# 1
library(readr)
setwd("C:/Users/user/Downloads/week12")
MData <- read.csv("jordan_playoffs.csv") # Downloading M Data
# Lebron Regular Season Data
LData <- read.csv("lebron_playoffs.csv") # Downloading L Data
Merged = merge(x = MData,
y = LData,
by = c("trb"))
head(Merged)
## trb game.x date.x series.x series_game.x team.x opp.x result.x mp.x fg.x
## 1 1 21 6/14/1998 FIN 6 CHI UTA W (+1) 44 15
## 2 2 8 5/23/1993 ECF 1 CHI NYK L (-8) 43 10
## 3 2 3 4/30/1997 EC1 3 CHI WSB W (+1) 43 14
## 4 2 13 6/4/1993 ECF 6 CHI NYK W (+8) 45 8
## 5 2 8 5/14/1996 ECS 5 CHI NYK W (+13) 43 13
## 6 2 15 5/29/1989 ECF 4 CHI DET L (-6) 43 5
## fga.x fgp.x three.x threeatt.x threep.x ft.x fta.x ftp.x orb.x drb.x ast.x
## 1 35 0.429 3 7 0.429 12 15 0.800 0 1 1
## 2 27 0.370 2 4 0.500 5 6 0.833 1 1 5
## 3 24 0.583 0 5 0.000 0 0 NA 1 1 6
## 4 24 0.333 1 4 0.250 8 9 0.889 0 2 9
## 5 29 0.448 1 2 0.500 8 9 0.889 1 1 5
## 6 15 0.333 1 3 0.333 12 17 0.706 0 2 4
## stl.x blk.x tov.x pts.x game_score.x plus_minus.x game.y date.y series.y
## 1 4 0 1 45 28.5 NA 6 5/8/2007 ECS
## 2 4 0 1 27 17.2 NA 14 5/28/2014 ECF
## 3 0 1 2 28 19.9 NA 14 5/28/2014 ECF
## 4 3 2 1 25 19.7 NA 14 5/28/2014 ECF
## 5 2 0 2 35 22.0 NA 14 5/28/2014 ECF
## 6 1 0 1 23 13.9 NA 14 5/28/2014 ECF
## series_game.y team.y opp.y result.y mp.y fg.y fga.y fgp.y three.y threeatt.y
## 1 2 CLE NJN W (+10) 46 12 24 0.5 3 7
## 2 5 MIA IND L (-3) 24 2 10 0.2 1 3
## 3 5 MIA IND L (-3) 24 2 10 0.2 1 3
## 4 5 MIA IND L (-3) 24 2 10 0.2 1 3
## 5 5 MIA IND L (-3) 24 2 10 0.2 1 3
## 6 5 MIA IND L (-3) 24 2 10 0.2 1 3
## threep.y ft.y fta.y ftp.y orb.y drb.y ast.y stl.y blk.y tov.y pts.y
## 1 0.429 9 13 0.692 0 1 12 3 0 2 36
## 2 0.333 2 3 0.667 1 1 4 0 1 3 7
## 3 0.333 2 3 0.667 1 1 4 0 1 3 7
## 4 0.333 2 3 0.667 1 1 4 0 1 3 7
## 5 0.333 2 3 0.667 1 1 4 0 1 3 7
## 6 0.333 2 3 0.667 1 1 4 0 1 3 7
## game_score.y plus_minus.y
## 1 31.3 8
## 2 -0.1 1
## 3 -0.1 1
## 4 -0.1 1
## 5 -0.1 1
## 6 -0.1 1
tail(Merged)
## trb game.x date.x series.x series_game.x team.x opp.x result.x mp.x
## 3601 15 8 5/13/1989 ECS 3 CHI NYK W (+23) 39
## 3602 15 8 5/13/1989 ECS 3 CHI NYK W (+23) 39
## 3603 16 5 5/8/1997 ECS 2 CHI ATL L (-8) 45
## 3604 16 5 5/8/1997 ECS 2 CHI ATL L (-8) 45
## 3605 16 5 5/8/1997 ECS 2 CHI ATL L (-8) 45
## 3606 19 8 5/14/1991 ECS 5 CHI PHI W (+5) 42
## fg.x fga.x fgp.x three.x threeatt.x threep.x ft.x fta.x ftp.x orb.x drb.x
## 3601 14 25 0.560 1 2 0.5 11 13 0.846 2 13
## 3602 14 25 0.560 1 2 0.5 11 13 0.846 2 13
## 3603 12 29 0.414 0 6 0.0 3 3 1.000 6 10
## 3604 12 29 0.414 0 6 0.0 3 3 1.000 6 10
## 3605 12 29 0.414 0 6 0.0 3 3 1.000 6 10
## 3606 14 31 0.452 0 2 0.0 10 11 0.909 4 15
## ast.x stl.x