1. Sample description

SOME TEXT HERE ABOUT YOUR DATA.

0# replace this by a basic sample description (by applying row(), table(), means(), sd(), summary(), ... (whatever applies best)
## [1] 0
df$salary <- as.numeric(df$salary)
df$years_exp <- as.numeric(df$years_exp)
df$gender <- as.factor(df$gender)

nrow(df)
## [1] 200
mean_salary <- mean(df$salary, na.rm=T)
mean_years <- mean(df$years_exp, na.rm=T)
table(mean_salary)
## mean_salary
## 108490.894848091 
##                1
table(mean_years)
## mean_years
## 15.6664792847936 
##                1
sd_salary <- sd(df$salary)
sd_years <- sd(df$ears_exp)
summary(df$salary)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   30028   60076   93164  108491  150437  255381


2. Association between years and salary as scatterplot.

The plot describes the relationship between years and employment (independent variable) and salary (dependent variable).The association appears to be positive.

The R value tells 0.9396925

plot(df$years_exp, df$salary)

lm(df$years_exp ~ df$salary)
## 
## Call:
## lm(formula = df$years_exp ~ df$salary)
## 
## Coefficients:
## (Intercept)    df$salary  
##    1.232249     0.000133
cor(df$years_exp, df$salary, use = "complete.obs", method = "pearson")
## [1] 0.9129636
cor(df$years_exp, df$salary, use = "complete.obs", method = "spearman")
## [1] 0.9396925


3. Estimate salary by years of employment

A non-liner relationship can be observed between the salary and the years of employment. a liner regression model is being applied to check the association with the salary variable.

df$salary_model <- log(df$salary)
model <- lm(df$salary_model ~ df$year)
summary(model)
## 
## Call:
## lm(formula = df$salary_model ~ df$year)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74993 -0.11686  0.00666  0.11146  0.77461 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 10.436444   0.032197  324.14   <2e-16 ***
## df$year      0.063322   0.001795   35.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2218 on 198 degrees of freedom
## Multiple R-squared:  0.8628, Adjusted R-squared:  0.8621 
## F-statistic:  1245 on 1 and 198 DF,  p-value: < 2.2e-16


4. Interpretation

The model descript positive linear realationship between year and salary model.


5. (Voluntary) Gender effects

SOME TEXT HERE OR DELETE THIS SECTION.

0# replace this by two regression models, separated by gender. 
## [1] 0

SOME TEXT HERE TO INTERPRET YOUR MODEL OUTPUT.