum’s Outline Series McGRAW-HILL, 2004. McClave J.T., Benson P.G. & Sincich T., “A First Course in Business Statistics”, 8/e, Prentice Hall, 2000. Render B., Stair R.M. & Hanna M.E., “Quantitative Analysis for Management”, 8/e, Prentice Hall, 2003. Tanis E.A., “Statistics I:Descriptive Statistics and Probability”, HJB, 1987. Triola M.F., “Elementary Statistics”, 9/e, Addison Wesley, 2005. Wates D., “Quantitative Methods for Business”, 4/e, Prentice Hall, 2008. Wikipedia, http://en.wikipedia.org/

  5. COURSE COVER SLIDE Operation and Information Management & Quantitative Technics CODE: 01 Introduction to quantitative methods 2008-2009 Fall Session 1 Prepared By: Saban Eren, Ph.D., Professor

  6. INTRODUCTION SLIDE Learing Objectives CODE: 02 In this session, you will learn about Presenting Data and Graphical Displays. After reading this session, you should be able to: What is quantitative analysis? How statistics is used in business Types of statistics The vocabulary of statistics

  7. LARGE VISUAL ELEMENT SLIDE Quantitative Analysis CODE: 05 Quantitative Analysis Meaningful Information RawData Quantitative analysis is a scientific approach to managerial decision making whereby raw data are processed and manipulated resulting in meaningful information. Quantitative analysis provides data-driven analytical services for a range of business challenges, specializing in statistical models for site selection decisions. Examples: When to order additional new meterial? What is the total annual cost? What is the safety stock lavel? Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall, 2003, pp.2. IPO (Input-Process-Output) is one of the most fundamental design patterns. Input Process Output

  8. JUST TEXT SLIDE The Quantitative Analysis Approach CODE: 06 Definingthe Problem Developing a Model AcquiringInput Data Developinga Solution Testingthe Solution Analyzingthe Results Implementing the Results Ref: Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall, 2003, pp.3.

  9. STANDARD SLIDE CODE: 04 Quantitative & Qualitative Factors The data; may be quantitative, with values expressed numerically may be qualitative, with characteristics such as consumer preferences being tabulated. Quantitative factors might be different investment alternatives, interest rates, inventory levels, demand, or labor cost. Qualitative factorssuch as the weather, state and federal legislation, and technology breakthroughs should also be considered. Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall, 2003, pp.2

  10. DEFINITION SLIDE Statistics CODE: 09 A branch of mathematics taking and transforming numbers into useful information for decision makers. Statistics is the art of learning from data. Methods for processing & analyzing numbers. Methods for helping reduce the uncertainty inherent in decision making. Statistics refers to the body of techniques used for collecting, organizing, analyzing, and interpreting data. A statistic is a quantity that is calculated from a sample of data. It is used to give information about unknown values in the corresponding population.

  11. STANDARD SLIDE CODE: 04 Why Learn Statistics? • So you are able to make better sense of the ubiquitous use of numbers: • Business memos • Business research • Technical reports • Technical journals • Newspaper articles • Magazine articles • Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009. A Wojtek Kozak illustration.

  12. STANDARD SLIDE CODE: 04 Why Study Statistics? • Present and describe business data and information properly • Draw conclusions about large groups of individuals or items, using information collected from subsets of the individuals or items • Make reliable forecasts about a business activity • Improve business processes • Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009.

  13. LARGE VISUAL ELEMENT SLIDE Application Areas CODE: 05 • Engineering • Construction • Materials • Business • Consumer Preferences • Financial Trends • Quality • Economics • Forecasting • Demographics • Sports • Individual & Team Performance Statistical analysis of quantitative data is important throughout the pure and social sciences. For example, during this module we will consider examples from Biology, Medicine, Agriculture, Economics, Business and Meteorology. McClave J.T. et al., “A First Course in Business Statistics”, 8/e, Prentice Hall,2000.

  14. DEFINITION SLIDE Business Statistics CODE: 09 Business statistics is the science of good decision making in the face of uncertainty and is used in many disciplines such as financial analysis, econometrics, auditing, production and operations including services improvement, and marketing research. Statistics is used in business to help make better decisions by understanding the sources of variation and by uncovering patterns and relationships in business data. Ref: Kazmier L.J., “Business Statistics”, 4/E, Schaum’s Outline Series McGRAW-HILL, 2004. Wikipedia, http://en.wikipedia.org/wiki/Business_statistics.

