library(readr)
library(ggplot2)

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.

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

ggplot(hr, aes(x = satisfaction_level, y = average_montly_hours)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Hours Worked Not Correlated To Satisfaction",
       x = "Satisfaction Level",
       y = "Average Monthly Hours")

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

The p-value is greater than 0.01. The correlation between satisfaction level and average monthly hours is not significant.

There is no correlation between satisfaction level and average monthly hours.

There is no relationship between satisfaction level and average monthly hours worked.

2.

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

ggplot(hr, aes(x = satisfaction_level, y = last_evaluation)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "More Satisfied Employees May Have Higher Evaluations",
       x = "Satisfaction Level",
       y = "Last Evaluation")

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

The p-value is very small. The correlation between satisfaction level and last evaluation is significant.

The correlation is positive and weak.

There is a weak positive relationship between satisfaction level and last evaluation. Although the connection is small, employees who report feeling more satisfied may also tend to have slightly higher evaluations.

3.

cor.test(hr$average_montly_hours , hr$number_project)

## 
##  Pearson's product-moment correlation
## 
## data:  hr$average_montly_hours and hr$number_project
## 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

ggplot(hr, aes(x = average_montly_hours, y = number_project)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "More Projects Tend To Mean More Hours",
       x = "Average Monthly Hours",
       y = "Number Project")

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

The p-value is very small. The correlation between average monthly hours and number project is significant.

The correlation is positive and moderate.

There is a moderate positive relationship between average monthly hours and number project. Employees who work on more projects tend to spend more hours working each month.

4.

cor.test(hr$time_spend_company , hr$last_evaluation)

## 
##  Pearson's product-moment correlation
## 
## data:  hr$time_spend_company and hr$last_evaluation
## t = 16.256, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1158309 0.1472844
## sample estimates:
##       cor 
## 0.1315907

ggplot(hr, aes(x = time_spend_company, y = last_evaluation)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "More Time At Company Shows Slightly Higher Evaluations",
       x = "Time Spent At Company",
       y = "Last Evaluation")

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

The p-value is very small. The correlation between time spend company and last evaluation is significant.

The correlation is positive and weak.

There is a weak positive relationship between time spend company and last evaluation. Employees who have been with the company longer tend to receive slightly higher evaluations.

Assignment 7

Vanessa Rodolico

2024-10-30

1.

The p-value is greater than 0.01. The correlation between satisfaction level and average monthly hours is not significant.

There is no correlation between satisfaction level and average monthly hours.

There is no relationship between satisfaction level and average monthly hours worked.

2.

The p-value is very small. The correlation between satisfaction level and last evaluation is significant.

The correlation is positive and weak.

There is a weak positive relationship between satisfaction level and last evaluation. Although the connection is small, employees who report feeling more satisfied may also tend to have slightly higher evaluations.

3.

The p-value is very small. The correlation between average monthly hours and number project is significant.

The correlation is positive and moderate.

There is a moderate positive relationship between average monthly hours and number project. Employees who work on more projects tend to spend more hours working each month.

4.

The p-value is very small. The correlation between time spend company and last evaluation is significant.

The correlation is positive and weak.

There is a weak positive relationship between time spend company and last evaluation. Employees who have been with the company longer tend to receive slightly higher evaluations.