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1 An Introduction to Business Statistics Chapter CHAPTER 1 MAP 1.1 Business Statistics and Their Uses 1.2 Data 1.3 Descriptive and Inferential Statistics 1.4 Ethics and Statistics  It’s a Dangerous World of Data Out There

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

Business Statistics and Their Uses Examples of how business use 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

1.2 Data Data values assigned to observations or measurements Information data that are transformed into useful facts that can be used for a specific purpose, such as making a decision

Data Data: Raw facts or measurements of interest Table 1.1 | Golf-Score Data Date Score 6/13 94 6/20 96 6/27 93 7/10 89 7/16 86 7/24 89 Each individual value is considered a data point

Information Analyzing the data can provide information for decision making Table 1.1 | Golf-Score Data Date Score 6/13 94 6/20 96 6/27 93 7/10 89 7/16 86 7/24 89 Did a new driver after 7/1 change the average golf score?

The Sources of Data Primary data data that you have collected for your own use Secondary data data collected by someone else

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

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

Figure 1.2 | An Example of a Survey To encourage respondents to participate, an effective survey will state its purpose in the beginning Personal demographic questions are often last, when respondents feel more comfortable with the process

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?”

The Two Main Types of Data Qualitative Data Quantitative Data Classified by descriptive terms Counted Measured Examples: Marital Status Political Party Eye Color (Defined categories) Described by numerical values Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics)

Classifying Data by Level of Measurement Figure 1.4 | Two Main Types of Data and their Corresponding Levels Types of Data Qualitative Quantitative Nominal Ordinal Interval Ratio

Classifying Data by Level of Measurement

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

Time Series vs. Cross-Sectional Data Table 1.3 | Unemployment Rate Data, 2008–2012 Unemployment Rate Year USA % CA % DE % MI % TX % 2008 4.9 5.9 3.8 7.1 4.4 2009 7.6 10.1 6.7 11.6 6.4 2010 9.7 12.3 8.8 13.7 8.2 2011 9.0 12.4 8.5 10.7 8.3 2012 10.9 7.0 7.3 Cross- Sectional Data Time Series Data

Time Series vs. Cross-Sectional Data Figure 1.5 - A Time Series Graph of U.S. Unemployment Rates, 2008–2012 Figure 1.6 - A Cross-Sectional Graph of 2012 Unemployment Rates

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

Population vs. Sample Population Sample 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

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

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

Inferential Statistics Figure 1.8 - Using Inferential Statistics for Quality Control Purposes: An Example

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

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.

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