  15. LARGE VISUAL ELEMENT SLIDE Types of Statistics CODE: 05 Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009. Statistics Descriptive Collecting,summarizing, and describing data Inferential Drawing conclusions and/or making decisions concerning a population based only on sample data

  16. STANDARD SLIDE CODE: 04 Descriptive Statistics • Descriptive statistic can be defined as collection, presentation, and characterization of a set of data in order to describe properly the various features of thatset of data. • Collect data • e.g., Survey • Present data • e.g., Tables and graphs • Characterize data • e.g., Sample mean = • Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009 • .

  17. STANDARD SLIDE CODE: 04 InferentialStatistics? • Inferential statistics can be defined as estimation of a characteristics of a population or the making of a decision concerning a population based only on sample results. • Estimation • e.g., Estimate the population mean weight using the sample mean weight • Hypothesis testing • e.g., Test the claim that the population mean weight is 120 pounds • Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009.

  18. DEFINITION SLIDE Data CODE: 09 Data are the different values associated with a variable. Data are observations (measurement, genders, survey responses) that have been collected.

  19. LARGE VISUAL ELEMENT SLIDE Types of Data CODE: 05 Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009. Examples: • Marital Status • Political Party • Eye Color (Defined categories) Examples: • Weight • Voltage (Measured characteristics) Examples: • Number of Children • Defects per hour (Counted items)

  20. LARGE VISUAL ELEMENT SLIDE Discrete Data CODE: 05 A set of data is said to be discrete if the values / observations belonging to it are distinct and separate, i.e. they can be counted (1,2,3,....). Examples: 3 - the number of kittens in a litter 2 – the number of patients in a doctors surgery 6 - the number of flaws in one metre of cloth (M, F) - gender (male, female) (O, A, B, AB) - blood group Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.

  21. LARGE VISUAL ELEMENT SLIDE Categorical Data CODE: 05 A set of data is said to be categorical if the values or observations belonging to it can be sorted according to category. Each value is chosen from a set of non-overlapping categories. Examples: Shoes in a cupboard can be sorted according to colour: the characteristic 'colour' can have non-overlapping categories 'black', 'brown', 'red' and 'other'. People have the characteristic of 'gender' with categories 'male' and 'female'. Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.

  22. LARGE VISUAL ELEMENT SLIDE Nominal Data CODE: 05 A set of data is said to be nominal if the values / observations belonging to it can be assigned a code in the form of a number where the numbers are simply labels. You can count but not order or measure nominal data. Examples: In a data set males could be coded as 0, females as 1. marital status of an individual could be coded as Y if married, N if single. Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.

  23. LARGE VISUAL ELEMENT SLIDE Ordinal Data CODE: 05 A set of data is said to be ordinal if the values / observations belonging to it can be ranked (put in order) or have a rating scale attached. You can count and order, but not measure, ordinal data. Examples: suppose a group of people were asked to taste varieties of biscuit and classify each biscuit on a rating scale of 1 to 5, representing strongly dislike, dislike, neutral, like, strongly like. A rating of 5 indicates more enjoyment than a rating of 4. Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.

  24. LARGE VISUAL ELEMENT SLIDE Interval Scale CODE: 05 An interval scale is a scale of measurement where the distance between any two adjacents units of measurement (or 'intervals') is the same but the zero point is arbitrary. Scores on an interval scale can be added and subtracted but can not be meaningfully multiplied or divided. Examples: The time interval between the starts of years 1981 and 1982 is the same as that between 1983 and 1984, namely 365 days. The zero point, year 1 AD, is arbitrary; time did not begin then. Other examples of interval scales include the heights of tides, and the measurement of longitude. Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary.

  25. DEFINITION SLIDE Variable CODE: 09 A variableis a characteristic of an item or individual. Variables Numerical (quantitative) variables have values that represent quantities. Categorical (qualitative) variables have values that can only be placed into categories, such as “yes” and “no.” Variables are either qualitative or quantitative. Qualitative variables have non-numeric outcomes, with no natural ordering. For example, gender, disease status, and type of car are all qualitative variables. Quantitative variables have numeric outcomes. For example, survival time, height, age, number of children, and number of faults are all quantitative variables.

