Typically, online aggregation algorithms on multi-dimensional data need additional auxiliary data for estimation, which make the performance of the storage and maintenance of the data cube worse. This paper presents the PE (progressively estimate) and HPE (hybrid progressively estimate) to progressively estimate the answers for range queries in the QC-Trees. MPE (multiple progressively estimate) is also proposed to simultaneously evaluate batches of range-sum queries. The difference between the algorithms and other online aggregation algorithms on data cubes is that these algorithms do not need any auxiliary information. The idea of this estimation method is to utilize the data stored in the QC-Tree itself. As a result, this algorithm will not deteriorate the performance of the storage and maintenance of the data cubes. Analysis and experimental results show that the algorithms provide an accurate estimation in far less time than the normal algorithms.