Data 3210 Assignment 7

Correlation One

library(readr)
library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

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.

View(hr)

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 = average_montly_hours, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "purple") +
  labs(title = "Minimal to no relationship between average monthly hours and satisfaction level",
       x = "Average Monthly Hours",
       y = "Satisfaction Level")

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

P-value interpretation: The p-value isn’t to small but small enough to likely mean that there’s some statistical significance.

Correlation estimate interpretation: The correlation weak and negative.

Non-technical interpretation: There is minimal to no relationship between average monthly hours and satisfaction level.

Correlation Two

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 = number_project, y = average_montly_hours)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "purple") +
  labs(title = "More projects will result in more average monthly hours",
       x = "Number Of Projects",
       y = "Average Monthly Hours")

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

P-value interpretation: The P value shows that the correlation is statistically significant.

Correlation estimate interpretation: The correlation is moderately positive.

Non-technical interpretation: More projects will result in more average monthly hours.

Correlation Three

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

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

ggplot(hr, aes(x = time_spend_company, y = number_project)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "purple") +
  labs(title = "More time spent at the company increases the number of projects",
       x = "Time Spend Company",
       y = "Number Of Projects")

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

P-value interpretation: The P value shows that the correlation is statistically significant.

Correlation estimate interpretation: The correlation is a weak positive relationship.

Non-technical interpretation: Generally the more time is spent at the company the number of projects increases.

Correlation Four

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

## 
##  Pearson's product-moment correlation
## 
## data:  hr$average_montly_hours and hr$time_spend_company
## t = 15.774, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1119801 0.1434654
## sample estimates:
##       cor 
## 0.1277549

ggplot(hr, aes(x = time_spend_company, y = average_montly_hours)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "purple") +
  labs(title = "Average monthly hours generally go up as time spent increases",
       x = "Time Spend Company",
       y = "Average Monthly Hours")

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

P-value interpretation: The P value shows that the correlation is statistically significant.

Correlation estimate interpretation: The correlation is a weak positive relationship.

Non-technical interpretation: Average monthly hours generally go up as time spent at the company increases.