Objetivo

Aplicar e interpretar modelo de regresión múltiple

Descripción

Aplicar regresión lineal en los datos de FIFA con la variable dependiene Value y varias variables independientes

Cargar librerias

library(readr)
library(dplyr)

## 
## 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

library(ggplot2)
library(caret)

## Warning: package 'caret' was built under R version 4.0.3

## Loading required package: lattice

library(knitr)

Cargar datos

getwd()

## [1] "C:/Users/pc/Documents/RStudio"

datos.fifa <- read.csv(file = "data.csv", encoding = "UTF-8")

datos <- select(datos.fifa, Value, Age, Overall, Potential, 
        Wage, Special, International.Reputation,Weak.Foot,
        Skill.Moves, Jersey.Number, Height,
        Weight, Crossing, Finishing, HeadingAccuracy,
        ShortPassing, Volleys, Dribbling, Curve,
        FKAccuracy, LongPassing, BallControl, Acceleration,
        SprintSpeed, Agility, Reactions, Balance, ShotPower,
        Jumping, Stamina, Strength,LongShots, Aggression,
        Interceptions, Positioning, Vision,
        Penalties, Composure, Marking, StandingTackle,
        SlidingTackle)


kable(head(datos, 10), caption = "Datos de FIFA con variables numéricas  (primeros diez)", row.names = 1:nrow(datos))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de FIFA con variables numéricas (primeros diez)
	Value	Age	Overall	Potential	Wage	Special	International.Reputation	Weak.Foot	Skill.Moves	Jersey.Number	Height	Weight	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle
1	€110.5M	31	94	94	€565K	2202	5	4	4	10	5’7	159lbs	84	95	70	90	86	97	93	94	87	96	91	86	91	95	95	85	68	72	59	94	48	22	94	94	75	96	33	28	26
2	€77M	33	94	94	€405K	2228	5	4	5	7	6’2	183lbs	84	94	89	81	87	88	81	76	77	94	89	91	87	96	70	95	95	88	79	93	63	29	95	82	85	95	28	31	23
3	€118.5M	26	92	93	€290K	2143	5	5	5	10	5’9	150lbs	79	87	62	84	84	96	88	87	78	95	94	90	96	94	84	80	61	81	49	82	56	36	89	87	81	94	27	24	33
4	€72M	27	91	93	€260K	1471	4	3	1	1	6’4	168lbs	17	13	21	50	13	18	21	19	51	42	57	58	60	90	43	31	67	43	64	12	38	30	12	68	40	68	15	21	13
5	€102M	27	91	92	€355K	2281	4	5	4	7	5’11	154lbs	93	82	55	92	82	86	85	83	91	91	78	76	79	91	77	91	63	90	75	91	76	61	87	94	79	88	68	58	51
6	€93M	27	91	91	€340K	2142	4	4	4	10	5’8	163lbs	81	84	61	89	80	95	83	79	83	94	94	88	95	90	94	82	56	83	66	80	54	41	87	89	86	91	34	27	22
7	€67M	32	91	91	€420K	2280	4	4	4	10	5’8	146lbs	86	72	55	93	76	90	85	78	88	93	80	72	93	90	94	79	68	89	58	82	62	83	79	92	82	84	60	76	73
8	€80M	31	91	91	€455K	2346	5	4	3	9	6’0	190lbs	77	93	77	82	88	87	86	84	64	90	86	75	82	92	83	86	69	90	83	85	87	41	92	84	85	85	62	45	38
9	€51M	32	91	91	€380K	2201	4	3	3	15	6’0	181lbs	66	60	91	78	66	63	74	72	77	84	76	75	78	85	66	79	93	84	83	59	88	90	60	63	75	82	87	92	91
10	€68M	25	90	93	€94K	1331	3	3	1	1	6’2	192lbs	13	11	15	29	13	12	13	14	26	16	43	60	67	86	49	22	76	41	78	12	34	19	11	70	11	70	27	12	18

kable(tail(datos, 10), caption = "Datos de FIFA con variables numéricas sólamente (últimos diez)")

