library(readr)

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. Perform the correlation (.5 point) Choose any two appropriate variables from the data and perform the correlation, displaying the results.

cor.test(hr$average_montly_hours , hr$satisfaction_level)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$average_montly_hours and hr$satisfaction_level
## 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

2. Interpret the results in technical terms (.5 point) For each correlation, explain what the test’s p-value means (significance).

The test p value is over 0.01 meaning there is no relation between the two variables.

3. Interpret the results in non-technical terms (1 point) For each correlation, what do the results mean in non-technical terms.

Average monthly hours does not affect satisfaction level.

4. Create a plot that helps visualize the correlation (.5 point) For each correlation, create a graph to help visualize the relationship between the two variables. The title must be the non-technical interpretation.

library(ggplot2)

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