HATCO Case

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• HATCO Case
• Primary Database
• This example investigates a business-to-business
  case from existing customers of HATCO.
• The primary database consists 100 observations on
  14 separate variables.
• Three types of information were collected
• The perceptions of HATCO, 7 attributes (X1 X7)
• The actual purchase outcomes, 2 specific measures
  (X9,X10)
• The characteristics of the purchasing companies,
  5 characteristics (X8, X11-X14).

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Table 2.1 Description of Database Variables
(Hair et al., 1998)
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Missing Data

• A missing data process is any systematic event
  external to the respondent (e.g. data entry
  errors or data collection problems) or action on
  the part of the respondent (such as refusal to
  answer) that leads to missing values.
• The impact of missing data is detrimental not
  only through its potential hidden biases of the
  results but also in its practical impact on the
  sample size available for analysis.

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• Understanding the missing data
• Ignorable missing data
• Remediable missing data
• Examining the pattern of missing data

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Table 2.2 Summary Statistics of Pretest Data
(Hair et al., 1998)
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Outliers

• Four classes of outliers
• Procedural error
• Extraordinary event can be explained
• Extraordinary observations has no explanation
• Observations fall within the ordinary range of
  values on each of the variables but are unique in
  their combination of values across the variables.
• Detecting outliers
• Univariate detection
• Bivariate detection
• Multivariate detection

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Outliers detection

• Univariate detection threshold
• For small samples, within 2.5 standardized
  variable values
• For larger samples, within 3 or 4 standardized
  variable values
• Bivariate detection threshold
• Varying between 50 and 90 percent of the ellipse
  representing normal distribution.
• Multivariate detection
• The Mahalanobis distance D2

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Table 2.7 Identification of Univariate and
Bivariate Outliers (Hair et al., 1998)
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Fig 2.3 Graphical Identification of Bivariate
Outliers (Hair et al., 1998)