A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However, when data sets have missing values, assumptions to statistical methods are violated and produce biased results. The statistician's objective is to implement methods that produce unbiased and accurate results. Research in missing data is becoming popular as modern methods that produce unbiased and accurate results are emerging, such as MICE in R, a statistical software. Using real data, we compare four common imputation methods, in the MICE package in R, at different levels of missingness. The results were compared in terms of the regression coefficients and adjusted R^2 values using the complete data set. The CART and PMM methods consistently performed better than the OTF and RF methods. The procedures were repeated on a second sample of real data and the same conclusions were drawn.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5014 |
| Date | 01 May 2019 |
| Creators | Heidt, Kaitlyn |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
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