Correlation 1: Satisfaction vs Evaluation

cor.test(hr$satisfaction_level, hr$last_evaluation)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$satisfaction_level and hr$last_evaluation
## t = 12.933, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08916727 0.12082195
## sample estimates:
##       cor 
## 0.1050212

Technical Interpretation:
The p-value tells us whether the relationship is statistically significant. If the p-value is less than 0.05, the relationship is significant.

Non-Technical Interpretation:
Employee satisfaction and evaluation scores are related.

ggplot(hr, aes(x = last_evaluation, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Employee Satisfaction and Evaluation Scores Are Related",
       x = "Last Evaluation",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'

Correlation 2: Projects vs Monthly Hours

cor.test(hr$number_project, hr$average_montly_hours)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$number_project and hr$average_montly_hours
## t = 56.219, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4039037 0.4303411
## sample estimates:
##       cor 
## 0.4172106

Technical Interpretation:
A small p-value (less than 0.05) indicates a statistically significant relationship.

Non-Technical Interpretation:
Employees with more projects tend to work more hours.

ggplot(hr, aes(x = number_project, y = average_montly_hours)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "More Projects Lead to More Work Hours",
       x = "Number of Projects",
       y = "Average Monthly Hours")
## `geom_smooth()` using formula = 'y ~ x'

Correlation 3: Time at Company vs Projects

cor.test(hr$time_spend_company, hr$number_project)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$time_spend_company and hr$number_project
## t = 24.579, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1813532 0.2121217
## sample estimates:
##       cor 
## 0.1967859

Technical Interpretation:
If the p-value is less than 0.05, the relationship is statistically significant.

Non-Technical Interpretation:
Time at the company influences how many projects employees handle.

ggplot(hr, aes(x = time_spend_company, y = number_project)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Time at Company Influences Project Load",
       x = "Time Spent at Company",
       y = "Number of Projects")
## `geom_smooth()` using formula = 'y ~ x'

Correlation 4: Satisfaction vs Monthly Hours

cor.test(hr$satisfaction_level, hr$average_montly_hours)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$satisfaction_level and hr$average_montly_hours
## t = -2.4556, df = 14997, p-value = 0.01408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.036040356 -0.004045605
## sample estimates:
##         cor 
## -0.02004811

Technical Interpretation:
A p-value less than 0.05 indicates the relationship is statistically significant.

Non-Technical Interpretation:
Work hours affect employee satisfaction.

ggplot(hr, aes(x = average_montly_hours, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Work Hours Affect Employee Satisfaction",
       x = "Average Monthly Hours",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'