1. 1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach

2. 2 2 Slide IS 310 – Business Statistics Why Study Statistics? Because, you would like to know: 1. How does an instructor grade on a curve 2. How does a tire manufacturer determine mileage warranty 3. How does FDA verify that a new drug is more effective than the present drug 4. What does it mean when one says the median home price in southern California is $420,000 5. How does one select a sample for a survey

3. 3 3 Slide IS 310 – Business Statistics What is Statistics? Statistics is a field of study that deals with collection, organization, presentation, analysis and interpretation of data.

4. 4 4 Slide IS 310 – Business Statistics Applications in Business and Economics n Accounting n Economics Public accounting firms use statistical sampling procedures when conducting audits for their clients. Economists use statistical information in making forecasts about the future of the economy or some aspect of it.

5. 5 5 Slide IS 310 – Business Statistics Applications in Business and Economics A variety of statistical quality control charts are used to monitor the output of a production process. n Production Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. n Marketing

6. 6 6 Slide IS 310 – Business Statistics Applications in Business and Economics Financial advisors use price-earnings ratios and dividend yields to guide their investment recommendations. Finance Finance

7. 7 7 Slide IS 310 – Business Statistics Data and Data Sets n Data are the facts and figures collected, summarized, analyzed, and interpreted. analyzed, and interpreted. The data collected in a particular study are referred The data collected in a particular study are referred to as the data set. to as the data set.

8. 8 8 Slide IS 310 – Business Statistics The elements are the entities on which data are The elements are the entities on which data are collected. collected. A variable is a characteristic of interest for the elements. A variable is a characteristic of interest for the elements. The set of measurements collected for a particular The set of measurements collected for a particular element is called an observation. element is called an observation. The total number of data values in a complete data The total number of data values in a complete data set is the number of elements multiplied by the set is the number of elements multiplied by the number of variables. number of variables. Elements, Variables, and Observations

9. 9 9 Slide IS 310 – Business Statistics Stock Annual Earn/ Stock Annual Earn/ Exchange Sales($M) Share($) Data, Data Sets, Elements, Variables, and Observations Company Dataram Dataram EnergySouth EnergySouth Keystone Keystone LandCare LandCare Psychemedics Psychemedics NQ 73.10 0.86 NQ 73.10 0.86 N 74.00 1.67 N 74.00 1.67 N365.70 0.86 N365.70 0.86 NQ111.40 0.33 NQ111.40 0.33 N 17.60 0.13 N 17.60 0.13 Variables Element Names Names Data Set Observation

10. 10 Slide IS 310 – Business Statistics Scales of Measurement The scale indicates the data summarization and The scale indicates the data summarization and statistical analyses that are most appropriate. statistical analyses that are most appropriate. The scale indicates the data summarization and The scale indicates the data summarization and statistical analyses that are most appropriate. statistical analyses that are most appropriate. The scale determines the amount of information The scale determines the amount of information contained in the data. contained in the data. The scale determines the amount of information The scale determines the amount of information contained in the data. contained in the data. Scales of measurement include: Scales of measurement include: Nominal Ordinal Interval Ratio

11. 11 Slide IS 310 – Business Statistics Scales of Measurement n Nominal A nonnumeric label or numeric code may be used. A nonnumeric label or numeric code may be used. Data are labels or names used to identify an Data are labels or names used to identify an attribute of the element. attribute of the element. Data are labels or names used to identify an Data are labels or names used to identify an attribute of the element. attribute of the element.

12. 12 Slide IS 310 – Business Statistics Example: Example: Students of a university are classified by the Students of a university are classified by the school in which they are enrolled using a school in which they are enrolled using a nonnumeric label such as Business, Humanities, nonnumeric label such as Business, Humanities, Education, and so on. Education, and so on. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and 2 denotes Humanities, 3 denotes Education, and so on). so on). Example: Example: Students of a university are classified by the Students of a university are classified by the school in which they are enrolled using a school in which they are enrolled using a nonnumeric label such as Business, Humanities, nonnumeric label such as Business, Humanities, Education, and so on. Education, and so on. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and 2 denotes Humanities, 3 denotes Education, and so on). so on). Scales of Measurement n Nominal

13. 13 Slide IS 310 – Business Statistics Scales of Measurement n Ordinal A nonnumeric label or numeric code may be used. A nonnumeric label or numeric code may be used. The data have the properties of nominal data and The data have the properties of nominal data and the order or rank of the data is meaningful. the order or rank of the data is meaningful. The data have the properties of nominal data and The data have the properties of nominal data and the order or rank of the data is meaningful. the order or rank of the data is meaningful.

