Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall Chapter Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

1.1 Business Statistics and Their Uses the mathematical science that deals with the collection, analysis, and presentation of data, which can then be used as a basis for inference and induction Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Business Statistics and Their Uses Examples of how business uses statistics: Marketing Research Focus group data, customer surveys Advertising Household surveys, TV viewing habits Operations Quality control, reliability Finance and Economics Data on income, credit risk, unemployment Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall 1.2 Data Data values assigned to observations or measurements raw facts or measurements of interest Information data that are transformed into useful facts that can be used for a specific purpose, such as making a decision analyzing data can provide information for decision making Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall The Sources of Data Primary data data that you have collected for your own use Secondary data data collected by someone else Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall The Sources of Data Primary data Secondary data Advantages: collected by the person or organization who uses the data Disadvantages: Can be expensive and time-consuming to gather Advantages: Readily available Less expensive to collect Disadvantages: No control over how the data was collected Less reliable unless collected and recorded accurately Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Primary data collection methods Direct Observation or Focus Group Experiments Surveys or Questionnaires Observing subjects in their natural environment Example: Watching to see if drivers stop at a stop sign Treatments are applied in controlled conditions Example: Crop growth from different plots using different fertilizers Subjects are asked to respond to questions or discuss attitudes Example: E-mail surveys to customers to assess service quality Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall Bias The manner in which survey questions are asked can affect responses Bias can occur when a question is stated in a way that encourages or leads a respondent to a particular answer Example: “Do you agree that the current overly complex tax code should be simplified and made more fair?” Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Classifying Data by Level of Measurement Types of Data Qualitative Quantitative Nominal Ordinal Interval Ratio Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Classifying Data by Level of Measurement Table 1.2 | The Four Levels of Data Measurement: A Summary Level Description Example Nominal Arbitrary labels for data Zip Codes No ranking allowed (19808, 76137) Ordinal Ranking allowed Education level No measurable meaning (Master’s degree, to the number differences doctorate degree) Interval Meaningful differences Calendar year No true zero point (2009, 2010) Ratio Meaningful differences Income True zero point ($48,000, $0) Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Time Series vs. Cross-Sectional Data Time Series Data values that correspond to specific measurements taken over a range of time periods Cross Section Data values collected from a number of subjects during a single time period Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Time Series vs. Cross-Sectional Data Table 1.3 | Unemployment Rate Data, 2006–2010 Unemployment Rate Year USA CA DE MI TX 2006 4.6% 4.9% 3.5% 6.9% 2007 4.6 5.3 3.5 7.2 4.4 2008 5.8 4.9 8.3 2009 9.3 11.3 8.0 13.3 7.6 2010 9.6 12.4 8.5 12.5 8.2 Cross- Sectional Data Time Series Data Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

1.3 Descriptive and Inferential Statistics Descriptive statistics collecting, summarizing, and displaying data Inferential statistics making claims or conclusions about the data based on a sample Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall Population vs. Sample Population represents all possible subjects that are of interest in a particular study Sample refers to a portion of the population that is representative of the population from which it was selected Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Parameter vs. Statistic Parameter – a described characteristic about a population Statistic – a described characteristic about a sample Population Sample Values calculated using population data are called parameters Values computed from sample data are called statistics Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Inferential Statistics Making statements about a population by examining sample results Example: Observed sample statistic (known) Estimated population parameter (unknown, but can be estimated from sample evidence) Inference Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Inferential Statistics Figure 1.5 | Using Inferential Statistics for Quality Control Purposes: An Example Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

1.4 Ethics and Statistics – It’s a Dangerous World of Data Out There Biased sample – a sample that does not represent the intended population can lead to distorted findings biased sampling can occur intentionally or unintentionally results can be manipulated by how we ask questions and who is responding to them Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

Ways to Misuse Statistics Changing the graph scale Should avoid distortion that might convey the wrong message Choosing a sample that is not representative of the population Avoid bias by randomly sampling from the population Vs. Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall

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