Data_Dive_Week_10
Rohan Royal
2023-10-30
Loading data
player_data <-read.csv('C:/Users/rohan/OneDrive/Desktop/INTRO TO STATISTICS IN R/DATA SETS/Datasets/Data/Nba_all_seasons_1996_2021.csv')
summary(player_data)## X player_name team_abbreviation age
## Min. : 0 Length:12305 Length:12305 Min. :18.00
## 1st Qu.: 3076 Class :character Class :character 1st Qu.:24.00
## Median : 6152 Mode :character Mode :character Median :26.00
## Mean : 6152 Mean :27.08
## 3rd Qu.: 9228 3rd Qu.:30.00
## Max. :12304 Max. :44.00
## player_height player_weight college country
## Min. :160.0 Min. : 60.33 Length:12305 Length:12305
## 1st Qu.:193.0 1st Qu.: 90.72 Class :character Class :character
## Median :200.7 Median : 99.79 Mode :character Mode :character
## Mean :200.6 Mean :100.37
## 3rd Qu.:208.3 3rd Qu.:108.86
## Max. :231.1 Max. :163.29
## draft_year draft_round draft_number gp
## Length:12305 Length:12305 Length:12305 Min. : 1.00
## Class :character Class :character Class :character 1st Qu.:31.00
## Mode :character Mode :character Mode :character Median :57.00
## Mean :51.29
## 3rd Qu.:73.00
## Max. :85.00
## pts reb ast net_rating
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :-250.000
## 1st Qu.: 3.600 1st Qu.: 1.800 1st Qu.: 0.600 1st Qu.: -6.400
## Median : 6.700 Median : 3.000 Median : 1.200 Median : -1.300
## Mean : 8.173 Mean : 3.559 Mean : 1.814 Mean : -2.256
## 3rd Qu.:11.500 3rd Qu.: 4.700 3rd Qu.: 2.400 3rd Qu.: 3.200
## Max. :36.100 Max. :16.300 Max. :11.700 Max. : 300.000
## oreb_pct dreb_pct usg_pct ts_pct
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.02100 1st Qu.:0.096 1st Qu.:0.1490 1st Qu.:0.4800
## Median :0.04100 Median :0.131 Median :0.1810 Median :0.5240
## Mean :0.05447 Mean :0.141 Mean :0.1849 Mean :0.5111
## 3rd Qu.:0.08400 3rd Qu.:0.180 3rd Qu.:0.2170 3rd Qu.:0.5610
## Max. :1.00000 Max. :1.000 Max. :1.0000 Max. :1.5000
## ast_pct season
## Min. :0.0000 Length:12305
## 1st Qu.:0.0660 Class :character
## Median :0.1030 Mode :character
## Mean :0.1314
## 3rd Qu.:0.1780
## Max. :1.0000Selecting the column for the binary variable
Let’s consider the “draft_round” column as the interesting binary variable for modeling.The round a player was drafted in is represented by this column, which can be transformed into a binary variable that indicates if a player was drafted in the first round (1) or not (0).
# Convert draft_round column into a binary variable (1 for first round, 0 for other rounds)
player_data$draft_round_binary <- ifelse(player_data$draft_round == 1, 1, 0)
print(player_data$draft_round_binary)## [1] 0 1 0 1 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 1 0 0 1 1
## [37] 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 1 1 0 0 0 0 1
## [73] 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 0 1 1 1 1 1 0 1
## [109] 1 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1 0 0 1 0 0 0 1 1 1 1 1 1 0 1 1 0 1 1 0
## [145] 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1
## [181] 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 1
## [217] 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 1 0
## [253] 0 1 1 0 0 0 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0
## [289] 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0
## [325] 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1
## [361] 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1
## [397] 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 1 0 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 0 1 0
## [433] 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0
## [469] 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 1 1 0 1 1
## [505] 0 1 1 1 0 0 1 0 1 1 1 0 0 1 1 0 1 1 1 1 0 0 1 1 0 0 0 1 0 1 1 1 1 1 0 1
## [541] 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1
## [577] 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1
## [613] 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1 0 0 1 0 1 1 0 0 0 0 0 1
## [649] 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0
## [685] 0 1 0 0 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0
## [721] 1 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 1 0 1 1 1 1
## [757] 1 1 0 0 1 0 0 0 0 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1
## [793] 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 1 0
## [829] 1 0 1 0 1 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 1 0
## [865] 0 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 1 0 1 0 1 0 0 0 1
## [901] 0 0 0 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 0
## [937] 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1
## [973] 1 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0
## [1009] 0 1 0 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 0 1
## [1045] 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1
## [1081] 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 1 1 0 1 1 1
## [1117] 1 0 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 1 1 1 1 0 1
## [1153] 1 0 0 0 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 0 1 0 0 0 0 1 1 1 0 1 0 1
## [1189] 1 1 0 0 1 1 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 1 1 0 0
## [1225] 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 0 0 1 0 0 1 1
## [1261] 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0 1 0 0
## [1297] 1 0 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1
## [1333] 0 1 1 1 0 1 1 0 1 0 0 1 1 1 1 0 0 0 0 0 1 1 0 1 1 1 0 0 1 0 1 1 1 0 1 1
## [1369] 1 1 1 0 0 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 0 1 1 1 1
## [1405] 1 1 0 0 1 1 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 1 1 1 1 0 1 1 0 0
## [1441] 1 1 1 1 0 0 1 1 0 1 0 1 0 0 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0
## [1477] 0 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0
## [1513] 0 0 1 1 1 0 1 1 0 1 0 1 0 1 0 1 1 1 1 0 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0
## [1549] 1 1 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1
## [1585] 1 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1
## [1621] 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1 0 0 1
## [1657] 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1
## [1693] 1 1 1 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 1
## [1729] 1 0 1 0 0 0 0 0 1 0 1 1 1 0 0 1 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 0 1 1 1
## [1765] 1 0 1 1 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1
## [1801] 0 0 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1