14. 14 Slide IS 310 – Business Statistics Scales of Measurement n Ordinal Example: Example: Students of a university are classified by their Students of a university are classified by their class standing using a nonnumeric label such as class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Freshman, 2 denotes Sophomore, and so on). Example: Example: Students of a university are classified by their Students of a university are classified by their class standing using a nonnumeric label such as class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Freshman, 2 denotes Sophomore, and so on).

15. 15 Slide IS 310 – Business Statistics Scales of Measurement n Interval Interval data are always numeric. Interval data are always numeric. The data have the properties of ordinal data, and The data have the properties of ordinal data, and the interval between observations is expressed in the interval between observations is expressed in terms of a fixed unit of measure. terms of a fixed unit of measure. The data have the properties of ordinal data, and The data have the properties of ordinal data, and the interval between observations is expressed in the interval between observations is expressed in terms of a fixed unit of measure. terms of a fixed unit of measure.

16. 16 Slide IS 310 – Business Statistics Scales of Measurement n Interval Example: Example: Melissa has an SAT score of 1205, while Kevin Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 has an SAT score of 1090. Melissa scored 115 points more than Kevin. points more than Kevin. Example: Example: Melissa has an SAT score of 1205, while Kevin Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 has an SAT score of 1090. Melissa scored 115 points more than Kevin. points more than Kevin.

17. 17 Slide IS 310 – Business Statistics Scales of Measurement n Ratio The data have all the properties of interval data The data have all the properties of interval data and the ratio of two values is meaningful. and the ratio of two values is meaningful. The data have all the properties of interval data The data have all the properties of interval data and the ratio of two values is meaningful. and the ratio of two values is meaningful. Variables such as distance, height, weight, and time Variables such as distance, height, weight, and time use the ratio scale. use the ratio scale. Variables such as distance, height, weight, and time Variables such as distance, height, weight, and time use the ratio scale. use the ratio scale. This scale must contain a zero value that indicates This scale must contain a zero value that indicates that nothing exists for the variable at the zero point. that nothing exists for the variable at the zero point. This scale must contain a zero value that indicates This scale must contain a zero value that indicates that nothing exists for the variable at the zero point. that nothing exists for the variable at the zero point.

18. 18 Slide IS 310 – Business Statistics Scales of Measurement n Ratio Example: Example: Melissa’s college record shows 36 credit hours Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned. Kevin has twice as many credit hours earned as Melissa. hours earned as Melissa. Example: Example: Melissa’s college record shows 36 credit hours Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned. Kevin has twice as many credit hours earned as Melissa. hours earned as Melissa.

19. 19 Slide IS 310 – Business Statistics Data can be further classified as being qualitative Data can be further classified as being qualitative or quantitative. or quantitative. Data can be further classified as being qualitative Data can be further classified as being qualitative or quantitative. or quantitative. The statistical analysis that is appropriate depends The statistical analysis that is appropriate depends on whether the data for the variable are qualitative on whether the data for the variable are qualitative or quantitative. or quantitative. The statistical analysis that is appropriate depends The statistical analysis that is appropriate depends on whether the data for the variable are qualitative on whether the data for the variable are qualitative or quantitative. or quantitative. In general, there are more alternatives for statistical In general, there are more alternatives for statistical analysis when the data are quantitative. analysis when the data are quantitative. In general, there are more alternatives for statistical In general, there are more alternatives for statistical analysis when the data are quantitative. analysis when the data are quantitative. Qualitative and Quantitative Data

20. 20 Slide IS 310 – Business Statistics Qualitative Data Labels or names used to identify an attribute of each Labels or names used to identify an attribute of each element element Labels or names used to identify an attribute of each Labels or names used to identify an attribute of each element element Often referred to as categorical data Often referred to as categorical data Use either the nominal or ordinal scale of Use either the nominal or ordinal scale of measurement measurement Use either the nominal or ordinal scale of Use either the nominal or ordinal scale of measurement measurement Can be either numeric or nonnumeric Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited Appropriate statistical analyses are rather limited

21. 21 Slide IS 310 – Business Statistics Quantitative Data Quantitative data indicate how many or how much: Quantitative data indicate how many or how much: discrete, if measuring how many discrete, if measuring how many continuous, if measuring how much continuous, if measuring how much Quantitative data are always numeric. Quantitative data are always numeric. Ordinary arithmetic operations are meaningful for Ordinary arithmetic operations are meaningful for quantitative data. quantitative data. Ordinary arithmetic operations are meaningful for Ordinary arithmetic operations are meaningful for quantitative data. quantitative data.

