1. Learning Objectives In this chapter, you will learn:  How statistics is used in business  The sources of data used in business  The types of data used in business  The basics of Microsoft Excel

2. Why Study Statistics? Decision Makers Use Statistics To:  Present and describe business data and information properly  Draw conclusions about large populations, using information collected from samples  Make reliable forecasts about a business activity  Improve business processes

3. Types of Statistics  Statistics  The branch of mathematics that transforms data into useful information for decision makers. Descriptive Statistics Collecting, summarizing, and describing data Inferential Statistics Drawing conclusions and/or making decisions concerning a population based only on sample data

4.  Specific number numerical measurement determined by a set of data Example: Twenty-three percent of people polled believed that there are too many polls. Statistics

5. Descriptive Statistics  Collect data  ex. Survey  Present data  ex. Tables and graphs  Characterize data  ex. Sample mean =

6. Inferential Statistics  Estimation  ex. Estimate the population mean weight using the sample mean weight  Hypothesis testing  ex. Test the claim that the population mean weight is 120 pounds Drawing conclusions and/or making decisions concerning a population based on sample results.

7.  Method of analysis a collection of methods for planning experiments, obtaining data, and then then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data Statistics

8. Basic Vocabulary of Statistics VARIABLE A variable is a characteristic of an item or individual. DATA Data are the different values associated with a variable. OPERATIONAL DEFINITIONS Variable values are meaningless unless their variables have operational definitions, universally accepted meanings that are clear to all associated with an analysis.

9. Basic Vocabulary of Statistics POPULATION A population consists of all the items or individuals about which you want to draw a conclusion. SAMPLE A sample is the portion of a population selected for analysis. PARAMETER A parameter is a numerical measure that describes a characteristic of a population. STATISTIC A statistic is a numerical measure that describes a characteristic of a sample.

10. Population vs. Sample PopulationSample Measures used to describe the population are called parameters Measures computed from sample data are called statistics

11. Why Collect Data?  A marketing research analyst needs to assess the effectiveness of a new television advertisement.  A pharmaceutical manufacturer needs to determine whether a new drug is more effective than those currently in use.  An operations manager wants to monitor a manufacturing process to find out whether the quality of product being manufactured is conforming to company standards.  An auditor wants to review the financial transactions of a company in order to determine whether the company is in compliance with generally accepted accounting principles.

12. Sources of Data  Primary Sources: The data collector is the one using the data for analysis  Data from a political survey  Data collected from an experiment  Observed data  Secondary Sources: The person performing data analysis is not the data collector  Analyzing census data  Examining data from print journals or data published on the internet.

13. Types of Variables  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.

14. Types of Variables Data CategoricalNumerical DiscreteContinuous Examples: Marital Status Political Party Eye Color (Defined categories) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics)

15. Levels of Measurement  A nominal scale classifies data into distinct categories in which no ranking is implied. Categorical Variables Categories Personal Computer Ownership Type of Stocks Owned Internet Provider Yes / No Microsoft Network / AOL GrowthValueOther

16. Levels of Measurement  An ordinal scale classifies data into distinct categories in which ranking is implied Categorical Variable Ordered Categories Student class designationFreshman, Sophomore, Junior, Senior Product satisfactionSatisfied, Neutral, Unsatisfied Faculty rankProfessor, Associate Professor, Assistant Professor, Instructor Standard & Poor’s bond ratingsAAA, AA, A, BBB, BB, B, CCC, CC, C, DDD, DD, D Student GradesA, B, C, D, F

17. Levels of Measurement  An interval scale is an ordered scale in which the difference between measurements is a meaningful quantity but the measurements do not have a true zero point.  A ratio scale is an ordered scale in which the difference between the measurements is a meaningful quantity and the measurements have a true zero point.

18. Interval and Ratio Scales

19.  Discrete data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3,...  Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps 2 3

20.  Discrete The number of eggs that hens lay; for example, 3 eggs a day.

21.  Discrete The number of eggs that hens lay; for example, 3 eggs a day.  Continuous The amounts of milk that cows produce; for example, 2.343115 gallons a day. Definitions

22. Microsoft Excel Terms  When you use Microsoft Excel, you place the data you have collected in worksheets.  The intersections of the columns and rows of worksheets form boxes called cells.  If you want to refer to a group of cells that forms a contiguous rectangular area, you can use a cell range.  Worksheets exist inside a workbook, a collection of worksheets and other types of sheets, including chart sheets that help visualize data.

23. Designing Effective Worksheets  You should associate column cell ranges with variables.  You do not skip any rows as you enter data, so column cell ranges will never contain any empty cells.  Place all the variables on a worksheet that is separate from the worksheet containing the statistical results.  Allow the user to be able to explicitly see the chain of calculations from the starting data.  Create two copies of your worksheets: one optimized for the screen, the other for the printer.

