Salaries

Package car

library(car)
## Loading required package: carData
data("Salaries")
summary(Salaries)
##         rank     discipline yrs.since.phd    yrs.service        sex     
##  AsstProf : 67   A:181      Min.   : 1.00   Min.   : 0.00   Female: 39  
##  AssocProf: 64   B:216      1st Qu.:12.00   1st Qu.: 7.00   Male  :358  
##  Prof     :266              Median :21.00   Median :16.00               
##                             Mean   :22.31   Mean   :17.61               
##                             3rd Qu.:32.00   3rd Qu.:27.00               
##                             Max.   :56.00   Max.   :60.00               
##      salary      
##  Min.   : 57800  
##  1st Qu.: 91000  
##  Median :107300  
##  Mean   :113706  
##  3rd Qu.:134185  
##  Max.   :231545
Scatter Plot
library(ggplot2)
ggplot(Salaries,aes(yrs.service,salary,color=rank,
                    shape=discipline))+ geom_point()

The Arithmetic Mean of Salaries is 113706.5.

Linear Regression for Salaries
fit <- lm(salary~., data = Salaries)
sfit<-summary(fit)
sfit
## 
## Call:
## lm(formula = salary ~ ., data = Salaries)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -65248 -13211  -1775  10384  99592 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    65955.2     4588.6  14.374  < 2e-16 ***
## rankAssocProf  12907.6     4145.3   3.114  0.00198 ** 
## rankProf       45066.0     4237.5  10.635  < 2e-16 ***
## disciplineB    14417.6     2342.9   6.154 1.88e-09 ***
## yrs.since.phd    535.1      241.0   2.220  0.02698 *  
## yrs.service     -489.5      211.9  -2.310  0.02143 *  
## sexMale         4783.5     3858.7   1.240  0.21584    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22540 on 390 degrees of freedom
## Multiple R-squared:  0.4547, Adjusted R-squared:  0.4463 
## F-statistic:  54.2 on 6 and 390 DF,  p-value: < 2.2e-16

The Coefficient of Determination, R2= 0.4546766.