Assignment 7

## 
##  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

• p-value interpretation: The p-value is very small, meaning that their is a significance between the satisfaction level and last evaluation.

• correlation interpretation: The correlation is positive and weak.

• non-technical interpretation: The higher they score on their evaluations, there is a chance they would have a higher satisfaction level.

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

• p-value interpretation: The p-value is very small, meaning there is a significance between the time spent at the company and average monthly hours.

• Correlation interpretation: The correlation is positive and weak.

• non-technical interpretation: If an employee has worked at the company for a long time, there is a chance that they are slightly likely to work longer hours.

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

• p-value interpretation: The p-value is very small, meaning there is a significance in correlation.

• correlation interpretation: The correlation is weak and negative.

• non-technical interpretation: As satisfaction levels increase, the number of projects decreases.

## 
##  Pearson's product-moment correlation
## 
## data:  hr$last_evaluation and hr$number_project
## t = 45.656, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3352028 0.3633053
## sample estimates:
##       cor 
## 0.3493326

• p-value interpretation: The p-value is very small, therefore there is a correlation between last evaluation and number of projects.

• correlation interpretation: The correlation is positive and moderate.

• non-technical interpretation: If people have a high number of projects, that means there is a moderate chance of them having better evaluations.