Application of Multiple Linear Regression Analysis in Ergonomics Study for Public Institution Seat W idth

Objectives: To determine minimum seat width of comfortable seating for adult population, using multiple linear regression analysis on chair leg thickness, chair leg width, chair apron width, and knee height. Methods: Dimensions of three (3) factors were recorded using 310 adult workers. Multiple linear regression was used for statistical analysis for understanding how multiple factors (like chair leg thickness, chair leg width, chair apron width, and knee height) influence a single outcome to enhance comfortable seating. The collected data (chair leg thickness, chair leg width, chair apron width, and knee height) was analysed with the help of the analysis tool. Findings: The analysis used four predictors (knee height, chair leg thickness, chair leg width, and chair apron width) to obtain one predicted variable (seat width) to achieve comfortable seating based on these measurements. Subsequently, a moderate correlation existed between predictors and predicted variables, while correlations between predictors were less than 0.7 for them to be retained in the model. The coefficient of determination ( ) is 0.3003, indicating that 30.03% of the variance in predictors could be explained by seat width. Based on the comfort assessment and analysis results, required widths that ensure comfortable seating for the target population can be recommended in public institutions. Overall, this research has the potential to improve public health, comfort, and productivity by creating data-driven design standards for seating in public institutions. Novelty: The study focused on elderly workers who patronise this furniture in the administration block of public institutions. Keywords: Multiple linear regression, Ergonomics, Knee height, Chair component dimensions, Seat width