Managerial Decision Making

1. MBA / 510 Managerial Decision Making Facilitator: René Cintrón

2. Syllabus & Expectations • Syllabus ◊ • Expectations • On-Time • Participation • Grades • Communication • Professionalism / Etiquette • APA Style ◊ • Bloom’s Taxonomy ◊

3. Decision Making • What is decision making? • Why do we make decisions? • Who makes decisions? • When do we make decisions? • How do we make decisions? • Personal / Professional / Other •

4. Week 1 - Objectives • Distinguish among what is knowable, unknowable, and researchable • Distinguish between secondary and primary research • Identify tools of data analysis • Describe the different levels of measurement • Explain the concepts of validity and reliability of data • Distinguish among sampling methods.

5. Research Defined • Why Study Business Research? • What is Research? • What is Good Research? • Value of Acquiring Research Skills • Manager-Researcher Relationship • Understanding theory: components and connections

6. Understanding theory: components and connections • Concepts • Constructs • Definitions • Variables • Propositions • Hypotheses

7. Types of Research • Primary • Interviews • Questionnaires • Observations • Secondary • Literature / Publications • Other Media • Non-human sources

8. Sources of Knowledge • Empiricists attempt to describe, explain, and make predictions through observation • Rationalists believe all knowledge can be deduced from known laws or basic truths of nature • Authorities serve as important sources of knowledge, but should be judged on integrity and willingness to present a balanced case

9. Thought process: Sound Reasoning Types of Discourse Exposition Argument Deduction Induction

10. Thought process: Deductive Reasoning Inner-city household interviewing is especially difficult and expensive This survey involves substantial inner-city household interviewing The interviewing in this survey will be especially difficult and expensive

11. Thought process: Inductive Reasoning Why didn’t sales increase during our promotional event? • Regional retailers did not have sufficient stock to fill customer requests during the promotional period • A strike by employees prevented stock from arriving in time for promotion to be effective • A hurricane closed retail outlets in the region for 10 days during the promotion

12. Direct observation Clearly defined variables Clearly defined methods Empirically testable Elimination of alternatives Statistical justification Self-correcting process The Scientific Method

13. Tools of Data Analysis • Descriptive and inferential statistics • Statistics, graphics, and ethics • Constructing a frequency distribution • Software example • Graphic presentation of a frequency distribution • Other graphic presentations of data

14. Descriptive Statistics: Methods of organizing, summarizing, and presenting data in an informative way. Types of Statistics EXAMPLE 1: A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer. EXAMPLE 2: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2001. The statistic 9 describes the number of problems out of every 100 machines.

15. Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample. Types of Statistics APopulationis aCollectionof all possible individuals, objects, or measurements of interest. ASampleis a portion, or part, of the population of interest

16. Types of Statistics(examples of inferential statistics) Example 2: Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale. Example 1: TV networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers. Example 3: The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.

17. For a Qualitative Variable the characteristic being studied is nonnumeric. Types of Variables

18. For a Qualitative Variable the characteristic being studied is nonnumeric. Frequency Table

19. In a Quantitative Variable information is reported numerically. Balance in your checking account Minutes remaining in class Number of children in a family Types of Variables

20. In a Quantitative Variable information is reported numerically. • Histograms • Stem and Leaf • Box plots • XY Scatter Charts (2 variables) • Line Graphs

21. Line Graph Exercise • The expenditures on research and development for the Hennen Manufacturing Company are given • Construct a simple line graph • Analyze the results of the graph • Estimate 2004’s expenses

22. Line Graph

23. Constructing a Frequency Distribution A Frequency Distribution is a grouping of data into mutually exclusive categories showing the number of observations in each class.

24. Constructing a frequency distribution involves: Drawing conclusions Presenting data (graph) Organizing data (frequency distribution) Collecting raw data Determining the question to be addressed

25. Software Commands

26. Graphic Presentations of Data • Line charts ◊ • Bar charts ◊ • Pie charts ◊ • Dot plots ◊ • Skewness ◊

27. Levels of Measurement There are four levels of data Nominal Ordinal Interval Ratio

28. Nominal data Nominal level Data that is classified into categories and cannot be arranged in any particular order.

29. Nominal level variables must be: Mutually exclusive An individual, object, or measurement is included in only one category. Exhaustive Each individual, object, or measurement must appear in one of the categories.

30. Ordinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless. During a taste test of 4 soft drinks, Coca Cola was ranked number 1, Dr. Pepper number 2, Pepsi number 3, and Root Beer number 4.

31. Interval level Similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point. Temperature on the Fahrenheit scale.

32. Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.

33. Validity • Content Validity • Concurrent Validity • Construct Validity

34. Reliability • Stability • Test-retest Equivalence • Parallel forms • Internal Consistency • Split-half • KR20 • Cronbach’s alpha

35. Sampling Methods • Reasons to sample • Simple random sampling • Systematic random sampling • Stratified random sampling • Cluster sampling

36. Why Sample the Population? The physical impossibility of checking all items in the population. The time-consuming aspect of contacting the whole population. The cost of studying all the items in a population. The adequacy of sample results in most cases. The destructive nature of certain tests.

37. Probability Sampling Methods Aprobability sampleis a sample selected such that each item or person in the population being studied has a known likelihood of being included in the sample. Simple Random SampleA sample formulated so that each item or person in the population has the same chance of being included. Systematic Random SamplingThe items or individuals of the population are arranged in some order. A random starting point is selected and then every kth member of the population is selected for the sample.

38. Methods of Probability Sampling Stratified Random Sampling: A population is first divided into subgroups, called strata, and a sample is selected from each stratum.

39. Cluster Sampling Cluster Sampling: A population is first divided into primary units then samples are selected from the primary units.

40. Methods of Probability Sampling In nonprobability sample inclusion in the sample is based on the judgment of the person selecting the sample. The sampling error is the difference between a sample statistic and its corresponding population parameter. The sampling distribution of the sample mean is a probability distribution consisting of all possible sample means of a given sample size selected from a population.

41. Next Week • Analyze data using descriptive statistics • Population mean (Lind Chapter 3) • Sample mean (Lind Chapter 3) • Weighted mean (Lind Chapter 3) • Median (Lind Chapter 3) • Mode (Lind Chapter 3) • Variance and standard deviation (Lind Chapter 3) • Empirical rule (Lind Chapter 3)

42. Next Week • Apply basic probability concepts to facilitate business decision making • What is a probability? (Lind Chapter 5) • Approaches to assigning probabilities (Lind Chapter 5) • Some rules for computing probabilities (Lind Chapter 5) • Rules of multiplication • Contingency tables (Lind Chapter 5)

43. Next Week • Distinguish between discrete and continuous probability distributions • Discrete probability distributions (Lind Chapter 6) • What is a probability distribution? • Random variables • Discrete random variable • Mean, variance, and standard deviation of a probability distribution • Continuous probability distributions (Lind Chapter 7) • Continuous random variable

44. Next Week • Apply the normal distribution to facilitate business decision making • Family of normal probability distributions (Lind Chapter 7) • Standard normal distribution (Lind Chapter 7) • Empirical rule (Lind Chapter 7) • Finding areas under the normal curve (Lind Chapter 7)