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Chapter
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2. 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
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3. 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
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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
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The Sources of Data
Primary data
data that you have collected for your own use
Secondary data
data collected by someone else
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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
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7. 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: surveys to customers to assess service quality
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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?”
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9. Classifying Data by Level of Measurement
Types of Data
Qualitative
Quantitative
Nominal
Ordinal
Interval
Ratio
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10. 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)
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11. 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
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12. 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
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13. 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
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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
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15. 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
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16. 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
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17. Inferential Statistics
Figure 1.5 | Using Inferential Statistics for Quality Control Purposes: An Example
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18. 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
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19. 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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