Redundancy is another important issue in data integration. In the data preprocessing, we need to reduce redundancy. There are different approaches to identify and reduce redundancy. Select the following appropriate options that can be used for reducing redundancy. Covariance of numeric data Correlation test for nominal data Correlation coefficient for numeric data Data reduction techniques can be applied to obtain a reduced representation of the data set that is much smaller in volume, yet closely maintains the integrity of the original data. Another necessary step in data preprocessing is data transformation. Data discretization is a form of data transformation. Select correct statements about those strategies that we can use in data reduction or data transformation Strategies for data transformation include but do not limit in smoothing, attribute construction, aggregation, normalization, and discretization. Techniques of numerosity reduction replace the original data volume by alternative, smaller forms of data representation Strategies for data reduction include but do not limit in dimensionality reduction, numerosity reduction, and data compression