## [1837] 0 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 1 1 1
## [1873] 1 0 0 0 0 0 1 1 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0
## [1909] 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 1
## [1945] 1 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 0 1 1
## [1981] 0 1 1 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1
## [2017] 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 1 0 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1
## [2053] 0 1 1 0 0 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1
## [2089] 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1
## [2125] 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 0 1
## [2161] 0 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 0 0 1 0 1 1 1 0
## [2197] 1 1 0 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1
## [2233] 1 1 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0
## [2269] 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1
## [2305] 1 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 0 0 0 1
## [2341] 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 0 0
## [2377] 1 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 0 0 1
## [2413] 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 0 1 1 0 1 0 0 1 1 0 0 0 1 0 1 0
## [2449] 1 0 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 1
## [2485] 0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 1 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 1
## [2521] 0 1 0 0 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 1
## [2557] 0 1 1 1 1 1 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 1
## [2593] 1 0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 1 1
## [2629] 1 0 1 1 0 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1
## [2665] 0 1 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 1
## [2701] 0 1 0 1 0 0 0 1 0 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 0 0 1 1 1 1 1 0 1 0 1 1
## [2737] 0 1 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 1 0
## [2773] 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1
## [2809] 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 0 1 0 1 1 1 0 1
## [2845] 0 1 0 1 0 0 1 1 0 1 1 1 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 0 1 1 0 0
## [2881] 0 1 0 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1
## [2917] 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 0 1 1 0
## [2953] 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 1 0 1 1 1 1 1 1
## [2989] 0 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1 1 0 1 0 0 0
## [3025] 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 1 0 0 1 1 0 0 1 0 1 0 1 0 1 1 1 1 0 1 1 1
## [3061] 1 0 0 0 1 0 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 0 1 0
## [3097] 1 1 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1
## [3133] 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 0 1 0 0 0 0 1 0
## [3169] 1 0 0 0 0 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1
## [3205] 1 1 0 0 0 0 1 1 0 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1
## [3241] 0 1 0 0 1 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1 0 1 0 0 0 0 1
## [3277] 0 1 1 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1
## [3313] 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 1 0 1 1 1 0 1 1 0 1 0 0 1 1 0 0 0
## [3349] 1 0 1 0 1 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 0
## [3385] 1 1 0 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 1 1 1 0 1
## [3421] 0 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 0 1 0
## [3457] 1 0 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1
## [3493] 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0 1 1 0 1 1
## [3529] 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 1 0 1
## [3565] 0 1 1 1 1 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1
## [3601] 1 1 1 1 1 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 1 0 1 1 0
## [3637] 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1
## [3673] 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 0 0 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 0 1
## [3709] 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1
## [3745] 1 1 1 1 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 0 0 0 1 0 1 1
## [3781] 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0
## [3817] 0 1 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1
## [3853] 0 0 1 0 0 1 0 1 1 0 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1
## [3889] 1 1 1 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0
## [3925] 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1
## [3961] 1 1 1 0 1 1 1 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0
## [3997] 0 1 1 1 1 0 1 1 1 1 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0
## [4033] 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 1
## [4069] 0 0 1 1 0 1 1 1 1 0 1 0 1 0 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0
## [4105] 0 1 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 1 1 0 1 0 1 1 1
## [4141] 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 1
## [4177] 1 0 1 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0
## [4213] 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1
## [4249] 0 1 1 0 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 1 0 1
## [4285] 1 0 0 0 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1
## [4321] 0 1 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1
## [4357] 0 0 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 1 1 1 1 1 1 0
## [4393] 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1
## [4429] 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0
## [4465] 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 1 1 0 0 1
## [4501] 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1 0 1 1
## [4537] 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 0 0 0 1 1 1
## [4573] 0 0 0 1 1 0 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1 1 1 1 0 0 1
## [4609] 1 0 1 1 1 0 1 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1
## [4645] 1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 1 0 1 0
## [4681] 0 1 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1
## [4717] 0 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 0 1 0 0 1 1 0 0 0 0 1 1
## [4753] 0 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 1 0 1 1 0 1 0
## [4789] 0 1 1 0 0 1 0 1 1 1 0 0 0 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 