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Business Statistics (BUSA 3101) Dr. Lari H. Arjomand lariarjomand@clayton.edu Text Book Statistical Techniques in Business & Economics Lind, Marchal, and Wathen 14th Ed. 2010 Slide 1 Business Statistics 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 I need Applications in Business and Economics help! Data Data Sources Descriptive Statistics Statistical Inference Computers and Statistical Analysis Slide 3 Application Areas of Statistics Accounting Management • Auditing • Describe employees • Costing • Quality improvement Finance Marketing • Financial trends • Consumer preferences • Forecasting • Marketing mix effects Slide 4 Applications in Business and Economics Accounting Public accounting firms use statistical sampling procedures when conducting audits for their clients. 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 Marketing Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. 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 price-earnings 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. 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 Variables Element Names Stock Annual Earn/ Company Exchange Sales($M) Share($) Dataram AMEX 73.10 0.86 EnergySouth OTC 74.00 1.67 Keystone NYSE 365.70 0.86 LandCare NYSE 111.40 0.33 Psychemedics AMEX 17.60 0.13 Data Set Slide 11 Scales of Measurement Scales of measurement include: Nominal Interval Ordinal Ratio The scale determines the amount of information contained in the data. The scale indicates the data summarization and statistical analyses that are most appropriate. Slide 12 Scales of Measurement Nominal Data are labels or names used to identify an attribute of the element. A nonnumeric label or numeric code may be used. Slide 13 Scales of Measurement Nominal Example: Students of a university are classified by the school in which they are enrolled using a nonnumeric label such as Business, Humanities, Education, and so on. Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and so on). Slide 14 Scales of Measurement Ordinal The data have the properties of nominal data and the order or rank of the data is meaningful. A nonnumeric label or numeric code may be used. Slide 15 Scales of Measurement Ordinal Example: Students of a university are classified by their class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Slide 16 Scales of Measurement Interval The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure. Interval data are always numeric. Slide 17 Scales of Measurement Interval Example: Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 points more than Kevin. Slide 18 Scales of Measurement Ratio The data have all the properties of interval data and the ratio of two values is meaningful. Variables such as distance, height, weight, and time use the ratio scale. This scale must contain a zero value that indicates that nothing exists for the variable at the zero point. Slide 19 Scales of Measurement Ratio Example: Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned as Melissa. Slide 20 Types of Data Data Numerical Categorical (Quantitative) (Qualitative) Discrete Continuous Slide 21 Qualitative and Quantitative Data Data can be further classified as being qualitative or quantitative. The statistical analysis that is appropriate depends on whether the data for the variable are qualitative or quantitative. In general, there are more alternatives for statistical analysis when the data are quantitative. Slide 22 Qualitative Data Labels or names used to identify an attribute of each element Often referred to as categorical data Use either the nominal or ordinal scale of measurement Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited Slide 23 Quantitative Data Quantitative data indicate how many or how much: discrete, if measuring how many continuous, if measuring how much Quantitative data are always numeric. Ordinary arithmetic operations are meaningful for quantitative data. Slide 24 Scales of Measurement Data Qualitative Quantitative Numerical Non-numerical Numerical Nominal Ordinal Nominal Ordinal Interval Ratio Slide 25 Cross-Sectional Data Cross-sectional data are collected at the same or approximately the same point in time. Example: data detailing the number of building permits issued in June 2003 in each of