chap1 - Business Statistics (BUSA 3101) Dr. Lari H. Arjomand

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Unformatted text preview: Business Statistics (BUSA 3101) Dr. Lari H. Arjomand [email protected] Text Book Statistical Techniques in Business & Economics Lind, Marchal, and Wathen 15th Ed. 2012 Slide 1 Business Statistics s For Lecture Notes, Slides, Quizzes, Projects, Syllabus, Office Hours, Exams & Due Dates, Statistical Links, Tutorials, Bulletin Board & Much More referee to my website at the following URL: http://business.clayton.edu/arjomand Slide 2 Chapter 1 What is Statistics s Applications in Business and Economics s Data s Data Sources s I need help! Descriptive Statistics Statistical Inference s Computers and Statistical Analysis s Slide 3 Application Areas of Statistics • • Accounting Auditing Costing • • • • Finance Financial trends Forecasting Management Describe employees Quality improvement • • Marketing Consumer preferences Marketing mix effects Slide 4 Applications in Business and Economics s Accounting Public accounting firms use statistical sampling procedures when conducting audits for their clients. s Economics Economists use statistical information in making forecasts about the future of the economy or some aspect of it. Slide 5 Applications in Business and Economics s Marketing Electronic point­of­sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. s Production A variety of statistical quality control charts are used to monitor the output of a production process. Slide 6 Applications in Business and Economics Finance Financial advisors use price­earnings ratios and dividend yields to guide their investment recommendations. Slide 7 Why Collect Data? Obtain input to a research study Measure performance Assist in formulating decision alternatives Satisfy curiosity • Knowledge for the sake of knowledge Slide 8 Data and Data Sets Data are the facts and figures collected, summarized, analyzed, and interpreted. s The data collected in a particular study are referred to as the data set. Slide 9 Elements, Variables, and Observations The elements are the entities on which data are collected. A variable is a characteristic of interest for the elements. The set of measurements collected for a particular element is called an observation. The total number of data values in a data set is the number of elements multiplied by the number of variables. Slide 10 Data, Data Sets, Elements, Variables, and Observations Element Names Variables Observation Company Dataram EnergySouth Keystone LandCare Psychemedics Stock Annual Earn/ Exchange Sales($M) Share($) AMEX OTC NYSE NYSE AMEX 73.10 0.86 74.00 1.67 365.70 0.86 111.40 0.33 17.60 0.13 Data Set Slide 11 Scales of Measurement Scales of measurement include: Scales of measurement include: Nominal Interval Ordinal Ratio The scale determines the amount of information The scale determines the amount of information contained in the data. contained in the data. 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. Slide 12 Scales of Measurement s Nominal 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. A nonnumeric label or numeric code may be used. A nonnumeric label or numeric code may be used. Slide 13 Scales of Measurement s Nominal 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). Slide 14 Scales of Measurement s Ordinal 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 A nonnumeric label or numeric code may be used. A nonnumeric label or numeric code may be used. Slide 15 Scales of Measurement s 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). Slide 16 Scales of Measurement s Interval 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. Interval data are always numeric.. Interval data are always numeric Slide 17 Scales of Measurement s 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. Slide 18 Scales of Measurement s 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 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. Slide 19 Scales of Measurement s 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. Slide 20 Types of Data Data Numerical (Quantitative) Discrete Categorical (Qualitative) Continuous Slide 21 Qualitative and Quantitative Data 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. 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. Slide 22 Qualitative Data 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 Can be either numeric or nonnumeric Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited Appropriate statistical analyses are rather limited Slide 23 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. Slide 24 Scales of Measurement Data Qualitative Numerical Numerical Nominal Nominal Ordinal Ordinal Quantitative Non­numerical Non­numerical Nominal Nominal Ordinal Ordinal Numerical Numerical Interval Interval Ratio Ratio Slide 25 Cross­Sectional Data Cross­sectional data are collected at the same or Cross­sectional 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 2003 in each of the counties permits issued in June 2003 in each of the counties of Ohio of Ohio Slide 26 Time Series Data 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 Slide 27 Data Sources Data Sources Primary Experiment Survey Secondary Observation Published (& On-Line) Slide 28 Data Sources s 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 Special­interest organizations – Graduate Management Admission Council Internet – more and more firms Slide 29 Data Sources (Continued) s Statistical Studies In experimental studies the variables of interest In experimental studies the variables of interest are first identified. Then one or more factors are are first identified. Then one or more factors are controlled so that data can be obtained about how controlled so that data can be obtained about how the factors influence the variables. the factors influence the variables. 