Modified Ratio Estimator Using Multiple Auxiliary Variables
| dc.contributor.author | Gboyega, Ayobami | |
| dc.date.accessioned | 2022-03-03T13:03:44Z | |
| dc.date.available | 2022-03-03T13:03:44Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | This study developed a modified ratio estimator using one variable of interest, multi-auxiliary variables and the product of Kurtosis and Median of the auxiliary variables. The questionnaire contained detail about workers income and expenditures. Boxplot was used to check for outliers. The biases and mean squared errors (MSE) were computed for both existing and modified ratio estimator. Principal Components Analysis (PCA) was used to determine the linear combination of auxiliary variables that best explain the variable of interest. The scree and bar plots were used to arrange eigenvalues in descending order. The expenditure on food stands as an outlier. The bias and MSE of the existing ratio estimator were 74.39586 and 8,382,470 while that of modified ratio estimator were 30.97307 and 6,128,431 respectively. PCA generated two principal components, which accounted for 76.2% of the total variance. The modified ratio estimator was efficient. | en_US |
| dc.identifier.uri | http://dspace.run.edu.ng:8080/jspui/handle/123456789/1944 | |
| dc.language.iso | en | en_US |
| dc.publisher | Pacific Journal of Science and Technology. | en_US |
| dc.subject | Ratio estimator | en_US |
| dc.subject | Mean square error | en_US |
| dc.subject | Principal component | en_US |
| dc.subject | Auxiliary variable | en_US |
| dc.title | Modified Ratio Estimator Using Multiple Auxiliary Variables | en_US |
| dc.type | Article | en_US |