Datos de FIFA con variables numéricas sólamente (últimos diez)
	Value	Age	Overall	Potential	Wage	Special	International.Reputation	Weak.Foot	Skill.Moves	Jersey.Number	Height	Weight	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle
18198	€60K	18	47	61	€1K	1362	1	3	2	14	5’10	141lbs	44	44	36	53	43	50	48	46	52	51	68	62	58	41	62	50	55	50	38	37	37	28	39	48	49	52	41	47	38
18199	€60K	18	47	70	€1K	792	1	2	1	22	5’11	154lbs	14	8	14	19	8	10	13	10	21	11	18	24	22	36	47	26	56	20	38	5	25	6	5	37	14	34	15	11	13
18200	€70K	18	47	69	€1K	1303	1	3	2	65	5’6	150lbs	31	31	41	51	26	46	35	31	55	47	60	63	53	46	55	49	57	42	43	30	53	49	35	40	36	40	48	49	49
18201	€60K	18	47	62	€1K	1203	1	2	2	21	5’9	157lbs	28	47	47	42	37	39	32	25	30	41	65	48	64	54	80	44	77	31	31	51	26	16	46	37	58	50	15	17	14
18202	€60K	18	47	68	€1K	1098	1	3	2	29	6’1	168lbs	22	23	45	25	27	21	21	27	27	32	52	52	39	43	48	39	74	39	52	16	44	45	20	31	38	43	44	47	53
18203	€60K	19	47	65	€1K	1307	1	2	2	22	5’9	134lbs	34	38	40	49	25	42	30	34	45	43	54	57	60	49	76	43	55	40	47	38	46	46	39	52	43	45	40	48	47
18204	€60K	19	47	63	€1K	1098	1	2	2	21	6’3	170lbs	23	52	52	43	36	39	32	20	25	40	41	39	38	40	52	41	47	43	67	42	47	16	46	33	43	42	22	15	19
18205	€60K	16	47	67	€1K	1189	1	3	2	33	5’8	148lbs	25	40	46	38	38	45	38	27	28	44	70	69	50	47	58	45	60	55	32	45	32	15	48	43	55	41	32	13	11
18206	€60K	17	47	66	€1K	1228	1	3	2	34	5’10	154lbs	44	50	39	42	40	51	34	32	32	52	61	60	52	21	71	64	42	40	48	34	33	22	44	47	50	46	20	25	27
18207	€60K	16	46	66	€1K	1321	1	3	2	33	5’10	176lbs	41	34	46	48	30	43	40	34	44	51	57	55	55	51	63	43	62	47	60	32	56	42	34	49	33	43	40	43	50

Definir variables

paste ("La variable dependiente y es :  Value")

## [1] "La variable dependiente y es :  Value"

paste ("Las variables independientes X1, X2, X3 ... Xn, son todas las demás: ")

## [1] "Las variables independientes X1, X2, X3 ... Xn, son todas las demás: "

Limpiar datos

source(file = "misfunciones.r")

datos <- datos %>%
  mutate(valor = ifelse (substr(Value, nchar(Value), nchar(Value)) == 'M', fcleanValue(Value) * 1000000, fcleanValue(Value) * 1000)) %>%
  filter(valor > 0)

datos <- datos %>%
  mutate(salario = ifelse (substr(Wage, nchar(Wage), nchar(Wage)) == 'M', fcleanValue(Wage) * 1000000, fcleanValue(Wage) * 1000)) %>%
  filter(salario > 0)

datos <- mutate(datos, estatura = festatura(Height))

datos <- mutate(datos, pesokgs = flbskgs(Weight))

datos <- fnames.minusculas(datos)

datos <- select(datos, -value, -wage, -height, -weight)

