Assignment 7
Kai Ostaszewski, Michael
2025-03-25
Correlation 1
##
## 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
## 0.1050212We reject the Ho because the p-value is < .01
The correlation is positive and weak
An increase in the last evaluation will increase satisfaction slightly
##
## Call:
## lm(formula = last_evaluation ~ satisfaction_level, data = hr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37337 -0.15433 0.00013 0.15158 0.31953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.671793 0.003697 181.70 <2e-16 ***
## satisfaction_level 0.072302 0.005591 12.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1702 on 14997 degrees of freedom
## Multiple R-squared: 0.01103, Adjusted R-squared: 0.01096
## F-statistic: 167.3 on 1 and 14997 DF, p-value: < 2.2e-16Correlation 2
##
## Pearson's product-moment correlation
##
## data: hr$number_project and hr$last_evaluation
## 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#Correlation Coefficient (r = 0.3493): ## The correlation between
number of projects and last evaluation is positive and moderate (r ≈
0.35).
## As the number of projects increases, the last evaluation score tends
to increase as well.
#P-Value (< 2.2e-16): ## The p-value is extremely small (less than 0.05), which means we reject the null hypothesis. ## This confirms that the relationship between these two variables is significant and unlikely due to chance.
Confidence Interval (0.3352, 0.3633):
95% confident that the true correlation lies between 0.335 and 0.363, supporting a moderate positive relationship.
Employees who have completed more projects tend to receive higher evaluation scores.
However, the relationship is moderate, meaning other factors also contribute to an employee’s evaluation beyond just the number of projects they complete.
##
## Call:
## lm(formula = last_evaluation ~ satisfaction_level, data = hr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37337 -0.15433 0.00013 0.15158 0.31953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.671793 0.003697 181.70 <2e-16 ***
## satisfaction_level 0.072302 0.005591 12.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1702 on 14997 degrees of freedom
## Multiple R-squared: 0.01103, Adjusted R-squared: 0.01096
## F-statistic: 167.3 on 1 and 14997 DF, p-value: < 2.2e-16correlation 3
##
## 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
## -0.02004811Correlation Coefficient (r = -0.0200):
The correlation between satisfaction level and average monthly hours is very weak and negative (close to zero).
This suggests that there is almost no meaningful relationship between these two variables.
P-Value (0.01408):
The p-value is less than 0.05, meaning we reject the null hypothesis.
However, since the correlation coefficient is very close to zero, the statistical significance does not imply a meaningful relationship.
Confidence Interval (-0.0360, -0.0040):
The true correlation is likely between -0.036 and -0.004, confirming the relationship is very weak and slightly negative.
There is no strong relationship between how many hours employees work per month and their job satisfaction.
There is a slight trend suggesting that working more hours is linked to lower satisfaction, but the effect is very small and likely not meaningful in the real-world ”
##
## Call:
## lm(formula = average_montly_hours ~ satisfaction_level, data = hr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -106.914 -45.176 -0.619 43.985 109.301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 203.518 1.085 187.648 <2e-16 ***
## satisfaction_level -4.027 1.640 -2.456 0.0141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49.93 on 14997 degrees of freedom
## Multiple R-squared: 0.0004019, Adjusted R-squared: 0.0003353
## F-statistic: 6.03 on 1 and 14997 DF, p-value: 0.01408correlation 4
##
## 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
## 0.1967859Correlation Coefficient (r = 0.1968):
The correlation between time spent at the company and number of projects is positive but weak (r ≈ 0.20).
This suggests that employees who have been at the company longer tend to work on more projects, but the relationship is not strong.
P-Value (< 2.2e-16):
The p-value is extremely small (less than 0.05), meaning we reject the null hypothesis (H₀: no correlation).
This confirms the relationship is significant, though weak.
#Confidence Interval (0.1814, 0.2121): ## 95% confident that the true correlation lies between 0.181 and 0.212, further supporting a weak positive relationship.
Employees who have been at the company longer tend to take on more projects.
However, the connection is not very strong, meaning that while experience might play a role, other factors likely influence how many projects an employee works on.
