Analysis of Worker’s Age and Working Time on the Influence of Productivity with Multiple Linear Regression Methods (Case Study of Automotive Spare Parts Manufacturing Company)
DOI:
https://doi.org/10.22441/ijiem.v4i2.20019Keywords:
Working period, Age, Productivity, Multiple Linear Regression Method, MinitabAbstract
Improving the quality of products produced by industry is largely determined by labor productivity. Age and years of service are factors that can reduce or increase employee productivity. So labor productivity is very influential on company profits. This research was conducted at companies that produce automotive parts, aiming to determine the effect of age and years of service of employees on the level of productivity in the pruning process. In this study, we took a sample of 44 employees using the multiple linear regression method using Minitab software. In this study the independent or independent variables are age (X1) and years of service (X2), while to determine the variable is the level of employee productivity (Y). The results of the study, through data analysis, showed that the variables X1 (age) and X2 (years of service) did not affect Y (work productivity). The results of the classical assumption test obtained that the normality test results obtained a P-Value value of 0.010 < 0.05, the multicollinearity test obtained a VIF value of 23.38 > 10, and for the heteroscedasticity test it was found that the graph versus fit occurred in an orderly distribution. As for the results of the hypothesis test with the summary of the model that the factors X1 (age) and X2 (years of service) have little effect on Y (work productivity), which is only 4.3% and the remaining 95.7% is influenced by other factors that are not research variables.Downloads
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