Verificacion

kable(head(datos, 10), caption = "Datos de FIFA con variables numéricas  (primeros diez)", row.names = 1:nrow(datos))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de FIFA con variables numéricas (primeros diez)
	age	overall	potential	special	international.reputation	weak.foot	skill.moves	jersey.number	crossing	finishing	headingaccuracy	shortpassing	volleys	dribbling	curve	fkaccuracy	longpassing	ballcontrol	acceleration	sprintspeed	agility	reactions	balance	shotpower	jumping	stamina	strength	longshots	aggression	interceptions	positioning	vision	penalties	composure	marking	standingtackle	slidingtackle	valor	salario	estatura	pesokgs
1	31	94	94	2202	5	4	4	10	84	95	70	90	86	97	93	94	87	96	91	86	91	95	95	85	68	72	59	94	48	22	94	94	75	96	33	28	26	110500000	565000	1.70	72.12
2	33	94	94	2228	5	4	5	7	84	94	89	81	87	88	81	76	77	94	89	91	87	96	70	95	95	88	79	93	63	29	95	82	85	95	28	31	23	77000000	405000	1.88	83.01
3	26	92	93	2143	5	5	5	10	79	87	62	84	84	96	88	87	78	95	94	90	96	94	84	80	61	81	49	82	56	36	89	87	81	94	27	24	33	118500000	290000	1.75	68.04
4	27	91	93	1471	4	3	1	1	17	13	21	50	13	18	21	19	51	42	57	58	60	90	43	31	67	43	64	12	38	30	12	68	40	68	15	21	13	72000000	260000	1.93	76.20
5	27	91	92	2281	4	5	4	7	93	82	55	92	82	86	85	83	91	91	78	76	79	91	77	91	63	90	75	91	76	61	87	94	79	88	68	58	51	102000000	355000	1.80	69.85
6	27	91	91	2142	4	4	4	10	81	84	61	89	80	95	83	79	83	94	94	88	95	90	94	82	56	83	66	80	54	41	87	89	86	91	34	27	22	93000000	340000	1.73	73.94
7	32	91	91	2280	4	4	4	10	86	72	55	93	76	90	85	78	88	93	80	72	93	90	94	79	68	89	58	82	62	83	79	92	82	84	60	76	73	67000000	420000	1.73	66.22
8	31	91	91	2346	5	4	3	9	77	93	77	82	88	87	86	84	64	90	86	75	82	92	83	86	69	90	83	85	87	41	92	84	85	85	62	45	38	80000000	455000	1.83	86.18
9	32	91	91	2201	4	3	3	15	66	60	91	78	66	63	74	72	77	84	76	75	78	85	66	79	93	84	83	59	88	90	60	63	75	82	87	92	91	51000000	380000	1.83	82.10
10	25	90	93	1331	3	3	1	1	13	11	15	29	13	12	13	14	26	16	43	60	67	86	49	22	76	41	78	12	34	19	11	70	11	70	27	12	18	68000000	94000	1.88	87.09

kable(tail(datos, 10), caption = "Datos de FIFA con variables numéricas sólamente (últimos diez)", row.names = 1:nrow(datos))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de FIFA con variables numéricas sólamente (últimos diez)
	age	overall	potential	special	international.reputation	weak.foot	skill.moves	jersey.number	crossing	finishing	headingaccuracy	shortpassing	volleys	dribbling	curve	fkaccuracy	longpassing	ballcontrol	acceleration	sprintspeed	agility	reactions	balance	shotpower	jumping	stamina	strength	longshots	aggression	interceptions	positioning	vision	penalties	composure	marking	standingtackle	slidingtackle	valor	salario	estatura	pesokgs
17946	18	47	61	1362	1	3	2	14	44	44	36	53	43	50	48	46	52	51	68	62	58	41	62	50	55	50	38	37	37	28	39	48	49	52	41	47	38	60000	1000	1.78	63.96
17947	18	47	70	792	1	2	1	22	14	8	14	19	8	10	13	10	21	11	18	24	22	36	47	26	56	20	38	5	25	6	5	37	14	34	15	11	13	60000	1000	1.80	69.85
17948	18	47	69	1303	1	3	2	65	31	31	41	51	26	46	35	31	55	47	60	63	53	46	55	49	57	42	43	30	53	49	35	40	36	40	48	49	49	70000	1000	1.68	68.04
17949	18	47	62	1203	1	2	2	21	28	47	47	42	37	39	32	25	30	41	65	48	64	54	80	44	77	31	31	51	26	16	46	37	58	50	15	17	14	60000	1000	1.75	71.21
17950	18	47	68	1098	1	3	2	29	22	23	45	25	27	21	21	27	27	32	52	52	39	43	48	39	74	39	52	16	44	45	20	31	38	43	44	47	53	60000	1000	1.85	76.20
17951	19	47	65	1307	1	2	2	22	34	38	40	49	25	42	30	34	45	43	54	57	60	49	76	43	55	40	47	38	46	46	39	52	43	45	40	48	47	60000	1000	1.75	60.78
17952	19	47	63	1098	1	2	2	21	23	52	52	43	36	39	32	20	25	40	41	39	38	40	52	41	47	43	67	42	47	16	46	33	43	42	22	15	19	60000	1000	1.91	77.11
17953	16	47	67	1189	1	3	2	33	25	40	46	38	38	45	38	27	28	44	70	69	50	47	58	45	60	55	32	45	32	15	48	43	55	41	32	13	11	60000	1000	1.73	67.13
17954	17	47	66	1228	1	3	2	34	44	50	39	42	40	51	34	32	32	52	61	60	52	21	71	64	42	40	48	34	33	22	44	47	50	46	20	25	27	60000	1000	1.78	69.85
17955	16	46	66	1321	1	3	2	33	41	34	46	48	30	43	40	34	44	51	57	55	55	51	63	43	62	47	60	32	56	42	34	49	33	43	40	43	50	60000	1000	1.78	79.83

