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1 Slide AQA - Business Statistics, Quantitative Analysis Peter Matthews FDA B&M

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2 Slide LECTURE The aim today is to describe What Are Statistics Where are they used

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3 Slide What is Statistics I need help! Applications in Business and Economics Statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty Plus : - it’s a pain and lots of people hate it.

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4 Slide Application Areas of Statistics Accounting Auditing Costing Finance Financial trends Forecasting Management Management Describe employees Describe employees Quality improvement Quality improvement Marketing Consumer preferences Consumer preferences Marketing mix effects Marketing mix effects

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5 Slide Applications in Business and Economics Accounting 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.

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

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7 Slide Applications in Business and Economics Financial advisers use price-earnings ratios and dividend yields to guide their investment recommendations. Finance

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8 Slide Why Collect Data? Obtain input to a research study Measure performance Assist in formulating decision alternatives Satisfy curiosity Knowledge for the sake of knowledge

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9 Slide Data and Data Sets The Data are the facts and figures collected, summarized, analyzed, and interpreted. Data collected in a particular study are referred to as the data set.

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10 Slide 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. Elements, Variables, and Observations

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11 Slide 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 AMEX AMEX OTC OTC NYSE NYSE NYSE NYSE AMEX AMEX Variables Element Names Names Data Set Observation

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12 Slide 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

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13 Slide 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

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14 Slide Quantitative Data Quantitative data indicate how many or how much: Quantitative data indicate how many or how much: discrete, if measuring how many (finite) discrete, if measuring how many (finite) continuous, if measuring how much (infinite) continuous, if measuring how much (infinite) 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.

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15 Slide Types of Data Types of Data

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16 Slide Data Sources

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17 Slide Data Sources Existing Sources (Secondary) Existing Sources (Secondary) 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

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18 Slide Statistical Studies Statistical Studies Data Sources (Continued) 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 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 In observational (non-experimental) 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 (non-experimental) studies no In observational (non-experimental) 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

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

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20 Slide What Is Statistics? Collecting data Collecting data e.g., Survey e.g., Survey Presenting data Presenting data e.g., Charts & tables e.g., Charts & tables Characterizing data Characterizing data e.g., Average e.g., Average Data Analysis Decision- Making Why?

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21 Slide 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.

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22 Slide Example: Dixon Car Repair The manager 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 £, are listed on the next slide.

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23 Slide Example: Hudson Auto Repair Example: Hudson Auto Repair Sample of Parts Cost for 50 Tune-ups Sample of Parts Cost for 50 Tune-ups

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24 Slide Tabular Summary: Frequency and Percent Frequency Tabular Summary: Frequency and Percent Frequency (2/50)100 Parts Cost Cost (£) Parts Frequency Percent Frequency

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25 Slide Graphical Summary: Histogram Graphical Summary: Histogram Parts Cost ($) Frequency 50 Tune-up Parts Cost

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26 Slide Inferential Statistics Involves Involves Estimation Estimation Hypothesis testing Hypothesis testing Purpose Purpose Make decisions about population characteristics Make decisions about population characteristics Population? Inferential Statistics: These are statistical methods used to find out something about population based on a sample.

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27 Slide Statistical Inference Population Sample Statistical inference Census Sample survey the set of all elements of interest in a particular study a subset of the population the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population collecting data for a population collecting data for a sample

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28 Slide Statistical Analysis Using Microsoft Excel Statistical analysis typically involves working with large amounts of dataa. 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.

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29 Slide Excel Worksheet (showing data) Excel Worksheet (showing data) Note: Rows are not shown. Statistical Analysis Using Microsoft Excel

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30 Slide Excel Formula Worksheet Excel Formula Worksheet Note: Columns A-B and rows are not shown. Statistical Analysis Using Microsoft Excel

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31 Slide Excel Value Worksheet Excel Value Worksheet Note: Columns A-B and rows are not shown. Statistical Analysis Using Microsoft Excel

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