Is die verandering beperkt tot de probleemlocatie?

Je hebt bij Southfield een verschil in disengagement tussen de twee jaren gevonden, en geen verschil in gemaakte overuren. Nu controleer je ook de andere locaties om zeker te weten dat daar niet óók een grote verandering in disengagement was. Als het disengagementpercentage steeg op andere locaties waar het ongevallenpercentage weinig veranderde, moet je je afvragen of disengagement en ongevallen wel met elkaar samenhangen.

Om de andere locaties in één keer te testen, maak je een nieuwe gegevensset waarin Southfield is weggefilterd. Voer daarna dezelfde statistische toetsen uit als in de vorige oefeningen, maar gebruik de nieuwe gegevensset om de andere locaties te testen.

Deze oefening maakt deel uit van de cursus

HR-analytics: werknemersgegevens verkennen in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Filter out Southfield
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

HR-analytics: werknemersgegevens verkennen in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

In this chapter, you will get an introduction to how data science is used in a human resources context. Then you will dive into a case study where you'll analyze and visualize recruiting data to determine which source of new candidates ultimately produces the best new hires. The dataset you'll use in this and the other chapters in this course is synthetic, to maintain the privacy of actual employees.

Exercise 1: Welcome to the course!Exercise 2: Applications of human resources (HR) analytics Exercise 3: Looking at the recruiting data Exercise 4: Recruiting and quality of hire Exercise 5: Identifying groups in data Exercise 6: Sales numbers by recruiting source Exercise 7: Attrition rates by recruiting source Exercise 8: Visualizing the recruiting data Exercise 9: Visualizing the sales performance differences Exercise 10: Visualizing the attrition differences Exercise 11: Drawing conclusions

Gallup defines engaged employees as those who are involved in, enthusiastic about and committed to their work and workplace. There is disagreement about the strength of the connection between employee engagement and business outcomes, but the idea is that employees that are more engaged will be more productive and stay with the organization longer. In this chapter, you'll look into potential reasons that one department's engagement scores are lower than the rest.

Exercise 1: Analyzing employee engagement Exercise 2: Importing the survey data Exercise 3: Which department has the lowest engagement?Exercise 4: Comparing other factors by department Exercise 5: Visualizing the engagement data Exercise 6: Visualizing several variables at once Exercise 7: Visualizing several variables at once with facets Exercise 8: Drawing conclusions from graphs Exercise 9: Is that difference meaningful?Exercise 10: Statistical significance - disengaged employees Exercise 11: Statistical significance - vacation days Exercise 12: Drawing conclusions from the tests

When employers make a new hire, they must determine what the new employee will be paid. If the employer is not careful, the new hires can come in with a higher salary than the employees that currently work at the same job, which can cause employee turnover and dissatisfaction. In this chapter, you will check whether new hires are really getting paid more than current employees, and how to double-check your initial observations.

Exercise 1: Paying new hires fairly Exercise 2: Importing the pay data Exercise 3: Is the difference significant?Exercise 4: Omitted variable bias Exercise 5: Omitted variable bias examples Exercise 6: What other differences exist?Exercise 7: What does the pay difference look like now?Exercise 8: Are hourly hires paid more?Exercise 9: Linear regression Exercise 10: New hire pay: a simple regression Exercise 11: New hire pay: accounting for job levels Exercise 12: Drawing conclusions from the tests

Performance management helps an organization keep track of which employees are providing extra value, or below-average value, and compensating them accordingly. Whether performance is a rating or the result of a questionnaire, whether employees are rated each year or more often than that, the process is somewhat subjective. An organization should check that ratings are being given with regard to performance, and not individual managers' preferences, or even biases (conscious or subconscious).

Exercise 1: Joining HR data Exercise 2: Importing the two datasets Exercise 3: Joining performance data Exercise 4: Performance ratings and fairness Exercise 5: Focus on high performers Exercise 6: Comparing distributions of high performers Exercise 7: Checking for omitted variable bias Exercise 8: Logistic regression Exercise 9: Linear and logistic regression Exercise 10: Performance ratings: a simple logistic regression Exercise 11: Performance ratings: accounting for job levels Exercise 12: Conclusions and recommendations

In many industries, workplace safety is a critical consideration. Maintaining a safe workplace provides employees with confidence and reduces costs for workers' compensation and legal liabilities. In this chapter, you'll look for explanations for an increase in workplace accidents.

Exercise 1: Veiligheid van werknemers Exercise 2: Ongevallengegevens importeren en samenvoegen Exercise 3: Waar is het ongevalspercentage het hoogst?Exercise 4: Waar nam het ongevalspercentage het meest toe?Exercise 5: Focussen op de locatie van interesse Exercise 6: Focus op de locatie van het probleem Exercise 7: Meer data toevoegen Exercise 8: Controleren op weggelaten variabelen Exercise 9: Is die verandering beperkt tot de probleemlocatie?

Huidige oefening

Exercise 10: De toename in ongevallen verklaren Exercise 11: Veranderingsoorzaken opsporen met regressie Exercise 12: Wat kun je concluderen?Exercise 13: Conclusie