Crear datos entrenamiento y validación

70 % datos de entrenamiento
30 % datos de validación

set.seed(2020)
entrena <- createDataPartition(y = datos$overall, p = 0.7, list = FALSE, times = 1)


datos.entrenamiento <- datos[entrena, ] 


datos.validacion <- datos[-entrena, ]

kable(head(datos.entrenamiento, 10), caption = "Datos de entrenamiento  (primeros diez)", row.names = 1:nrow(datos.entrenamiento))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de entrenamiento (primeros diez)
	age	overall	potential	special	international.reputation	weak.foot	skill.moves	jersey.number	crossing	finishing	headingaccuracy	shortpassing	volleys	dribbling	curve	fkaccuracy	longpassing	ballcontrol	acceleration	sprintspeed	agility	reactions	balance	shotpower	jumping	stamina	strength	longshots	aggression	interceptions	positioning	vision	penalties	composure	marking	standingtackle	slidingtackle	valor	salario	estatura	pesokgs
1	31	94	94	2202	5	4	4	10	84	95	70	90	86	97	93	94	87	96	91	86	91	95	95	85	68	72	59	94	48	22	94	94	75	96	33	28	26	110500000	565000	1.70	72.12
2	33	94	94	2228	5	4	5	7	84	94	89	81	87	88	81	76	77	94	89	91	87	96	70	95	95	88	79	93	63	29	95	82	85	95	28	31	23	77000000	405000	1.88	83.01
3	26	92	93	2143	5	5	5	10	79	87	62	84	84	96	88	87	78	95	94	90	96	94	84	80	61	81	49	82	56	36	89	87	81	94	27	24	33	118500000	290000	1.75	68.04
5	27	91	92	2281	4	5	4	7	93	82	55	92	82	86	85	83	91	91	78	76	79	91	77	91	63	90	75	91	76	61	87	94	79	88	68	58	51	102000000	355000	1.80	69.85
6	27	91	91	2142	4	4	4	10	81	84	61	89	80	95	83	79	83	94	94	88	95	90	94	82	56	83	66	80	54	41	87	89	86	91	34	27	22	93000000	340000	1.73	73.94
7	32	91	91	2280	4	4	4	10	86	72	55	93	76	90	85	78	88	93	80	72	93	90	94	79	68	89	58	82	62	83	79	92	82	84	60	76	73	67000000	420000	1.73	66.22
8	31	91	91	2346	5	4	3	9	77	93	77	82	88	87	86	84	64	90	86	75	82	92	83	86	69	90	83	85	87	41	92	84	85	85	62	45	38	80000000	455000	1.83	86.18
11	29	90	90	2152	4	4	4	9	62	91	85	83	89	85	77	86	65	89	77	78	78	90	78	88	84	78	84	84	80	39	91	77	88	86	34	42	19	77000000	205000	1.83	79.83
12	28	90	90	2190	4	5	3	8	88	76	54	92	82	81	86	84	93	90	64	62	70	89	71	87	30	75	73	92	60	82	79	86	73	85	72	79	69	76500000	355000	1.83	76.20
14	32	90	90	2115	4	2	4	21	84	76	54	93	82	89	82	77	87	94	70	64	92	90	90	72	64	78	52	75	57	50	89	92	75	93	59	53	29	60000000	285000	1.73	67.13

kable(tail(datos.validacion, 10), caption = "Datos de validación  (ultimos diez)", row.names = 1:nrow(datos.entrenamiento))

## Warning in if (is.na(row.names)) row.names = has_rownames(x): la condición tiene
## longitud > 1 y sólo el primer elemento será usado