22. 22 Slide IS 310 – Business Statistics Scales of Measurement QualitativeQualitativeQuantitativeQuantitative NumericalNumerical NumericalNumerical Non-numericalNon-numerical DataData NominalNominalOrdinalOrdinalNominalNominalOrdinalOrdinalIntervalIntervalRatioRatio

23. 23 Slide IS 310 – Business Statistics Cross-Sectional Data Cross-sectional data are collected at the same or Cross-sectional data are collected at the same or approximately the same point in time. approximately the same point in time. Cross-sectional data are collected at the same or Cross-sectional data are collected at the same or approximately the same point in time. approximately the same point in time. Example: data detailing the number of building Example: data detailing the number of building permits issued in June 2007 in each of the counties permits issued in June 2007 in each of the counties of Ohio of Ohio Example: data detailing the number of building Example: data detailing the number of building permits issued in June 2007 in each of the counties permits issued in June 2007 in each of the counties of Ohio of Ohio

24. 24 Slide IS 310 – Business Statistics Time Series Data Time series data are collected over several time Time series data are collected over several time periods. periods. Time series data are collected over several time Time series data are collected over several time periods. periods. Example: data detailing the number of building Example: data detailing the number of building permits issued in Lucas County, Ohio in each of permits issued in Lucas County, Ohio in each of the last 36 months the last 36 months Example: data detailing the number of building Example: data detailing the number of building permits issued in Lucas County, Ohio in each of permits issued in Lucas County, Ohio in each of the last 36 months the last 36 months

25. 25 Slide IS 310 – Business Statistics Data Sources n Existing Sources Within a firm – almost any department Business database services – Dow Jones & Co. Government agencies - U.S. Department of Labor Industry associations – Travel Industry Association of America of America Special-interest organizations – Graduate Management Admission Council Admission Council Internet – more and more firms

26. 26 Slide IS 310 – Business Statistics n Statistical Studies Data Sources In experimental studies the variable of interest is first identified. Then one or more other variables are identified and controlled so that data can be obtained about how they influence the variable of interest. In experimental studies the variable of interest is first identified. Then one or more other variables are identified and controlled so that data can be obtained about how they influence the variable of interest. In observational (nonexperimental) studies no In observational (nonexperimental) studies no attempt is made to control or influence the attempt is made to control or influence the variables of interest. variables of interest. In observational (nonexperimental) studies no In observational (nonexperimental) studies no attempt is made to control or influence the attempt is made to control or influence the variables of interest. variables of interest. a survey is a good example

27. 27 Slide IS 310 – Business Statistics Data Acquisition Considerations Time Requirement Cost of Acquisition Data Errors Data Errors Searching for information can be time consuming. Searching for information can be time consuming. Information may no longer be useful by the time it Information may no longer be useful by the time it is available. is available. Organizations often charge for information even Organizations often charge for information even when it is not their primary business activity. when it is not their primary business activity. Using any data that happen to be available or were Using any data that happen to be available or were acquired with little care can lead to misleading acquired with little care can lead to misleading information. information.

28. 28 Slide IS 310 – Business Statistics Descriptive Statistics n Descriptive statistics are the tabular, graphical, and numerical methods used to summarize and present data.

29. 29 Slide IS 310 – Business Statistics Example: Hudson Auto Repair The manager of Hudson Auto would like to have a better understanding of the cost of parts used in the engine tune-ups performed in the shop. She examines 50 customer invoices for tune-ups. The costs of parts, rounded to the nearest dollar, are listed on the next slide.

30. 30 Slide IS 310 – Business Statistics Example: Hudson Auto Repair Example: Hudson Auto Repair n Sample of Parts Cost ($) for 50 Tune-ups

31. 31 Slide IS 310 – Business Statistics Inferential Statistics Inferential Statistics involves analyzing a set of data to make conclusions. This branch of statistics is more difficult than Descriptive Statistics. In the study of Inferential Statistics, two basic concepts are important: o Population o Population o Sample o Sample

32. 32 Slide IS 310 – Business Statistics Population and Sample Population refers to all possible subjects for a given study. Sample refers to part (subset) of a population.

33. 33 Slide IS 310 – Business Statistics Population and Sample n Let’s take a few examples. n Example 1 n We are interested in knowing the proportion of CSULB students are in favor of legalizing the use of marijuana. n Population consists of all CSULB students. n Sample is 250 students selected at random.

34. 34 Slide IS 310 – Business Statistics Population and Sample n Example 2 n We want to know what percentage of Los Angeles County residents are supportive of a half-percent increase in sales tax. n Population consists of all Los Angeles County residents who are at least 18 years old. n Sample is 1000 Los Angeles County residents selected randomly.

