R Markdown

## 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 between Satisfaction Level & 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
## `geom_smooth()` using formula = 'y ~ x'

Technical Analysis

The p-value is less than 0.1, meaning the correlation is statistically significant.
There correlation is weak and positive

Non-Technical Analysis

The higher an employee’s satisfaction level, the slightly higher their evaluation score.

2. Correlation between Satisfaction Level & Average Monthly 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
## `geom_smooth()` using formula = 'y ~ x'

Technical Analysis

The p-value is less than 0.1, meaning the correlation is statistically significant. The correlation is weak and negative

Non-Technical Analysis

The more satisfied an employee is, the fewer hours they tend to work.

3. Correlation between Number of Projects & Average Monthly 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
## `geom_smooth()` using formula = 'y ~ x'

Technical Analysis

The p-value is less than 0.1, meaning the correlation is statistically significant.
The correlation is strong and positive.

Non-Technical Analysis

The more projects an employee has, the more hours they work.

4. Correlation between Time Spent at Company & Number of Projects

## 
##  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
## `geom_smooth()` using formula = 'y ~ x'

Technical Analysis

The p-value is less than 0.1, meaning the correlation is statistically significant.
The correlation is weak and positive.

Non-Technical Analysis

The longer an employee stays, the slightly more projects they tend to handle.