## Warning in if (row.names) {: la condición tiene longitud > 1 y sólo el primer
## elemento será usado

Datos de validación (ultimos diez)
	age	overall	potential	special	international.reputation	weak.foot	skill.moves	jersey.number	crossing	finishing	headingaccuracy	shortpassing	volleys	dribbling	curve	fkaccuracy	longpassing	ballcontrol	acceleration	sprintspeed	agility	reactions	balance	shotpower	jumping	stamina	strength	longshots	aggression	interceptions	positioning	vision	penalties	composure	marking	standingtackle	slidingtackle	valor	salario	estatura	pesokgs
17918	19	48	59	1152	1	3	2	28	28	23	45	27	24	25	21	26	29	28	63	50	47	42	76	35	65	49	49	21	45	46	25	29	37	43	42	54	54	40000	1000	1.73	72.12
17924	18	48	63	1370	1	3	3	24	38	34	49	54	34	49	33	37	50	41	67	61	61	46	59	50	56	64	55	35	52	39	46	45	36	44	44	40	41	60000	1000	1.85	73.03
17931	17	48	66	1296	1	2	2	32	45	46	46	38	27	46	28	24	34	38	61	57	56	47	66	39	59	60	48	33	53	46	43	37	33	38	43	49	45	50000	1000	1.80	60.78
17933	18	48	55	1368	1	3	2	33	33	24	42	54	33	44	34	36	50	47	61	57	57	44	58	47	64	59	66	31	53	49	35	46	37	42	47	49	53	40000	1000	1.85	81.19
17939	18	47	67	1285	1	3	2	32	32	32	45	48	31	41	32	43	47	37	53	55	31	47	61	41	54	61	55	34	44	44	51	54	34	46	35	44	47	60000	1000	1.75	79.83
17942	18	47	64	1191	1	2	2	4	36	25	40	27	27	46	31	25	23	29	64	58	55	54	81	22	56	54	40	22	48	49	35	30	32	32	41	48	48	50000	1000	1.73	66.22
17945	19	47	61	1333	1	3	2	26	31	28	40	53	31	46	39	37	48	48	58	58	60	48	79	42	63	35	51	30	55	44	28	51	44	35	41	44	54	60000	1000	1.70	66.22
17948	18	47	69	1303	1	3	2	65	31	31	41	51	26	46	35	31	55	47	60	63	53	46	55	49	57	42	43	30	53	49	35	40	36	40	48	49	49	70000	1000	1.68	68.04
17951	19	47	65	1307	1	2	2	22	34	38	40	49	25	42	30	34	45	43	54	57	60	49	76	43	55	40	47	38	46	46	39	52	43	45	40	48	47	60000	1000	1.75	60.78
17955	16	46	66	1321	1	3	2	33	41	34	46	48	30	43	40	34	44	51	57	55	55	51	63	43	62	47	60	32	56	42	34	49	33	43	40	43	50	60000	1000	1.78	79.83

Modelo de Regresion Multiple

modelo <- lm(formula = valor ~ ., datos.entrenamiento)
summary(modelo)

