hr <- read_csv("https://raw.githubusercontent.com/aiplanethub/Datasets/refs/heads/master/HR_comma_sep.csv")
## Rows: 14999 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Department, salary
## dbl (8): satisfaction_level, last_evaluation, number_project, average_montly...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

1. Correlation: Satisfaction Level with 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

Technical interpretation:

The p-value is < alpha (0.001) therefore we reject the Ho and say there is a positive and small correlation between last evaluation and satisfaction level.

Non-Technical Interpretation:

As evaluations increase, satisfaction increases slightly.

ggplot(hr, aes(x = satisfaction_level, y = last_evaluation)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "As evaluations increase,\nsatisfaction level increases slightly",
    x = "Satisfaction Level",
    y = "Last Evaluation"
  )
## `geom_smooth()` using formula = 'y ~ x'

2. Correlation: Number of Projects with Average 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:

The p-value is < alpha (0.001) therefore we reject the Ho and say there is a positive and moderate correlation between the number of projects and average monthly hours.

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, color = "red") +
  labs(
    title = "Employees with more projects work more hours",
    x = "Number of Projects",
    y = "Average Monthly Hours"
  )
## `geom_smooth()` using formula = 'y ~ x'

3. Correlation: Time at Company with Number of 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:

The p-value is < alpha (0.001) therefore we reject the Ho and say there is a positive and small correlation between time spent at the company and number of projects.

Non-Technical Interpretation:

Employees who have been at the company longer tend to be assigned more projects.

ggplot(hr, aes(x = time_spend_company, y = number_project)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Employees who have been at the company longer\ntend to be assigned more projects",
    x = "Time Spent at Company",
    y = "Number of Projects"
  )
## `geom_smooth()` using formula = 'y ~ x'

4. Correlation: Satisfaction Level with 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

Technical interpretation:

The p-value is < alpha (0.001) therefore we reject the Ho and say there is a negative and very small correlation between satisfaction level and average monthly hours.

Non-Technical Interpretation:

Employees who work more hours tend to be slightly less satisfied.

ggplot(hr, aes(x = average_montly_hours, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Employees working more hours\ntend to be slightly less satisfied",
    x = "Average Monthly Hours",
    y = "Satisfaction Level"
  )
## `geom_smooth()` using formula = 'y ~ x'