the counties of Ohio Slide 26 Time Series Data Time series data are collected over several time periods. Example: data detailing the number of building permits issued in Lucas County, Ohio in each of the last 36 months Slide 27 Data Sources Data Sources Primary Secondary Experiment Survey Observation Published (& On-Line) Slide 28 Data Sources 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 Special-interest organizations – Graduate Management Admission Council Internet – more and more firms Slide 29 Data Sources (Continued) Statistical Studies In experimental studies the variables of interest are first identified. Then one or more factors are controlled so that data can be obtained about how the factors influence the variables. In observational (non-experimental) studies no attempt is made to control or influence the 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? Collecting data • e.g., Survey Presenting data Why? • e.g., Charts & tables Data Characterizing data Analysis • e.g., Average Decision- Making Slide 32 Statistical Methods Statistical Methods Descriptive Inferential Statistics Statistics Slide 33 Descriptive Statistics 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. 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 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. Slide 35 Example: Hudson Auto Repair Sample of Parts Cost for 50 Tune-ups 91 78 93 57 75 52 99 80 97 62 71 69 72 89 66 75 79 75 72 76 104 74 62 68 97 105 77 65 80 109 85 97 88 68 83 68 71 69 67 74 62 82 98 101 79 105 79 69 62 73 Slide 36 Tabular Summary: Frequency and Percent Frequency Parts Parts Percent Cost ($) Frequency Frequency 50-59 2 4 60-69 13 26 (2/50)100 70-79 16 32 80-89 7 14 90-99 7 14 100-109 5 10 50 100 Slide 37 Graphical Summary: Histogram Tune-up Parts Cost 18 16 14 12 Frequency 10 8 6 4 2 Parts 50-59 60-69 70-79 80-89 90-99 100-110 Cost ($) 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 tune-ups studied, is $79 (found by summing the 50 cost values and then dividing by 50). Slide 39 Inferential Statistics Involves • Estimation • Hypothesis Population? testing Purpose • Make decisions about population characteristics Inferential Statistics: These are statistical methods used to find out something about population based on a sample. 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 2. A sample of 50 tune-ups. Average engine tune-ups cost of parts is is examined. unknown. 4. The sample average 3. The sample data provide a sample is used to estimate the average parts cost population average. of $79 per tune-up. Slide 42 Statistical Analysis Using Microsoft Excel Statistical analysis typically involves working with large amounts of data. Computer software is typically used to conduct the analysis. Frequently the data that is to be analyzed resides in a spreadsheet. Modern spreadsheet packages are capable of data management, analysis, and presentation. MS Excel is the most widely available spreadsheet software in business organizations. Slide 43 Statistical Analysis Using Microsoft Excel 3 tasks might be needed: A Parts • Enter Data 1 Cost • Enter Functions and Formulas 2 91 3 71 • Apply Tools 4 104 5 85 6 62 7 78 8 69 D E Mean =AVERAGE(A2:A71) Median =MEDIAN(A2:A71) Mode =MODE(A2:A71) Range =MAX(A2:A71)-MIN(A2:A71) Slide 44 Statistical Analysis Using Microsoft Excel Excel Worksheet (showing data) A B C D Parts Labor 1 Customer Invoice # Cost ($) Cost ($) 2 Sam Abrams 20994 91 185 3 Mary Gagnon 21003 71 205 4 Ted Dunn 21010 104 192 5 ABC Appliances 21094 85 178 6 Harry Morgan 21116 62 242 7 Sara Morehead 21155 78 148 8 Vista Travel, Inc. 21172 69 165 9 John W illiams 21198 74 190 Note: Rows 10-51 are not shown. Slide 45 Statistical Analysis Using Microsoft Excel Excel Formula Worksheet C D E F G Parts Labor 1 Cost ($) Cost ($) 2 91 185 Average Parts Cost =AVERAGE(C2:C51) 3 71 205 4 104 192 5 85 178 6 62 242 7 78 148 8 69 165 9 74 190 Note: Columns A-B and rows 10-51 are not shown. Slide 46 Statistical Analysis Using Microsoft Excel Excel Value Worksheet C D E F G Parts Labor 1 Cost ($) Cost ($) 2 91 185 Average Parts Cost 79 3 71 205 4 104 192 5 85 178 6 62 242 7 78 148 8 69 165 9 74 190 Note: Columns A-B and rows 10-51 are not shown. Slide 47 End of Chapter 1 Slide 48 Slide 48

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