1 0
## [4825] 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 0 0
## [4861] 1 0 0 0 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 1 1 1 0 1
## [4897] 1 0 0 1 1 0 0 0 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1 1 0 0 0 0 1 1 1 1 1
## [4933] 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0
## [4969] 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1
## [5005] 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1 0 1 0 1
## [5041] 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1
## [5077] 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 1 1
## [5113] 0 1 0 1 1 0 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1
## [5149] 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1
## [5185] 1 1 1 1 0 1 1 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1
## [5221] 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1 0 1 0 1 0
## [5257] 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1 0 0 1 0 0 1 0 1 1 0
## [5293] 0 1 1 1 1 0 1 0 1 1 1 0 1 0 0 0 1 1 1 1 0 1 1 1 0 1 0 1 0 1 1 0 0 1 1 1
## [5329] 1 0 1 1 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 1
## [5365] 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1
## [5401] 0 1 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 1 1 1 0 1 0 0 1 0 1 0 0 0 1 0 0 1 1
## [5437] 1 0 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 0 0 1 1 1
## [5473] 1 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 0 1
## [5509] 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1
## [5545] 0 1 1 0 1 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1
## [5581] 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0
## [5617] 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 0 0 1 1
## [5653] 1 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1
## [5689] 0 1 1 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 1
## [5725] 1 0 0 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 1
## [5761] 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0
## [5797] 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 1 0
## [5833] 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 0 0 1 1 0 0 0 1 0 1 1
## [5869] 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0
## [5905] 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0
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## [12277] 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0# Now player_data$draft_round_binary contains the binary variable for modelingThe ifelse function is used to create a new column called draft_round_binary. It does this by assigning a value of 1 if the draft_round equals 1 (which indicates the first round), and 0 otherwise. For modelling purposes, you can now use nba_data$draft_round_binary as your binary variable.
Logistic regeression
# Selecting explanatory variables
explanatory_vars <- c("age", "pts", "reb", "ast")
# Building the logistic regression model
logit_model <- glm(draft_round_binary ~ age + pts + reb + ast, data = player_data, family = binomial)
# Summarize the model
summary(logit_model)##
## Call:
## glm(formula = draft_round_binary ~ age + pts + reb + ast, family = binomial,
## data = player_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.306510 0.128299 -10.183 <2e-16 ***
## age 0.005736 0.004573 1.254 0.210
## pts 0.120911 0.006853 17.643 <2e-16 ***
## reb 0.158306 0.012432 12.734 <2e-16 ***
## ast 0.007419 0.016896 0.439 0.661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 16783 on 12304 degrees of freedom
## Residual deviance: 14619 on 12300 degrees of freedom
## AIC: 14629
##
## Number of Fisher Scoring iterations: 4# Calculating confidence intervals for coefficients (bonus)
conf_intervals <- confint(logit_model)## Waiting for profiling to be done...# Print coefficients and their confidence intervals
coefficients(logit_model)## (Intercept) age pts reb ast
## -1.306510327 0.005735646 0.120910988 0.158305518 0.007419109conf_intervals## 2.5 % 97.5 %
## (Intercept) -1.55834390 -1.05538673
## age -0.00322794 0.01469873
## pts 0.10754016 0.13440674
## reb 0.13406614 0.18280149
## ast -0.02554578 0.04070126The response variable in our logistic regression model is draft_round_binary, and the explanatory variables are age, pts (points), reb (rebounds), and ast (assists). The glm function is being used to construct the model. Logistic regression is specified by the family = binomial argument.
The summary function presents coefficient estimates, standard errors, z-values, and p-values following model fitting. The coefficients show how the respective explanatory variable changes by one unit, corresponding to a change in the log odds of being drafted in the first round.
The “confint” function, which yields confidence intervals for each estimate of a coefficient, is used to compute confidence intervals for coefficients.
You should interpret the coefficients in your notebook with reference to the particular dataset that you are working with. For instance, if the coefficient for points is 0.05, it indicates that the probability of being drafted in the first round increases by exp(0.05) for each extra point scored. The range that the confidence interval gives us indicates how likely it is that the true coefficient value falls within it.
Plotting
Examining the relationship between an explanatory variable and the response variable is crucial when thinking about a transformation for that variable. To see this relationship and assess whether a transformation is required, scatter plots are an excellent tool.
# Select an explanatory variable for transformation and the response variable
explanatory_var <- "pts" # Example variable (points scored)
response_var <- "draft_round_binary"
# Create a scatter plot to visualize the relationship
plot(player_data[[explanatory_var]], player_data[[response_var]],
xlab = explanatory_var, ylab = response_var,
main = paste("Scatter Plot of", explanatory_var, "vs.", response_var))
# Optionally, you can add a trendline to the scatter plot
# This helps in visually assessing the relationship
abline(lm(player_data[[response_var]] ~ player_data[[explanatory_var]]), col = "red")
This scatter plot shows the relationship between the response variable (“draft_round_binary”) and the explanatory variable (“pts,” which stands for points scored in this case). A linear regression line, or trendline, is added to the scatter plot using the lm function to aid in the visual assessment of the linear relationship.