1 Sample description

The sample consists of 200 persons in the public sector. The gender distribution is as follows (Table 1):
Table 1: Gender distribution
gender Frequency
Female 100
Male 100
Further descriptive statistics are summarized in the following Table 2:
Table 2: descreptive statistics
Mean Salary in Euro SD Salary Mean Years SD Years
122303.4 79030.12 15.73 9.04

2 Association between years and salary as scatterplot

The scatterplot shows a positive but non-linear relationship between length of employment and salary. While a salary increase tends to be observed with increasing professional experience, this increase is not constantly linear.

3 Estimate salary by years of employment

A linear regression model was estimated in order to make the relationship between length of employment and salary quantifiable. Due to the non-linear form of the relationship (see scatterplot), the salary was logarithmized to enable a linear approach (log(salary)=š¯›½0+š¯›½1ā‹…years).


Table 3: Regression results
term estimate std.error statistic p-Wert
(Intercept) 10.383 0.028 377.543 0
Years of Employment 0.071 0.002 46.813 0

4 Interpretation

The regression model yields an estimated coefficient of 0.071 for the variable years of employment. This means that an additional year of work experience is associated with an average salary increase of approximately 7.36 % , when assuming that other influencing factors remain constant. The exponential increase matches the non-linear development recognizable in the scatterplot.