35. 35 Slide IS 310 – Business Statistics Population and Sample n Example 3 n We want to test if a new brand of tires manufactured by Goodyear is better than existing tires. n Population consists of all tires of the new brand manufactured by Goodyear. n Sample is 100 tires of the new brand chosen at random.

36. 36 Slide IS 310 – Business Statistics Population and Sample n Example 4 n We would like to know if a new perfume will be preferred by American women over 35 years. n Population consists of all American women who are over 35 years. n Sample is 500 American women of over 35 years selected randomly.

37. 37 Slide IS 310 – Business Statistics Population and Sample n Example 5 n A restaurant has undergone extensive remodeling and wants to know if customers will like the new décor. n Population consists of all customers who have visited the restaurant in the past. n Sample consists of customers who visited the restaurant during a specific month.

38. 38 Slide IS 310 – Business Statistics Population and Sample n Example 6 n American Airlines is planning to introduce a new policy on flying hours by its pilots. n Population consists of all American Airlines pilots. n Sample consists of 50 American Airlines pilots selected at random.

39. 39 Slide IS 310 – Business Statistics Population and Sample n Example 7 n A workers union has reached a new contract with management. It wants to know the opinion of its members on the terms and conditions of the new contract. n Population consists of all members of the union. n Sample consists of 50 union members selected at random.

40. 40 Slide IS 310 – Business Statistics Population and Sample n Example 8 n FDA wants to compare the average nicotine content of two brands of cigarettes: Brand A and Brand B. n There are two populations: all cigarettes of Brand A and all cigarettes of Brand B. n Sample A consists of 100 cigarettes chosen randomly from all Brand A cigarettes. n Sample B consists of 100 cigarettes chosen randomly from all Brand B cigarettes.

41. 41 Slide IS 310 – Business Statistics Population and Sample n Example 9 n You want to compare home prices between Costa Mesa and Fountain Valley. n There are two populations: Population A consists of all homes in Costa Mesa. Population B consists of all homes in Fountain Valley. n Sample A consists of 100 homes selected at random from all homes in Costa Mesa. Sample B consists of 100 homes from all homes in Fountain Valley.

42. 42 Slide IS 310 – Business Statistics Population and Sample n Example 10 n A research firm wants to compare the average fat content used in meat between McDonald’s Big Mac and Burger King’s Whopper during the month of September in Los Angeles county. n There are two populations: Population A consists of all Big Macs made by McDonald in the month of September in Los Angeles County. Population B consists of all Whoppers made by Burger King in September in Los Angeles County. n Sample A consists of 200 Big Macs selected randomly from Population A and Sample B consists of 200 Whoppers selected at random from Population B.

43. 43 Slide IS 310 – Business Statistics More on Population and Sample Answer if the following questions deal with population or sample. n What is the average MPG of cars driven by all CSULB students? n What percent of 500 students selected at random support off-shore drilling for oil? n What is the range of income of all residents of Long Beach? n What is the average weight of chickens raised in a farm?

44. 44 Slide IS 310 – Business Statistics Sample Problems n Problem # 11 on page 21 n A. Annual sales – Quantitative and ratio. n B. Soft drink size – Qualitative and ordinal. n C. Employee classification – Qualitative and nominal. n D. Earnings per share – Quantitative and ratio. n E. Method of payment – Qualitative and nominal.

45. 45 Slide IS 310 – Business Statistics Sample Problems n Problem # 22 on page 24 n A. All registered voters in California. n B. Those registered voters who were contacted by the policy Institute of California. n C. A sample was reduced to reduce the cost and time.

46. 46 Slide IS 310 – Business Statistics Statistical Inference n Statistical inference is a statistical procedure to determine the characteristics of a population by studying a sample. n Let’s the case of Norris Electronics mentioned in your book. Norris developed a new light bulb that increases its useful life. In this case, all new light bulbs comprise the population. To test if the new light bulb really has a longer life, a sample of 200 bulbs was tested and the average life of these bulbs was calculated. This average life will be used to conclude if the new bulb has a longer useful life. This is an example of statistical inference.

47. 47 Slide IS 310 – Business Statistics Statistical Inference n Statistical inference allows us to make conclusions about a population. This conclusion is made by studying a sample. n In the Norris case, the population was all new light bulbs whose life expectancy we wanted to verify. n Do all the new bulbs have a longer life? n We answered this question by studying a sample and calculating the average life of this sample of bulbs.

48. 48 Slide IS 310 – Business Statistics End of Chapter 1