## 
## Call:
## lm(formula = valor ~ ., data = datos.entrenamiento)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -19161476   -832836    -60785    700651  56366387 
## 
## Coefficients:
##                              Estimate   Std. Error t value             Pr(>|t|)
## (Intercept)              -6237527.444  1293049.221  -4.824  0.00000142445759378
## age                       -260441.896    10830.032 -24.048 < 0.0000000000000002
## overall                    263151.521    12324.511  21.352 < 0.0000000000000002
## potential                  -45061.430     9216.102  -4.889  0.00000102388538355
## special                     -4971.156     1050.494  -4.732  0.00000224521357859
## international.reputation  2096975.406    80512.189  26.045 < 0.0000000000000002
## weak.foot                   64861.448    37182.465   1.744             0.081112
## skill.moves                227777.379    60540.118   3.762             0.000169
## jersey.number               -5180.184     1478.922  -3.503             0.000462
## crossing                    -4564.702     3279.621  -1.392             0.163996
## finishing                   19777.935     3898.800   5.073  0.00000039756985080
## headingaccuracy             -3149.386     2946.924  -1.069             0.285224
## shortpassing                 -403.627     5483.003  -0.074             0.941319
## volleys                     10266.460     3483.094   2.948             0.003209
## dribbling                   -4145.766     4894.856  -0.847             0.397031
## curve                        -914.425     3441.961  -0.266             0.790498
## fkaccuracy                  19978.179     3058.942   6.531  0.00000000006782730
## longpassing                 17968.651     4230.417   4.247  0.00002177436178140
## ballcontrol                -23414.482     5821.108  -4.022  0.00005796128114699
## acceleration                 7772.372     4605.084   1.688             0.091478
## sprintspeed                 -1521.284     4289.824  -0.355             0.722876
## agility                     -1031.663     3552.756  -0.290             0.771528
## reactions                   34476.184     5330.003   6.468  0.00000000010278779
## balance                      5856.472     3586.184   1.633             0.102480
## shotpower                   -9906.667     3540.906  -2.798             0.005153
## jumping                       549.760     2690.688   0.204             0.838107
## stamina                     22668.921     2876.934   7.880  0.00000000000000356
## strength                     3944.760     3414.156   1.155             0.247943
## longshots                     771.132     3840.908   0.201             0.840883
## aggression                   3220.423     2690.702   1.197             0.231379
## interceptions               -4011.602     3769.712  -1.064             0.287274
## positioning                  9072.650     3724.818   2.436             0.014876
## vision                      10036.751     3659.156   2.743             0.006098
## penalties                   -7407.033     3273.003  -2.263             0.023648
## composure                     896.362     3767.794   0.238             0.811962
## marking                      9743.854     2960.754   3.291             0.001001
## standingtackle              21022.779     5464.713   3.847             0.000120
## slidingtackle              -19840.500     5183.721  -3.827             0.000130
## salario                       160.301        1.456 110.081 < 0.0000000000000002
## estatura                 -1159502.240   664549.743  -1.745             0.081045
## pesokgs                      1722.922     5678.866   0.303             0.761596
##                             
## (Intercept)              ***
## age                      ***
## overall                  ***
## potential                ***
## special                  ***
## international.reputation ***
## weak.foot                .  
## skill.moves              ***
## jersey.number            ***
## crossing                    
## finishing                ***
## headingaccuracy             
## shortpassing                
## volleys                  ** 
## dribbling                   
## curve                       
## fkaccuracy               ***
## longpassing              ***
## ballcontrol              ***
## acceleration             .  
## sprintspeed                 
## agility                     
## reactions                ***
## balance                     
## shotpower                ** 
## jumping                     
## stamina                  ***
## strength                    
## longshots                   
## aggression                  
## interceptions               
## positioning              *  
## vision                   ** 
## penalties                *  
## composure                   
## marking                  ** 
## standingtackle           ***
## slidingtackle            ***
## salario                  ***
## estatura                 .  
## pesokgs                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2527000 on 12504 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.8088, Adjusted R-squared:  0.8082 
## F-statistic:  1323 on 40 and 12504 DF,  p-value: < 0.00000000000000022

Se eligen las variables que el primer modelo no identifica como variables estadisticamente significativas (que no tienen ***) para el valor economico del jugador.

modelo2 <- lm(formula = valor ~ age + overall + potential + special + international.reputation + jersey.number + finishing +  fkaccuracy + longpassing + ballcontrol + reactions +  stamina + salario , datos.entrenamiento)

summary(modelo2)

## 
## Call:
## lm(formula = valor ~ age + overall + potential + special + international.reputation + 
##     jersey.number + finishing + fkaccuracy + longpassing + ballcontrol + 
##     reactions + stamina + salario, data = datos.entrenamiento)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -18997420   -824040    -75785    697276  56424654 
## 
## Coefficients:
##                             Estimate  Std. Error t value             Pr(>|t|)
## (Intercept)              -7857348.39   452119.77 -17.379 < 0.0000000000000002
## age                       -266566.73    10213.53 -26.099 < 0.0000000000000002
## overall                    254603.39    10507.57  24.230 < 0.0000000000000002
## potential                  -44787.95     9087.82  -4.928   0.0000008398819053
## special                     -3549.32      372.39  -9.531 < 0.0000000000000002
## international.reputation  2102462.29    80084.70  26.253 < 0.0000000000000002
## jersey.number               -5350.31     1478.81  -3.618             0.000298
## finishing                   22222.02     2169.52  10.243 < 0.0000000000000002
## fkaccuracy                  18559.56     2463.89   7.533   0.0000000000000531
## longpassing                 16414.29     3085.71   5.319   0.0000001058745305
## ballcontrol                -19753.30     4063.23  -4.861   0.0000011792807544
## reactions                   36293.34     5005.88   7.250   0.0000000000004408
## stamina                     22403.91     2712.00   8.261 < 0.0000000000000002
## salario                       160.60        1.46 110.037 < 0.0000000000000002
##                             
## (Intercept)              ***
## age                      ***
## overall                  ***
## potential                ***
## special                  ***
## international.reputation ***
## jersey.number            ***
## finishing                ***
## fkaccuracy               ***
## longpassing              ***
## ballcontrol              ***
## reactions                ***
## stamina                  ***
## salario                  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2538000 on 12531 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.8068, Adjusted R-squared:  0.8066 
## F-statistic:  4026 on 13 and 12531 DF,  p-value: < 0.00000000000000022

