# Innovation in Company Labor Productivity Management: Data Science Methods Application
## 3 Experimental Results  
### Exploratory Data Analysis and Visualization  
We use qualitative and quantitative indicators about social characteristics of personnel (education, marital status, children), anthropometric (gender, age) and characteristics of self-assessment of the state and quality of personnel health (current health problems; chronic diseases; health self-assessment; proper nutrition; bad habits), Table 1.  
The transition of the qualitative values into quantitative ones is carried out on the basis of binary coding (0 and 1), while the quantitative value of a feature increases with its qualitative characteristics intensifies. Individual labor productivity is calculated using the methodology that is used to assess the labor productivity of companies in the basic non-resource sectors of the economy [55]. Within this methodology labor productivity reflects the measure of value added per employee of the company. We receive all data by the special employees' survey with using the developed questionnaires. Whiskers diagrams visualize the expected impact of qualitative features on labor productivity (LP), Figure 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Variable** & **Variable Value or Binary** \\ \hline Age & age & 20–65 \\ \hline Gender & gender & female (0), male (1) \\ \hline Marital status & mar & Not married (0), married (1) \\ \hline Education & edu & Specialized secondary (0), higher (1) \\ \hline Children & child & 0, 1, 2,... \\ \hline Chronic diseases & chron\_dis & no (0), yes (1) \\ \hline Feeling unwell & bad\_health & Infrequently (0), often (1) \\ \hline Self-reported health as weak & health\_self-ass & no (0), yes (1) \\ and unsatisfactory & & no (0), yes (1) \\ \hline Inadequate nutrition & nutr & no (0), yes (1) \\ \hline Bad habits & bad\_hab & no (0), yes (1) \\ \hline Individual labor productivity & labor\_pert & 136–420 rub (1 ruble is equivalent to 0.01 euro) per hour \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indicators, variables and range of variable values. Diagrams show that different levels of the factors gender, child, health_self-ass and bad_hab determine the difference in the labor productivity indicator--labor_perf. Thus, men have higher average labor productivity, about 280 rubles per hour than women--225 rubles per hour. Those personnel who do not have chronic diseases do not often feel unwell and do not have bad habits; their average level of labor productivity is higher than others. Figure 2: Whiskers diagrams for the factor groups “child and labor_perf” (**a**), “mar and labor_perf” (**b**), “health_self-ass and labor_perf” (**c**), “bad_hab and labor_perf” (**d**), “chron_dis and labor_perf” (**e**), “gender and labor_perf” (**f**). Workers with one or two children have more stable level of labor productivity, close to the average, which, in turn, is higher than workers without children. 3D surface plots and contour plots for visualization of labor productivity (labor_perf) depending on the quantitative variables (age) and (child) are shown in Figure 3. From these visualizations, it's obvious that younger workers under the age of 35 with children have higher levels of labor productivity than older workers (over 55 years old) with more than three children, as well as middle-aged workers (from 35 to 55 years old) without children. Descriptive statistics for the variable \"labor productivity\" characterize the significant variation, non-coincidence of indexes--mean, mode, median and variation demonstrate data heterogeneity, Table 2.  
Correlation analysis identifies paired relationships between studied factors and labor productivity. We also note that surveyed indicators were measured on different scales. Labor productivity, age and number of children have a continuous metric scale, education level has an ordinal (rank) scale and other indicators have on a nominal, categorical scale.