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sldar216 sldar216
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6 years ago
Imputing values is valid only if the variable values are We may replace missing values with the variable's mode, mean, or median, but only if the variable values are
 a. not missing at random (MNAR).
  b. missing at random (MAR).
 
  c. missing completely at random (MCAR).
  d. Classification and Regression Trees (CART).

Q. 2

A(n)_______________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.
 a. record
  b. data point
  c. classification
  d. location

Q. 3

If a model's implications depend on the inclusion or exclusion of outliers, one should spend additional time to track down
 a. the cause of the outliers.
 
  b. the missing values.
 
  c. a better estimation of the outliers.
  d. another source of data.

Q. 4

The set of recorded values of variables associated with a single entity is a(n)
 a. observation.
  b. data point.
  c. classification.
  d. location.
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Replies
wrote...
6 years ago
Ans. #1

c
RATIONALE: Options for replacing the missing entries for a variable include replacing the missing value with the variable's mode, mean, or median. Imputing values in this manner is truly valid only if variable values are MCAR; otherwise, we may be introducing misleading information into the data.

Ans. #2

a
RATIONALE: An observation, or record, is the set of recorded values of variables associated with a single entity. It is displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

Ans. #3

a
RATIONALE: If a model's implications depend on the inclusion or exclusion of outliers, then one should spend additional time to track down the cause of the outliers.

Ans. #4

a
RATIONALE: An observation, or record, is the set of recorded values of variables associated with a single entity. It is displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.
sldar216 Author
wrote...
6 years ago
Makes more sense now, TY
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