El valor de Multiple R-square de aproximadamente 80% y Adjusted R-square de aproximadamente 80% representan que tanto representan todas las variables al valor económico del jugador.

Los tres *** de las variables significan que estas variables son estadísticamente significativas para el valor económico del jugador.

Prediccion del conjunto de datos de validacion

prediccion <- predict(object = modelo2, newdata =  datos.validacion)

new.datos.validacion <- mutate(datos.validacion, predicho = prediccion)

kable(head(new.datos.validacion, 10), caption = "Primeros diez registros con predicciones")

Primeros diez registros con predicciones
age	overall	potential	special	international.reputation	weak.foot	skill.moves	jersey.number	crossing	finishing	headingaccuracy	shortpassing	volleys	dribbling	curve	fkaccuracy	longpassing	ballcontrol	acceleration	sprintspeed	agility	reactions	balance	shotpower	jumping	stamina	strength	longshots	aggression	interceptions	positioning	vision	penalties	composure	marking	standingtackle	slidingtackle	valor	salario	estatura	pesokgs	predicho
27	91	93	1471	4	3	1	1	17	13	21	50	13	18	21	19	51	42	57	58	60	90	43	31	67	43	64	12	38	30	12	68	40	68	15	21	13	72000000	260000	1.93	76.20	53767689
32	91	91	2201	4	3	3	15	66	60	91	78	66	63	74	72	77	84	76	75	78	85	66	79	93	84	83	59	88	90	60	63	75	82	87	92	91	51000000	380000	1.83	82.10	71493065
25	90	93	1331	3	3	1	1	13	11	15	29	13	12	13	14	26	16	43	60	67	86	49	22	76	41	78	12	34	19	11	70	11	70	27	12	18	68000000	94000	1.88	87.09	25556761
32	90	90	1946	3	3	2	10	55	42	92	79	47	53	49	51	70	76	68	68	58	85	54	67	91	66	88	43	89	88	48	52	50	82	90	89	89	44000000	125000	1.88	78.02	28009254
24	89	94	2092	3	3	4	21	82	84	68	87	88	92	88	88	75	92	87	83	91	86	85	82	75	80	65	88	48	32	84	87	86	84	23	20	20	89000000	205000	1.78	74.84	43715121
31	89	89	2161	4	4	3	21	70	89	89	78	90	80	77	76	52	82	75	76	77	91	59	87	88	92	78	79	84	48	93	77	85	82	52	45	39	60000000	200000	1.85	77.11	43286358
32	89	89	1473	5	4	1	1	15	13	25	55	11	30	14	11	59	48	54	60	51	84	35	25	77	43	80	16	29	30	12	70	47	70	17	10	11	38000000	130000	1.93	92.08	32968482
33	89	89	1841	4	3	2	3	58	33	83	59	45	58	60	31	59	57	63	75	54	82	55	78	89	65	89	49	92	88	28	50	50	84	93	93	90	27000000	215000	1.88	84.82	43991912
27	88	88	2017	3	3	4	10	86	77	56	85	74	90	87	77	78	93	94	86	94	83	93	75	53	75	44	84	34	26	83	87	61	83	51	24	22	62000000	165000	1.63	58.97	36279405
26	88	91	2137	3	3	4	22	75	79	55	89	65	94	88	76	83	95	75	69	87	77	90	69	64	70	59	87	58	64	78	89	76	86	60	64	51	73500000	315000	1.75	78.93	59750432

kable(tail(new.datos.validacion, 10), caption = "Ultimos diez registros con predicciones")

