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Explanation: Replacing missing data with statistical measures like mean (for continuous data), median (for skewed distributions), or mode (for categorical data) is a robust imputation technique. This approach minimizes the loss of data while maintaining the dataset's integrity. It is particularly effective when missing values are random (MCAR) and do not introduce significant bias. However, this method may not work well for datasets with a high proportion of missing values or when patterns in the missing data need to be preserved. Advanced imputation methods like k-Nearest Neighbors (KNN) or predictive models can be used in such cases. Option A: Deleting rows with missing values can result in significant data loss, reducing the dataset's representativeness. Option C: Ignoring missing data leads to inaccuracies and potential errors in analysis. Option D: Filling with arbitrary constants like zero can distort the dataset, introducing bias. Option E: Duplicating rows compromises the dataset's integrity and can lead to overfitting in predictive models.
Gameti' is leader of which of the following tribal people?
Any ineligible Member of Parliament is terminated by?
The measures of tenancy reforms pertain to
(1) Regulation of rent
(2) Security of tenure
(3) Ownership rights for tenants
In which of the following regions are Pygmies found?
UNO was established on?
Padmashree Kripalsingh Shekhawat is related to -
The intensity of earthquake is measured by?
What was the immediate outcome of the conference of ambassadors of European states, which was chaired by
Duke Metternich?
Lohit River flows in?
Who formed the association in favour of widow remarriage in Madras Presidency?