  26. JUST TEXT SLIDE Quantitative variables CODE: 06 Quantitative variables can be discrete or continuous. Discrete random variables have outcomes which can take only a countable number of possible values. These possible values are usually taken to be integers, but don’t have to be. For example, number of children and number of faults are discrete random variables which take only integer values, but your score in a quiz where “half” marks are awarded is a discrete quantitative random variable which can take on non-integer values. Continuous random variables can take any value over some continuous scale. For example, survival time and height are continuous random variables. Often, continuous random variables are rounded to the nearest integer, but the are still considered to be continuous variables if there is an underlying continuous scale. Age is a good example of this.

  27. DEFINITION SLIDE Operational Definations CODE: 09 Data values are meaningless unless their variables have operational definitions,universally accepted meanings that are clear to all associated with an analysis. Ref: Berenson M.L. Et al., “Basic Business Statistics”, 11/E, Prentice Hall, 2009.

  28. DEFINITION SLIDE Population CODE: 09 A population is any entire collection of people, animals, plants or things from which we may collect data. It is the entire group we are interested in, which we wish to describe or draw conclusions about. Example The population for a study of infant health might be all children born in the UK in the 1980's. The sample might be all babies born on 7th May in any of the years. Ref: Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary

  29. DEFINITION SLIDE Census CODE: 09 A census is the collection of data from every member of the population. Ref: Triola M.F., “Elementary Statistics”, 9/e, Addison Wesley, 2005, p.4.

  30. DEFINITION SLIDE Sample CODE: 09 A sample is a group of units selected from a larger group (the population). By studying the sample it is hoped to draw valid conclusions about the larger group. A sample is generally selected for study because the population is too large to study in its entirety. The sample should be representative of the general population. This is often best achieved by random sampling. Also, before collecting the sample, it is important that the researcher carefully and completely defines the population, including a description of the members to be included. Example The population for a study of infant health might be all children born in the UK in the 1980's. The sample might be all babies born on 7th May in any of the years. Ref: Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary

  31. STANDARD SLIDE CODE: 04 Statistical Computer Programs • Minitab • SAS • SPSS • Microsoft Excel Example SPSS screen shot.

  32. STANDARD MULTIPLE-CHOICE QUESTION CODE: 11 What is the correct word(s) to fill in the blank in the sentence? Statistics isa branch of mathematics taking and transforming numbers into ________________ for decision makers. decision estimation useful information population qualitative factors c) useful information

  33. MULTIPLE-CHOICE QUESTION WITH SOLUTION SLIDE CODE: 10 DefiningtheProblem Developing a Model AcquiringInputData DevelopingaSolution Testingthe Solution AnalyzingtheResults Implementing the Results Which one of the following is one of the steps of Quantitative Analysis approach? Defining the problem Developing a model Acquiring input data Developing a solution All e) All All choise are related to qauantitative analysis approach. Lets remember:

  34. MATCHING QUESTION SLIDE CODE: 12 Select the best match for each defination. A) A characteristic of an item or individual 1) Data B) Taking and transforming numbers into useful information for decision makers. 2) Population C) Set of observations that have been collected 3) Sample D) Any entire collection of people, animals, plants or things from which we may collect data. 4) Variable E) A group of units selected from a larger group 5) Statistics 1C, 2D, 3E, 4A, 5B

  35. STANDARD MULTIPLE-CHOICE QUESTION CODE: 11 Which of the following is not a statistical computer programs? Minitab SAS SPSS Microsoft Word Microsoft Excel d) Microsoft Word

  36. CONCLUSION SLIDE Conclusion CODE: 14 In this session, we have: Defined quantitative methods Defined statictics Distinguished descriptive & inferentialstatistics Defined basic statistical terms Defined levels of measurement Defined types of data References Berenson M.L., Levine D.K. & Krehbiel T.C., “Basic Business Statistics”, 11/e, Prentice Hall, 2009. Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary/ Kazmier L.J., “Business Statistics”, 4/E, Schaum’s Outline Series McGRAW-HILL, 2004. McClave J.T., Benson P.G. & Sincich T., “A First Course in Business Statistics”, 8/e, Prentice Hall, 2000. Render B., Stair R.M. & Hanna M.E., “Quantitative Analysis for Management”, 8/e, Prentice Hall, 2003. Triola M.F., “Elementary Statistics”, 9/e, Addison Wesley, 2005. Wikipedia, http://en.wikipedia.org/wiki/Business_statistics

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