Ultimos diez registros con predicciones
	age	overall	potential	special	international.reputation	weak.foot	skill.moves	jersey.number	crossing	finishing	headingaccuracy	shortpassing	volleys	dribbling	curve	fkaccuracy	longpassing	ballcontrol	acceleration	sprintspeed	agility	reactions	balance	shotpower	jumping	stamina	strength	longshots	aggression	interceptions	positioning	vision	penalties	composure	marking	standingtackle	slidingtackle	valor	salario	estatura	pesokgs	predicho
5376	19	48	59	1152	1	3	2	28	28	23	45	27	24	25	21	26	29	28	63	50	47	42	76	35	65	49	49	21	45	46	25	29	37	43	42	54	54	40000	1000	1.73	72.12	-1780511
5377	18	48	63	1370	1	3	3	24	38	34	49	54	34	49	33	37	50	41	67	61	61	46	59	50	56	64	55	35	52	39	46	45	36	44	44	40	41	60000	1000	1.85	73.03	-1427709
5378	17	48	66	1296	1	2	2	32	45	46	46	38	27	46	28	24	34	38	61	57	56	47	66	39	59	60	48	33	53	46	43	37	33	38	43	49	45	50000	1000	1.80	60.78	-1306960
5379	18	48	55	1368	1	3	2	33	33	24	42	54	33	44	34	36	50	47	61	57	57	44	58	47	64	59	66	31	53	49	35	46	37	42	47	49	53	40000	1000	1.85	81.19	-1654365
5380	18	47	67	1285	1	3	2	32	32	32	45	48	31	41	32	43	47	37	53	55	31	47	61	41	54	61	55	34	44	44	51	54	34	46	35	44	47	60000	1000	1.75	79.83	-1536809
5381	18	47	64	1191	1	2	2	4	36	25	40	27	27	46	31	25	23	29	64	58	55	54	81	22	56	54	40	22	48	49	35	30	32	32	41	48	48	50000	1000	1.73	66.22	-1547318
5382	19	47	61	1333	1	3	2	26	31	28	40	53	31	46	39	37	48	48	58	58	60	48	79	42	63	35	51	30	55	44	28	51	44	35	41	44	54	60000	1000	1.70	66.22	-2620240
5383	18	47	69	1303	1	3	2	65	31	31	41	51	26	46	35	31	55	47	60	63	53	46	55	49	57	42	43	30	53	49	35	40	36	40	48	49	49	70000	1000	1.68	68.04	-2639956
5384	19	47	65	1307	1	2	2	22	34	38	40	49	25	42	30	34	45	43	54	57	60	49	76	43	55	40	47	38	46	46	39	52	43	45	40	48	47	60000	1000	1.75	60.78	-2321330
5385	16	46	66	1321	1	3	2	33	41	34	46	48	30	43	40	34	44	51	57	55	55	51	63	43	62	47	60	32	56	42	34	49	33	43	40	43	50	60000	1000	1.78	79.83	-1963480

Ejemplo elaborado de un jugador con distintos valores para saber el valor en el mercado.

nombre <- "Jelitza Bermudez"
jugador <- data.frame(age = 25, overall = 60, potential = 60, special = 1000, international.reputation = 5, jersey.number = 13, finishing = 70, fkaccuracy = 70, longpassing = 60, ballcontrol  = 90, reactions  = 60, stamina  = 80, salario = 150000)

nueva.prediccion <- predict(object = modelo2, newdata =  jugador)

paste ("El jugador ", nombre, " debe tener un valor en el mercado de $", round(nueva.prediccion,2))

## [1] "El jugador  Jelitza Bermudez  debe tener un valor en el mercado de $ 35082811.15"

Interpretacion del caso

Por medio del modelo de regresion multiple se facilito la identificacion de las variables con mayor importancia y con mejor correlacion para el valor de un jugador.

El modelo pudo identificar las mas importantes esto ayudo a poder centrarnos en las que tienen mayor valor y poder priorizar las variables.

Las 13 variables que se puede encontrar con mayor relacion saber el valor de cualquier jugador por medio de estas variables.

La facilidad de este modelo nos hizo poder utilizar estadisticamente las variables importantes con la prediccion basandose en el modelo 2 la identificacion del valor de un jugador.

CASO8

Karla Jelitza Bermudez

02/12/2020