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

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

Objectives 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. INTRODUCTION SLIDE CODE: 02

Course Outline 1.Introduction to quantitative methods 2.Presenting data 3.Central tendency 4.Dispersion of the data 5.Probability 6.Distribution 7.Distribution 8.Sampling theory 9.Confidence intervals 10.Hypothesis theory 11.Hypothesis theory 12.Regression analysis 13.Time series INTRODUCTION SLIDE CODE: 02

References 1.Berenson M.L., Levine D.K. & Krehbiel T.C., “Basic Business Statistics”, 11/e, Prentice Hall, 2009. 2.Burton G., Carrol G. & Wall S., “Quantitative Methods for Business & Economics”, Prentice Hall, 2/e, 2002. 3.Easton & Coll, Statistics Glossary, http://www.stats.gla.ac.uk/steps/glossary/inde x.html 4.Kay D., “CliffsAP Statistics”, Wiley Publishing, 2005. 5.Kazmier L.J., “Business Statistics”, 4/E, Schaum’s Outline Series McGRAW-HILL, 2004. 6.McClave J.T., Benson P.G. & Sincich T., “A First Course in Business Statistics”, 8/e, Prentice Hall, 2000. 7.Render B., Stair R.M. & Hanna M.E., “Quantitative Analysis for Management”, 8/e, Prentice Hall, 2003. 8.Tanis E.A., “Statistics I:Descriptive Statistics and Probability”, HJB, 1987. 9.Triola M.F., “Elementary Statistics”, 9/e, Addison Wesley, 2005. 10.Wates D., “Quantitative Methods for Business”, 4/e, Prentice Hall, 2008. 11.Wikipedia, http://en.wikipedia.org/ INTRODUCTION SLIDE CODE: 02

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

Learing Objectives In this session, you will learn about Presenting Data and Graphical Displays. After reading this session, you should be able to: 1.What is quantitative analysis? 2.How statistics is used in business 3.Types of statistics 4.The vocabulary of statistics INTRODUCTION SLIDE CODE: 02

Quantitative Analysis 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE Meaningful Information Quantitative Analysis Raw Data Input Process Output

The Quantitative Analysis Approach Ref: Render B. et al, “Quantitative Analysis for Management”, 8th ed., Prentice Hall, 2003, pp.3. JUST TEXT SLIDE CODE: 06 Implementing the Results Analyzing the Results Testing the Solution Developing a Solution Acquiring Input Data Developing a Model Defining the Problem

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 factors such 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 STANDARD SLIDE CODE: 04

Statistics 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. CODE: 09 DEFINITION SLIDE

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. STANDARD SLIDE CODE: 04 A Wojtek Kozak illustration.

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. STANDARD SLIDE CODE: 04

Application Areas 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE Economics Forecasting Demographics Sports Individual & Team Performance Engineering Construction Materials Business Consumer Preferences Financial Trends Quality

Business Statistics 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. CODE: 09 DEFINITION SLIDE

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

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. STANDARD SLIDE CODE: 04

Inferential Statistics? 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. STANDARD SLIDE CODE: 04

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

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

Discrete Data 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE

Categorical Data 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE

Nominal Data 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE

Ordinal Data 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE

Interval Scale 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. CODE: 05 LARGE VISUAL ELEMENT SLIDE

Variable A variable is a characteristic of an item or individual. CODE: 09 DEFINITION SLIDE Categorical (qualitative) variables have values that can only be placed into categories, such as “yes” and “no.” Numerical (quantitative) variables have values that represent quantities. Variables 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.

Quantitative variables 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. JUST TEXT SLIDE CODE: 06

Operational Definations 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. CODE: 09 DEFINITION SLIDE

Population 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 CODE: 09 DEFINITION SLIDE

Census 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. CODE: 09 DEFINITION SLIDE

Sample 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 CODE: 09 DEFINITION SLIDE

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

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

Which one of the following is one of the steps of Quantitative Analysis approach? a)Defining the problem b)Developing a model c)Acquiring input data d)Developing a solution e)All All choise are related to qauantitative analysis approach. Lets remember: MULTIPLE-CHOICE QUESTION WITH SOLUTION SLIDE CODE: 10 Implementing the Results Analyzing the Results Testing the Solution Developing a Solution Acquiring Input Data Developing a Model Defining the Problem

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

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

Conclusion In this session, we have: 1.Defined quantitative methods 2.Defined statictics 3.Distinguished descriptive & inferential statistics 4.Defined basic statistical terms 5.Defined levels of measurement 6.Defined types of data CONCLUSION SLIDE CODE: 14 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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