## 3.5 Regression analysis

Finally, let’s run a regression analysis to see if a pirate’s age, weight, and number of tattoos (s)he has predicts how many treasure chests he/she’s found:

# Create a linear regression model: DV = tchests, IV = age, weight, tattoos
tchests.model <- lm(formula = tchests ~ age + weight + tattoos,
data = pirates)

# Show summary statistics
summary(tchests.model)
##
## Call:
## lm(formula = tchests ~ age + weight + tattoos, data = pirates)
##
## Residuals:
##    Min     1Q Median     3Q    Max
## -33.30 -15.83  -6.86   8.41 119.97
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   5.1908     7.1844    0.72     0.47
## age           0.7818     0.1344    5.82    8e-09 ***
## weight       -0.0901     0.0718   -1.25     0.21
## tattoos       0.2540     0.2255    1.13     0.26
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24 on 996 degrees of freedom
## Multiple R-squared:  0.0406, Adjusted R-squared:  0.0377
## F-statistic:   14 on 3 and 996 DF,  p-value: 5.75e-09

It looks like the only significant predictor of the number of treasure chests that a pirate has found is his/her age. There does not seem to be significant effect of weight or tattoos.