Manufacturing a DRAM chip involves multiple steps. External impurities, faulty deposition, or manufacturing errors in any of these steps could generate chips with faulty memory cells, rendering the chip unusable. To overcome these faulty memory cells, redundancies are included in the memory, allowing mapping of faulty memory cells to these redundant cells. The process of mapping faulty cells to redundant cells is called Redundancy Analysis (RA). Different RA algorithms have been developed and are often tested on randomly generated defect to test their efficiency and execution time. But we observed that, the defect pattern of a chip is not completely random, it follows a distribution pattern and the algorithms should be tested on chips with similar error distribution patterns. So, in this paper, we propose a Statistical Wafer Scale Error and Redundancy Analysis Simulator to generate defects on the chips similar to defects on the manufacturing line. The simulated errors on the chips are based on statistical models derived from real data. After generating defects on the chip, execution, comparison and benchmarking of algorithms based on yield and execution time is done. The simulator gives insights on algorithm behavior with different kinds of memory architectures and defect patterns. This allows designers of memory architecture and RA algorithm to simulate, predict and improve the wafer yield for different RA algorithm designs and memory architectures before manufacturing a new memory device.