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 Slide 30 Data Acquisition Considerations Time Requirement • Searching for information can be time consuming. • Information may no longer be useful by the time it is available. Cost of Acquisition • Organizations often charge for information even when it is not their primary business activity. Data Errors • Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information. Slide 31 What Is Statistics? s s s Collecting data • e.g., Survey Presenting data • e.g., Charts & tables Characterizing data • e.g., Average Data Data Analysis Analysis Why? DecisionMaking Slide 32 Statistical Methods Statistical Methods Descriptive Statistics Inferential Statistics Slide 33 Descriptive Statistics s Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data. Descriptive Statistics: These are statistical methods used to describe data that have been collected. have Slide 34 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 tune­ups performed in the shop. She examines 50 customer invoices for tune­ups. The costs of parts, rounded to the nearest dollar, are listed on the next slide. Slide 35 Example: Hudson Auto Repair s Sample of Parts Cost for 50 Tune­ups 91 71 104 85 62 78 69 74 97 82 93 72 62 88 98 57 89 68 68 101 75 66 97 83 79 52 75 105 68 105 99 79 77 71 79 80 75 65 69 69 97 72 80 67 62 62 76 109 74 73 Slide 36 Tabular Summary: Frequency and Percent Frequency Parts Cost ($) 50­59 60­69 70­79 80­89 90­99 100­109 Parts Frequency 2 13 16 7 7 5 50 Percent Frequency 4 26 (2/50)100 32 14 14 10 100 Slide 37 Graphical Summary: Histogram 18 Tune­up Parts Cost 16 Frequency 14 12 10 8 6 4 2 50−59 60−69 70−79 80−89 90−99 100­110 Parts Cost ($) Slide 38 Numerical Descriptive Statistics The most common numerical descriptive statistic is the average (or mean). Hudson’s average cost of parts, based on the 50 tune­ups studied, is $79 (found by summing the 50 cost values and then dividing by 50). Slide 39 Inferential Statistics s s Involves • Estimation • Hypothesis testing Population? Population? Purpose • Make decisions about population characteristics Inferential Statistics: These are statistical methods used to find out something about population based on a sample. on Slide 40 Statistical Inference Population − the set of all elements of interest in a particular study Sample − a subset of the population Statistical inference − the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population Census − collecting data for a population Sample survey − collecting data for a sample Slide 41 Process of Statistical Inference 1. Population consists of all tune­ups. Average cost of parts is unknown. 2. A sample of 50 4. The sample average 3. The sample data is used to estimate the population average. engine tune­ups is examined. provide a sample average parts cost of $79 per tune­up. Slide 42 Statistical Analysis Using Microsoft Excel Statistical analysis typically involves working with large amounts of data. Computer software is typically used to conduct the analysis. Frequently the data that is to be analyzed resides in a spreadsheet. Modern spreadsheet packages are capable of data management, analysis, and presentation. MS Excel is the most widely available spreadsheet software in business organizations. Slide 43 Statistical Analysis Using Microsoft Excel s 3 tasks might be needed: • Enter Data • Enter Functions and Formulas • Apply Tools 1 2 3 4 5 6 7 8 D Mean Median Mode Range A Parts Cost 91 71 104 85 62 78 69 E =AVERAGE(A2:A71) =MEDIAN(A2:A71) =MODE(A2:A71) =MAX(A2:A71)-MIN(A2:A71) Slide 44 Statistical Analysis Using Microsoft Excel s Excel Worksheet (showing data) A 1 2 3 4 5 6 7 8 9 Customer Sam Abrams Mary Gagnon Ted Dunn ABC Appliances Harry Morgan Sara Morehead Vista Travel, Inc. John Williams B Invoice # 20994 21003 21010 21094 21116 21155 21172 21198 C Parts Cost ($) 91 71 104 85 62 78 69 74 D Labor Cost ($) 185 205 192 178 242 148 165 190 Note: Rows 10­51 are not shown. Slide 45 Statistical Analysis Using Microsoft Excel s 1 2 3 4 5 6 7 8 9 Excel Formula Worksheet C D E Parts Labor Cost ($) Cost ($) 91 185 71 205 104 192 85 178 62 242 78 148 69 165 74 190 F G Average Parts Cost =AVERAGE(C2:C51) Note: Columns A­B and rows 10­51 are not shown. Slide 46 Statistical Analysis Using Microsoft Excel s 1 2 3 4 5 6 7 8 9 Excel Value Worksheet C D E Parts Labor Cost ($) Cost ($) 91 185 71 205 104 192 85 178 62 242 78 148 69 165 74 190 F G Average Parts Cost 79 Note: Columns A­B and rows 10­51 are not shown. Slide 47 End of Chapter 1 Slide 48 ...
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This document was uploaded on 11/25/2011.

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