A method for restoring the anthropometric database of elderly women for design

In order to solve the problem of missing data from anthropometric database, this paper proposes a weighted regression method that can better repair the database. Through data classification and correlation analysis, least squares method and weighted regression method optimal setting and fitting effect analysis, the proposed "algorithm" was used to predict three data samples with different missing degrees. The results show that (1) the optimal setting for the least squares method is when the X and Y counts are both 1, and for the weighted regression method,is when the X and Y counts are both 1 and the weight decay factor is 90%. (2) The individual comparison effect indicator T ≤ 1 and the overall comparison effect indicator G = 0.68 < 1 both indicate that the weighted regression method has a better fitting performance. (3) The mean errors of the least squares method for the three samples with small missing data were 3.6, 1.16, and 1.11 times that of the weighted regression method. For with medium missing data, were 1.27, 1.16, and 1.05 times. For with large missing data,were 1.15, 1.42, and 1.25 times. In summary, the weighted regression method can provide better prediction results.