24. Stem-and Leaf Plot Raw Data (Test Grades) 67 72 85 75 89 89 88 90 99 100 6 7 8 9 10 7 2 5 5 8 9 9 0 9 0 StemLeaves

25. Example: Create a Stem and Leaf Plot for the following data which represents ages of CEO's: 53 45 41 36 55 50 37 48 52 62 43 46 39 50 52 61 48 37 48 55 59 52 39 41 50 The TI-83 will not create the Stem and Leaf Plot for you completely, but it will allow you to sort the data which makes creating the chart by hand easy. Here is what to do:

26. 1.Enter the data into a free list (use L1 if it is available). Recall that you do this by hitting STAT, then 1 for Edit and clear L1 if necessary. After you have entered the data into L1 the screen should look like this:

27. 2. Now hit 2nd MODE for quit to get to the homescreen. Now hit STAT to get this screen: 3. Now select 2 to get SortA(which stands for Sort Ascending). Your screen will look like this:

28. 4. Enter the list you wish to sort in this case L1 (hit 2nd 1). Your screen looks like this 5. Now hit enter, the screen will say done. Hit Stat then edit to get back to the editor. Your data should be sorted. Here is what the screen should look like:

29. Stem Leaf 3 3 67799 41135 46888 500222355 59 612

30. Definitions  Median  the middle value when the original data values are arranged in order of increasing (or decreasing) magnitude

31. Definitions  Median  the middle value when the original data values are arranged in order of increasing (or decreasing) magnitude  often denoted by x (pronounced ‘x-tilde’) ~

32. Definitions  Median  the middle value when the original data values are arranged in order of increasing (or decreasing) magnitude  often denoted by x (pronounced ‘x-tilde’)  is not affected by an extreme value ~

33. 6.72 3.46 3.606.44 3.46 3.60 6.446.72 no exact middle -- shared by two numbers 3.60 + 6.44 2 (even number of values) MEDIAN is 5.02

34. 6.72 3.46 3.606.44 26.70 3.46 3.60 6.446.72 26.70 (in order - odd number of values) exact middle MEDIAN is 6.44 6.72 3.46 3.606.44 3.46 3.60 6.446.72 no exact middle -- shared by two numbers 3.60 + 6.44 2 (even number of values) MEDIAN is 5.02

35. Qualitative vs Quantitative Number of students who turn a paper in late. Sex of the next baby born in a hospital. Amount of fluid in a machine to fill bottles of soda pop. Brand of a personal computer. Zip Codes.

36. Discrete vs Continuous Price of a textbook. The length of a new born baby. The number of bad checks received by a store. Concentration of a contaminant in a solution. Actual weight of a 1-lb can of coffee.

37. Measures of Position Quartiles, Deciles, Percentiles

38. Quartiles Q 1, Q 2, Q 3

39. Quartiles Q 1, Q 2, Q 3 divides ranked scores into four equal parts 25% Q3Q3 Q2Q2 Q1Q1

40. Deciles D 1, D 2, D 3, D 4, D 5, D 6, D 7, D 8, D 9 divides ranked data into ten equal parts

41. D 1, D 2, D 3, D 4, D 5, D 6, D 7, D 8, D 9 divides ranked data into ten equal parts Deciles 10% D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 D 9

42. Quartiles Q 1 = P 25 Q 2 = P 50 Q 3 = P 75

43.  Range The difference between the highest and lowest score  Interquartile Range (or IQR): Q 3 - Q 1

44.  Semi-interquartile Range: (Q 3 - Q 1 )/2  Midquartile: (Q 1 + Q 3 )/2  10 - 90 Percentile Range: P 90 - P 10  Midrange: (smallest + largest)/2

45. Finding the Percentile of a Given Score Percentile of score x = 100 number of scores less than x total number of scores

46. Finding the Score Given a Percentile n total number of values in the data set k percentile being used R locator that gives the position of a value P k k th percentile R = n k 100

47. Finding the Value of the kth Percentile Sort the data. (Arrange the data in order of lowest to highest.) The value of the kth percentile is midway between the Lth value and the next value in the sorted set of data. Find P k by adding the L th value and the next value and dividing the total by 2. Start Compute L = n where n = number of values k = percentile in question ) ( k 100 Change L by rounding it up to the next larger whole number. The value of P k is the Lth value, counting from the lowest Is L a whole number ? Yes No

48. Stem-and Leaf Plot Raw Data (Test Grades) 67 72 85 75 89 89 88 90 99 100 Find P 50 P 50 = Find P 33 P 33 = P 50 = the mean of the 5 th and 6 th score or 88.5 score or 88.5 P 33 = round up to 4, the fourth score is 85 6 7 8 9 10 7 2 5 5 8 9 9 0 9 0 StemLeaves

49. Stem-and Leaf Plot Raw Data 16 19 22 23 24 25 26 27 28 28 29 30 31 31 34 Find P 50 P 50 = Find P 30 P 30 = P 50 = round up to 8, the eight score is 27 score is 27 P 30 = round up to 5, the fifth score is 24 1 2 3 6 9 2 3 4 5 6 7 8 8 9 0 1 1 4 StemLeaves

50. Stem-and Leaf Plot Raw Data 16 19 22 23 24 25 26 27 28 28 29 30 31 31 34 1 2 3 6 9 2 3 4 5 6 7 8 8 9 0 1 1 4 StemLeaves What percentile is 30? 11/15 = 73 rd percentile What percentile is 22? 2/15 = 13 th percentile

51. Chapter Summary  Reviewed why a manager needs to know statistics  Introduced key definitions:  Population vs. Sample  Primary vs. Secondary data types  Categorical vs. Numerical data  Examined descriptive vs. inferential statistics  Reviewed data types and measurement levels  Discussed Microsoft Excel terms and tips In this chapter, we have