Correlation 1: Satisfaction Levels vs. average monthly hours
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
Interpret the Results in technical Terms: The p-value exceeded the
0.01 cutoff point.
Interpret the Results in Non-technical terms: As average monthly
hours increases, satisfaction level slightly decreases.
ggplot(hr, aes(x = average_montly_hours, y = satisfaction_level)) +
geom_point() + geom_smooth(method = "lm", se = FALSE, color = "red") + labs(title = "There is No Relationship Between Satisfaction Level and Average Monthly Hours", x = "Average Monthly Hours", y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'
Correlation 2:
Interpret the Results in Technical terms:
Interpret the Results in non-technical terms:
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
ggplot(hr, aes(x = number_project, y = satisfaction_level)) +
geom_point() + geom_smooth(method = "lm", se = FALSE, color = "red") + labs(title = "Satisfaction Level Decreases The Number of Projects Increases Slightly", x = "Number Project", y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'
Correlation 3:
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
Interpret in technical terms: The
Interpret in non-technical terms:
ggplot(hr, aes(x = last_evaluation, y = satisfaction_level)) + geom_point() +geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(title = "As Satisfaction Level increases, Ther Evaluation Score Increases",x = "Last Evaluation",y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'
Correlation 4:
cor.test(hr$satisfaction_level, hr$time_spend_company)
##
## Pearson's product-moment correlation
##
## data: hr$satisfaction_level and hr$time_spend_company
## t = -12.416, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11668153 -0.08499948
## sample estimates:
## cor
## -0.1008661
Interpret in technical terms: The
Interpret in non-technical terms:
ggplot(hr, aes(x = time_spend_company, y = satisfaction_level)) +
geom_point() + geom_smooth(method = "lm", se = FALSE, color = "red") + labs(title = "As Satisfaction Level increases, Ther Evaluation Score Increases", x = "Time Spend Money",y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'
