1. Correlation between Satisfaction Level and Last 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

P-value interpretation: The p-value is very small, therefore the correlation between Satisfaction levels and last evaluations is significant.

Correlation interpretation in technical terms: The correlation is positive and small

Non-technical interpretation: Increase in Satifaction level, slight increase in Last evaluations

library(ggplot2)

ggplot(hr, aes(x = satisfaction_level , y =last_evaluation )) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Increase in Satisfaction level, Slight increase in Last Evaluation",
       x = "Satisfaction Level",
       y = "Last Evaluation")
## `geom_smooth()` using formula = 'y ~ x'

2. Correlation between Satisfaction Level and Number of Projects

cor.test(hr$satisfaction_level, hr$number_project)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$satisfaction_level and hr$number_project
## t = -17.69, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1586105 -0.1272570
## sample estimates:
##        cor 
## -0.1429696

The p-value is within the 0.01 cutoff point. The correlation suggests that there is a weak negative correlation.

Correlation interpretation in technical terms: There is a weak negative correlation

Non-technical interpretation: satisfaction level decreases, the number of projects increases slightly.

ggplot(hr, aes(x = number_project, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "As Satisfaction Level Decreases, the Number of Projects Increases Slightly",
       x = "Number of Projects",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'

3. Correlation between Satisfaction Level and Average 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

The p-value exceeded the 0.01 cutoff point.

Correlation interpretation in technical terms:The correlation is between -0.1 and 0 whichs suggests no correlation.

Non-technical interpretation: There is no relationship between Average Monthly Hours and Satisfaction Level.

ggplot(hr, aes(x = average_montly_hours, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "There is no Relationship Between Average Monthly Hours and Satisfaction Level",
       x = "Average Monthly Hours",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'

## 4. Correlation between Satisfaction Level and Time Spent at the Company

cor.test(hr$satisfaction_level, hr$time_spend_company)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$satisfaction_level and hr$time_spend_company
## t = -12.416, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.11668153 -0.08499948
## sample estimates:
##        cor 
## -0.1008661

The p-value is within the 0.01 cutoff point.

Correlation interpretation in technical terms:The correlation test suggests that there is a weak negative correlation.

Non-technical interpretation: As satisfaction level decreases, the time spent at the company increases.

ggplot(hr, aes(x = time_spend_company, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "As Satisfaction Level Decreases, the Time spent at the Company Increases",
       x = "Time Spent at Company",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'