Discussion11
sudhan_maharjan
April 12, 2019
I have used the dataset who shows different incidents and accidents happended in different airlines betwen 1980s to 2010s.
# dataset from url
airline <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv", header = TRUE)
airline## airline avail_seat_km_per_week incidents_85_99
## 1 Aer Lingus 320906734 2
## 2 Aeroflot* 1197672318 76
## 3 Aerolineas Argentinas 385803648 6
## 4 Aeromexico* 596871813 3
## 5 Air Canada 1865253802 2
## 6 Air France 3004002661 14
## 7 Air India* 869253552 2
## 8 Air New Zealand* 710174817 3
## 9 Alaska Airlines* 965346773 5
## 10 Alitalia 698012498 7
## 11 All Nippon Airways 1841234177 3
## 12 American* 5228357340 21
## 13 Austrian Airlines 358239823 1
## 14 Avianca 396922563 5
## 15 British Airways* 3179760952 4
## 16 Cathay Pacific* 2582459303 0
## 17 China Airlines 813216487 12
## 18 Condor 417982610 2
## 19 COPA 550491507 3
## 20 Delta / Northwest* 6525658894 24
## 21 Egyptair 557699891 8
## 22 El Al 335448023 1
## 23 Ethiopian Airlines 488560643 25
## 24 Finnair 506464950 1
## 25 Garuda Indonesia 613356665 10
## 26 Gulf Air 301379762 1
## 27 Hawaiian Airlines 493877795 0
## 28 Iberia 1173203126 4
## 29 Japan Airlines 1574217531 3
## 30 Kenya Airways 277414794 2
## 31 KLM* 1874561773 7
## 32 Korean Air 1734522605 12
## 33 LAN Airlines 1001965891 3
## 34 Lufthansa* 3426529504 6
## 35 Malaysia Airlines 1039171244 3
## 36 Pakistan International 348563137 8
## 37 Philippine Airlines 413007158 7
## 38 Qantas* 1917428984 1
## 39 Royal Air Maroc 295705339 5
## 40 SAS* 682971852 5
## 41 Saudi Arabian 859673901 7
## 42 Singapore Airlines 2376857805 2
## 43 South African 651502442 2
## 44 Southwest Airlines 3276525770 1
## 45 Sri Lankan / AirLanka 325582976 2
## 46 SWISS* 792601299 2
## 47 TACA 259373346 3
## 48 TAM 1509195646 8
## 49 TAP - Air Portugal 619130754 0
## 50 Thai Airways 1702802250 8
## 51 Turkish Airlines 1946098294 8
## 52 United / Continental* 7139291291 19
## 53 US Airways / America West* 2455687887 16
## 54 Vietnam Airlines 625084918 7
## 55 Virgin Atlantic 1005248585 1
## 56 Xiamen Airlines 430462962 9
## fatal_accidents_85_99 fatalities_85_99 incidents_00_14
## 1 0 0 0
## 2 14 128 6
## 3 0 0 1
## 4 1 64 5
## 5 0 0 2
## 6 4 79 6
## 7 1 329 4
## 8 0 0 5
## 9 0 0 5
## 10 2 50 4
## 11 1 1 7
## 12 5 101 17
## 13 0 0 1
## 14 3 323 0
## 15 0 0 6
## 16 0 0 2
## 17 6 535 2
## 18 1 16 0
## 19 1 47 0
## 20 12 407 24
## 21 3 282 4
## 22 1 4 1
## 23 5 167 5
## 24 0 0 0
## 25 3 260 4
## 26 0 0 3
## 27 0 0 1
## 28 1 148 5
## 29 1 520 0
## 30 0 0 2
## 31 1 3 1
## 32 5 425 1
## 33 2 21 0
## 34 1 2 3
## 35 1 34 3
## 36 3 234 10
## 37 4 74 2
## 38 0 0 5
## 39 3 51 3
## 40 0 0 6
## 41 2 313 11
## 42 2 6 2
## 43 1 159 1
## 44 0 0 8
## 45 1 14 4
## 46 1 229 3
## 47 1 3 1
## 48 3 98 7
## 49 0 0 0
## 50 4 308 2
## 51 3 64 8
## 52 8 319 14
## 53 7 224 11
## 54 3 171 1
## 55 0 0 0
## 56 1 82 2
## fatal_accidents_00_14 fatalities_00_14
## 1 0 0
## 2 1 88
## 3 0 0
## 4 0 0
## 5 0 0
## 6 2 337
## 7 1 158
## 8 1 7
## 9 1 88
## 10 0 0
## 11 0 0
## 12 3 416
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 1 225
## 18 0 0
## 19 0 0
## 20 2 51
## 21 1 14
## 22 0 0
## 23 2 92
## 24 0 0
## 25 2 22
## 26 1 143
## 27 0 0
## 28 0 0
## 29 0 0
## 30 2 283
## 31 0 0
## 32 0 0
## 33 0 0
## 34 0 0
## 35 2 537
## 36 2 46
## 37 1 1
## 38 0 0
## 39 0 0
## 40 1 110
## 41 0 0
## 42 1 83
## 43 0 0
## 44 0 0
## 45 0 0
## 46 0 0
## 47 1 3
## 48 2 188
## 49 0 0
## 50 1 1
## 51 2 84
## 52 2 109
## 53 2 23
## 54 0 0
## 55 0 0
## 56 0 0names(airline)## [1] "airline" "avail_seat_km_per_week"
## [3] "incidents_85_99" "fatal_accidents_85_99"
## [5] "fatalities_85_99" "incidents_00_14"
## [7] "fatal_accidents_00_14" "fatalities_00_14"#get only certain columns for linear regression
library(dplyr)## Warning: package 'dplyr' was built under R version 3.5.1##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# lets see if there is relationship between fatalities and fatal_accidents increment or decrement within these years
x1 <- lm(fatal_accidents_85_99 ~ fatalities_85_99, data = airline)
summary(x1)##
## Call:
## lm(formula = fatal_accidents_85_99 ~ fatalities_85_99, data = airline)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4783 -0.9927 -0.6522 0.2725 11.6570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.992742 0.410248 2.420 0.0189 *
## fatalities_85_99 0.010549 0.002232 4.725 1.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.429 on 54 degrees of freedom
## Multiple R-squared: 0.2925, Adjusted R-squared: 0.2794
## F-statistic: 22.33 on 1 and 54 DF, p-value: 1.681e-05qqnorm(resid(x1))
qqline(resid(x1))
#get only certain columns for linear regression
library(dplyr)
# lets see if there is relationship between fatalities and fatal_accidents increment or decrement within these years
x2 <- lm(fatal_accidents_00_14 ~ fatalities_00_14, data = airline)
summary(x2)##
## Call:
## lm(formula = fatal_accidents_00_14 ~ fatalities_00_14, data = airline)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2449 -0.3628 -0.3628 0.2450 1.5192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3627478 0.0931025 3.896 0.000272 ***
## fatalities_00_14 0.0053670 0.0007538 7.120 2.63e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6224 on 54 degrees of freedom
## Multiple R-squared: 0.4842, Adjusted R-squared: 0.4747
## F-statistic: 50.7 on 1 and 54 DF, p-value: 2.628e-09qqnorm(resid(x2))
qqline(resid(x2))
According to the relationship, there is an increment in the fatalities after the accidents from 80s to 2010s.