Assignment 7

Load the dataset

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.

head(hr)

## # A tibble: 6 × 10
##   satisfaction_level last_evaluation number_project average_montly_hours
##                <dbl>           <dbl>          <dbl>                <dbl>
## 1               0.38            0.53              2                  157
## 2               0.8             0.86              5                  262
## 3               0.11            0.88              7                  272
## 4               0.72            0.87              5                  223
## 5               0.37            0.52              2                  159
## 6               0.41            0.5               2                  153
## # ℹ 6 more variables: time_spend_company <dbl>, Work_accident <dbl>,
## #   left <dbl>, promotion_last_5years <dbl>, Department <chr>, salary <chr>

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Correlation 1: Satisfaction vs. 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 tells us if the relationship between satisfaction and last evaluation is statistically significant.
A small p-value (< 0.05) means the correlation is significant.

Non-Technical Interpretation

Employees who are more satisfied tend to also have higher evaluation scores.

ggplot(hr, aes(x = last_evaluation, y = satisfaction_level)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Higher Satisfaction is Linked to Higher Evaluation Scores",
       x = "Last Evaluation Score",
       y = "Satisfaction Level")

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

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Correlation 2: Average Monthly Hours vs. Last Evaluation

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

## 
##  Pearson's product-moment correlation
## 
## data:  hr$average_montly_hours and hr$last_evaluation
## t = 44.237, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3255078 0.3538218
## sample estimates:
##       cor 
## 0.3397418

Technical Interpretation

The p-value indicates whether average monthly hours and evaluation score are significantly related.
If p < 0.05, the correlation is significant.

Non-Technical Interpretation

Employees who work more hours tend to have higher evaluation scores.

ggplot(hr, aes(x = average_montly_hours, y = last_evaluation)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Employees Working More Hours Tend to Have Higher Evaluations",
       x = "Average Monthly Hours",
       y = "Last Evaluation Score")

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

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Correlation 3: Years at Company vs. 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 tells us whether years at the company and number of projects have a significant correlation.
If p < 0.05 → statistically significant.

Non-Technical Interpretation

Employees who spend more years at the company tend to work on more projects.

ggplot(hr, aes(x = time_spend_company, y = number_project)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "More Years at the Company Means More Projects Handled",
       x = "Years at Company",
       y = "Number of Projects")

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

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Correlation 4: Satisfaction Level vs. Number of Projects

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

## 
##  Pearson's product-moment correlation
## 
## data:  hr$satisfaction_level and hr$number_project
## t = -17.69, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1586105 -0.1272570
## sample estimates:
##        cor 
## -0.1429696

Technical Interpretation

The p-value tells us if satisfaction and number of projects have a statistically significant relationship.

Non-Technical Interpretation

Employees with more projects tend to be less satisfied.

ggplot(hr, aes(x = number_project, y = satisfaction_level)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Employees With More Projects Tend to Be Less Satisfied",
       x = "Number of Projects",
       y = "Satisfaction Level")

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

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