wpa7

Q1 Plot the relationship between the following continuous independent variable and treasure. For each plot, add axis and plot labels and a regression line showing the relationship between the independent and dependent variables.

size

cannons

date

decorations

daysfromshore

speed

library(yarrr)

capture.lm <- lm(treasure ~ size,
data = capture)
summary(capture)

##       size          cannons          style        warnshot    
##  Min.   :37.00   Min.   : 0.00   classic: 93   Min.   :0.000  
##  1st Qu.:47.00   1st Qu.:18.00   modern :907   1st Qu.:0.000  
##  Median :50.00   Median :32.00                 Median :0.000  
##  Mean   :50.06   Mean   :32.86                 Mean   :0.202  
##  3rd Qu.:53.00   3rd Qu.:46.00                 3rd Qu.:0.000  
##  Max.   :63.00   Max.   :96.00                 Max.   :1.000  
##       date           heardof       decorations daysfromshore  
##  Min.   :  1.00   Min.   :0.000   Min.   : 1   Min.   : 1.00  
##  1st Qu.: 91.75   1st Qu.:0.000   1st Qu.: 2   1st Qu.: 8.00  
##  Median :185.00   Median :0.000   Median : 4   Median :15.00  
##  Mean   :183.30   Mean   :0.281   Mean   : 4   Mean   :15.43  
##  3rd Qu.:275.00   3rd Qu.:1.000   3rd Qu.: 6   3rd Qu.:23.00  
##  Max.   :365.00   Max.   :1.000   Max.   :10   Max.   :30.00  
##      speed          treasure   
##  Min.   :10.00   Min.   : 835  
##  1st Qu.:18.00   1st Qu.:1595  
##  Median :20.00   Median :1900  
##  Mean   :20.12   Mean   :2104  
##  3rd Qu.:22.00   3rd Qu.:2251  
##  Max.   :29.00   Max.   :5615

plot(x = capture$size,
y = capture$treasure,
xlab = "Size of the ship",
ylab = "Ammount of treasure",
main = "Relation between ship size and treasure", pch = 16, col = gray(.05, .15)
)
abline(capture.lm)

capture.lm.2 <- lm(treasure ~ cannons,
data = capture)


plot(x = capture$cannons,
y = capture$treasure,
xlab = "Canons on ship",
ylab = "Ammount of treasure",
main = "Relation between ship cannons and treasure", pch = 16, col = gray(.05, .15)
)
abline(capture.lm.2,
       col="hotpink")

capture.lm.3 <- lm(treasure ~ date,
data = capture)


plot(x = capture$date,
y = capture$treasure,
xlab = "Date",
ylab = "Ammount of treasure",
main = "Relation between date and treasure", pch = 16, col = gray(.05, .15)
)
abline(capture.lm.3,
       col="green")

capture.lm.4 <- lm(treasure ~ decorations,
data = capture)


plot(x = capture$decorations,
y = capture$treasure,
xlab = "Decorations of a ship",
ylab = "Ammount of treasure",
main = "Relation between ship-decorations and treasure", pch = 16, col = gray(.05, .15)
)
abline(capture.lm.4,
       col="blue")

capture.lm.5 <- lm(treasure ~ daysfromshore,
data = capture)


plot(x = capture$daysfromshore,
y = capture$treasure,
xlab = "Days from shore",
ylab = "Ammount of treasure",
main = "Relation between days off shore and treasure", pch = 16, col = gray(.05, .15)
)
abline(capture.lm.5,
       col="red")

capture.lm.6 <- lm(treasure ~ speed,
data = capture)


plot(x = capture$speed,
y = capture$treasure,
xlab = "Speed of a ship",
ylab = "Ammount of treasure",
main = "Relation between ship-speed and treasure", pch = 16, col = gray(.05, .15)
)
abline(capture.lm.6,
       lwd = 3,
       col="yellow")

Q2 style

warnshot

heardof as IV to D treasure, add regression and pirateplot

pirateplot(dv.name = "treasure",
        iv.name = "style",
        data = capture,
        my.palette = "espresso",
        add.hdi = F
        )

pirateplot(dv.name = "treasure",
        iv.name = "warnshot",
        data = capture,
        my.palette = "drugs",
        add.hdi = F
        )

pirateplot(dv.name = "treasure",
        iv.name = "heardof",
        data = capture,
        my.palette = "monalisa",
        add.hdi = F
        )

Q3 For each of the following variables (separately), calculate the median amount of treasure earned for each level of the IV: style, warnshot, decorations (hint: use aggregate or dplyr!)

aggregate(formula = treasure ~ style,
FUN = median,
na.rm = T,
data = capture
)

##     style treasure
## 1 classic     2000
## 2  modern     1895

aggregate(formula = treasure ~ warnshot,
FUN = median,
na.rm = T,
data = capture
)

##   warnshot treasure
## 1        0     1885
## 2        1     1945

aggregate(formula = treasure ~ decorations,
FUN = median,
na.rm = T,
data = capture
)

##    decorations treasure
## 1            1   2657.5
## 2            2   1780.0
## 3            3   1905.0
## 4            4   1797.5
## 5            5   1880.0
## 6            6   1855.0
## 7            7   1920.0
## 8            8   1935.0
## 9            9   1935.0
## 10          10   1955.0

Q4

corr.capture<-cor.test(~ cannons + size, 
         data = capture)
names(corr.capture)

## [1] "statistic"   "parameter"   "p.value"     "estimate"    "null.value" 
## [6] "alternative" "method"      "data.name"   "conf.int"

corr.capture$p.value

## [1] 0.3656786

capture.lm.new<-lm(size ~ cannons,
data = capture)

summary(capture.lm.new)

## 
## Call:
## lm(formula = size ~ cannons, data = capture)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.9844  -2.8873  -0.0596   2.9028  13.1409 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 49.859132   0.262458 189.970   <2e-16 ***
## cannons      0.006266   0.006923   0.905    0.366    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.14 on 998 degrees of freedom
## Multiple R-squared:  0.00082,    Adjusted R-squared:  -0.0001812 
## F-statistic: 0.819 on 1 and 998 DF,  p-value: 0.3657