ESTADÍSTICA INFERENCIAL
Grupo No. 5
2025-07-14
0.1 Todas las Variables Continuas
En esta sección se analizan las variables continuas del estudio a través del ajuste a diferentes modelos de distribuciones probabilísticas. El objetivo es identificar la distribución teórica que mejor representa el comportamiento de cada variable, permitiendo realizar inferencias estadísticas con mayor precisión. Se incluyen análisis exploratorios, representaciones gráficas y los test. Este enfoque proporciona una base sólida para la formulación de hipótesis, la estimación de parámetros y la predicción de comportamientos futuros.
Cargamos los datos:
setwd("C:/Users/Usuario/Documents/Trabajo Estadistica/PROYECTO/")
datos<-read.csv("database.csv",header = TRUE, sep = ",", dec = ".")
str(datos)## 'data.frame': 38113 obs. of 81 variables:
## $ Vehicle.ID : int 26587 27705 26561 27681 27550 28426 27549 28425 27593 28455 ...
## $ Year : int 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 ...
## $ Make : chr "Alfa Romeo" "Alfa Romeo" "Alfa Romeo" "Alfa Romeo" ...
## $ Model : chr "GT V6 2.5" "GT V6 2.5" "Spider Veloce 2000" "Spider Veloce 2000" ...
## $ Class : chr "Minicompact Cars" "Minicompact Cars" "Two Seaters" "Two Seaters" ...
## $ Drive : chr "" "" "" "" ...
## $ Transmission : chr "Manual 5-Speed" "Manual 5-Speed" "Manual 5-Speed" "Manual 5-Speed" ...
## $ Transmission.Descriptor : chr "" "" "" "" ...
## $ Engine.Index : int 9001 9005 9002 9006 1830 1880 1831 1881 1524 1574 ...
## $ Engine.Descriptor : chr "(FFS)" "(FFS) CA model" "(FFS)" "(FFS) CA model" ...
## $ Engine.Cylinders : int 6 6 4 4 4 4 6 6 6 6 ...
## $ Engine.Displacement : num 2.5 2.5 2 2 2.5 2.5 4.2 4.2 4.2 4.2 ...
## $ Turbocharger : logi NA NA NA NA NA NA ...
## $ Supercharger : chr "" "" "" "" ...
## $ Fuel.Type : chr "Regular" "Regular" "Regular" "Regular" ...
## $ Fuel.Type.1 : chr "Regular Gasoline" "Regular Gasoline" "Regular Gasoline" "Regular Gasoline" ...
## $ Fuel.Type.2 : chr "" "" "" "" ...
## $ City.MPG..FT1. : int 17 17 18 18 18 18 13 13 15 15 ...
## $ Unrounded.City.MPG..FT1. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City.MPG..FT2. : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Unrounded.City.MPG..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City.Gasoline.Consumption..CD. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City.Electricity.Consumption : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City.Utility.Factor : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.MPG..FT1. : int 24 24 25 25 17 17 13 13 20 19 ...
## $ Unrounded.Highway.MPG..FT1. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.MPG..FT2. : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Unrounded.Highway.MPG..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.Gasoline.Consumption..CD. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.Electricity.Consumption : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.Utility.Factor : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Unadjusted.City.MPG..FT1. : num 21 21 23 23 22 22 16 16 19 19 ...
## $ Unadjusted.Highway.MPG..FT1. : num 34 34 35 35 24 24 18 18 27 26 ...
## $ Unadjusted.City.MPG..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Unadjusted.Highway.MPG..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Combined.MPG..FT1. : int 20 20 21 21 17 17 13 13 17 17 ...
## $ Unrounded.Combined.MPG..FT1. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Combined.MPG..FT2. : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Unrounded.Combined.MPG..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Combined.Electricity.Consumption : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Combined.Gasoline.Consumption..CD. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Combined.Utility.Factor : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Annual.Fuel.Cost..FT1. : int 1750 1750 1650 1650 2050 2050 2700 2700 2050 2050 ...
## $ Annual.Fuel.Cost..FT2. : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Gas.Guzzler.Tax : chr "" "" "" "" ...
## $ Save.or.Spend..5.Year. : int -2000 -2000 -1500 -1500 -3500 -3500 -6750 -6750 -3500 -3500 ...
## $ Annual.Consumption.in.Barrels..FT1.: num 16.5 16.5 15.7 15.7 19.4 ...
## $ Annual.Consumption.in.Barrels..FT2.: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Tailpipe.CO2..FT1. : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ Tailpipe.CO2.in.Grams.Mile..FT1. : num 444 444 423 423 523 ...
## $ Tailpipe.CO2..FT2. : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ Tailpipe.CO2.in.Grams.Mile..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Fuel.Economy.Score : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ GHG.Score : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ GHG.Score..Alt.Fuel. : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ My.MPG.Data : chr "N" "N" "N" "N" ...