blk.x tov.x pts.x game_score.x plus_minus.x game.y date.y
## 3601 9 6 1 3 40 41.4 NA 3 4/21/2011
## 3602 9 6 1 3 40 41.4 NA 17 6/7/2012
## 3603 6 0 1 1 27 21.8 NA 19 6/13/2016
## 3604 6 0 1 1 27 21.8 NA 16 6/7/2015
## 3605 6 0 1 1 27 21.8 NA 15 9/26/2020
## 3606 7 0 1 4 38 29.2 NA 11 5/13/2010
## series.y series_game.y team.y opp.y result.y mp.y fg.y fga.y fgp.y three.y
## 3601 EC1 3 MIA PHI W (+6) 44 8 15 0.533 1
## 3602 ECF 6 MIA BOS W (+19) 45 19 26 0.731 2
## 3603 FIN 5 CLE GSW W (+15) 43 16 30 0.533 4
## 3604 FIN 2 CLE GSW W (+2) 50 11 35 0.314 3
## 3605 WCF 5 LAL DEN W (+10) 40 15 25 0.600 1
## 3606 ECS 6 CLE BOS L (-9) 46 8 21 0.381 2
## threeatt.y threep.y ft.y fta.y ftp.y orb.y drb.y ast.y stl.y blk.y tov.y
## 3601 4 0.25 7 10 0.700 1 14 6 1 0 1
## 3602 4 0.50 5 9 0.556 2 13 5 0 0 4
## 3603 8 0.50 5 8 0.625 4 12 7 3 3 2
## 3604 6 0.50 14 18 0.778 4 12 11 1 1 3
## 3605 4 0.25 7 8 0.875 3 13 10 1 0 2
## 3606 4 0.50 9 12 0.750 3 16 10 3 1 9
## pts.y game_score.y plus_minus.y
## 3601 24 24.2 8
## 3602 45 36.4 22
## 3603 41 39.2 13
## 3604 39 28.9 0
## 3605 38 37.3 12
## 3606 27 22.1 -5
# my independent variable is series_game.x and dependent variable is game_score.x
# $ Y = beta_0 + simX(i)\beta_1 + \epsilon for the formula, Y is equal to series.game.x and x is equal to game_score.x
#lm(formula = Units ~ game_score.x, data = Merged)
MJ.lm = lm(data = Merged, formula = series_game.x ~ game_score.x)
summary(MJ.lm)
##
## Call:
## lm(formula = series_game.x ~ game_score.x, data = Merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5459 -1.2046 -0.1146 1.1409 4.0487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.600379 0.090817 39.644 < 2e-16 ***
## game_score.x -0.020940 0.003415 -6.132 9.64e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.641 on 3604 degrees of freedom
## Multiple R-squared: 0.01032, Adjusted R-squared: 0.01005
## F-statistic: 37.6 on 1 and 3604 DF, p-value: 9.643e-10
# Interpret the slope and intercept parameters .
#The slope of the data set indicates that a one-unit increase in the value of series_game results in an increase of 3.600379 in the game_score. Meanwhile, the intercept reveals that when the value of series_game is 0, the value of game_score.x is equivalent to the intercept.
Slope <- cov(Merged$series_game.x, Merged$game_score.x)/var(Merged$game_score.x)
Slope
## [1] -0.02093955
Intercept1 <- mean(Merged$series_game.x) - Slope * mean(Merged$game_score.x)
Intercept1
## [1] 3.600379
lm(data = Merged, formula = series_game.x ~ game_score.x)
##
## Call:
## lm(formula = series_game.x ~ game_score.x, data = Merged)
##
## Coefficients:
## (Intercept) game_score.x
## 3.60038 -0.02094