Explaining the increase in accidents

1. Explaining the increase in accidents

In the final exercises of this case study, you'll try to answer the executive's question - why did the accident rate increase?

2. Finding explanations with multiple regression

You'll use multiple regression to get an answer. When you used multiple linear regression in a previous chapter, you knew that new hires had higher salaries, but you didn't know why. Multiple linear regression allowed you to check whether job level could explain that difference in salary. When the result of the regression was not significant for new hires, you gained confidence that job level was a bigger driver of salary than being a new hire was. Then, in the next chapter, you used multiple logistic regression to check whether job level could also explain the difference between genders in being labeled a high performer. Even though job levels had different proportions of high performers, the difference between men and women was still significant. Both job level and gender were connected with being labeled a high performer. In these final exercises, you'll be using multiple regression in a similar way. You know the accident rate is different between 2016 and 2017, and you've found other variables that could help explain the difference between those years. Putting them together in a regression will tell you which variables are connected to a higher accident rate.

3. Let's practice!

It's time to use what you've learned about multiple regression in the case study.

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This exercise is part of the course

HR Analytics: Exploring Employee Data in R

IntermediateSkill Level

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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: Employee safety Exercise 2: Importing and joining the accident data Exercise 3: Where is the highest accident rate?Exercise 4: Where did the accident rate increase most?Exercise 5: Focusing on the location of interest Exercise 6: Focusing on the problem location Exercise 7: Bringing in more data Exercise 8: Checking for omitted variables Exercise 9: Is that change isolated to the problem location?Exercise 10: Explaining the increase in accidents

Current Exercise

Exercise 11: Using regression to identify change drivers Exercise 12: What can you conclude?Exercise 13: Conclusion