## $ X2D.Passenger.Volume : int 74 74 0 0 0 0 0 0 0 0 ...
## $ X2D.Luggage.Volume : int 7 7 0 0 0 0 0 0 0 0 ...
## $ X4D.Passenger.Volume : int 0 0 0 0 0 0 0 0 0 0 ...
## $ X4D.Luggage.Volume : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Hatchback.Passenger.Volume : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Hatchback.Luggage.Volume : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Start.Stop.Technology : chr "" "" "" "" ...
## $ Alternative.Fuel.Technology : chr "" "" "" "" ...
## $ Electric.Motor : chr "" "" "" "" ...
## $ Manufacturer.Code : chr "" "" "" "" ...
## $ Gasoline.Electricity.Blended..CD. : chr "False" "False" "False" "False" ...
## $ Vehicle.Charger : chr "" "" "" "" ...
## $ Alternate.Charger : chr "" "" "" "" ...
## $ Hours.to.Charge..120V. : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Hours.to.Charge..240V. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hours.to.Charge..AC.240V. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Composite.City.MPG : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Composite.Highway.MPG : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Composite.Combined.MPG : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Range..FT1. : int 0 0 0 0 0 0 0 0 0 0 ...
## $ City.Range..FT1. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.Range..FT1. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Range..FT2. : chr "" "" "" "" ...
## $ City.Range..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Highway.Range..FT2. : num 0 0 0 0 0 0 0 0 0 0 ...Empezamos el desarrollo para cada variable continua:
1 Engine Displacement
Desmotor <- datos$Engine.Index
Desmotor <- as.numeric(datos$Engine.Index)
Desmotor <- Desmotor[!is.na(Desmotor) & Desmotor != -1]
Desmotor <- Desmotor[Desmotor > 0]
hist(Desmotor)
## [1] 13143.05## [1] 20375.25HistogramaDesmotor <- hist(Desmotor, freq=FALSE,
main="Gráfica No.1: Histograma
de Desplazamiento Motor con Curva Normal",
xlab="Desplazamiento Motor",
ylab="Densidad",
col="lightgreen")
x <- seq(min(Desmotor), max(Desmotor), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2, add = TRUE)
# Área bajo la curva del histograma
Altura <- HistogramaDesmotor$density
Area <- sum(Altura * diff(HistogramaDesmotor$breaks))
Area## [1] 1## [1] 25517## [1] 18747 73 288 249 311 892 108 655 354 1022 302 1647
## [13] 454 415Test:
## [1] 100## [1] 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
## [13] 60000 65000## [1] 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000
## [13] 65000 70000# Frecuencias esperadas bajo normal
P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(Desmotor)
FE## [1] 2175.53458 2398.53164 2490.59453 2435.78390 2243.63709 1946.45573
## [7] 1590.42943 1223.94547 887.13012 605.60564 389.37633 235.79026
## [13] 134.48025 72.23821## [1] 147734.1## [1] 19.67514## [1] FALSEModelo Exponencial:
## [1] 7.608586e-05# Histograma con curva exponencial
HistExp <- hist(Desmotor, freq=FALSE,
main="Gráfica No.2: Histograma
de Desplazamiento Motor con Curva Exponencial",
xlab="Desplazamiento Motor",
ylab="Densidad",
col="lightgreen")
# Añadimos curva exponencial
x <- seq(min(Desmotor), max(Desmotor), 0.01)
curve(dexp(x, rate = lambda), col = "red", lwd = 2, add = TRUE)
Test:
## [1] 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000
## [13] 60000 65000## [1] 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000
## [13] 65000 70000# Frecuencias esperadas bajo la exponencial
Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(Desmotor)
X2_exp <- sum((FO - FEexp)^2 / FEexp)
X2_exp## [1] 51740.14## [1] 21.02607## [1] FALSE2 City MPG (FT1)
mpg_ciudad <- as.numeric(datos$City.MPG..FT1.) # Asegúrate de que esta sea la columna correcta
mpg_ciudad <- mpg_ciudad[!is.na(mpg_ciudad) & mpg_ciudad != -1] # Limpieza de valores
hist(mpg_ciudad)
## [1] 17.98111## [1] 6.849728# Histograma con densidad
HistogramaMpg_ciudad <- hist(mpg_ciudad, freq = FALSE,
main = "Gráfica No.3: Histograma
de City MPG con Curva Normal",
xlab = "City MPG",
ylab = "Densidad",
col = "#FAEBD7")
x <- seq(min(mpg_ciudad), max(mpg_ciudad), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2, add = TRUE)
## [1] 1## [1] 38113## [1] 870 28465 7959 568 108 22 7 8 28 19 16 4
## [13] 31 7 1Test:
## [1] 100## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(mpg_ciudad)
FE## [1] 4.483742e+03 1.882512e+04 1.312752e+04 1.486643e+03 2.483976e+01
## [6] 5.614414e-02 1.629035e-05 5.881636e-10 0.000000e+00 0.000000e+00
## [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00## [1] Inf## [1] 21.02607## [1] FALSEModelo Log-Normal:
mpg_ciudad <- as.numeric(datos$City.MPG..FT1.) # Ya está limpio
mpg_ciudad <- mpg_ciudad[!is.na(mpg_ciudad) & mpg_ciudad > 0]
log_mpg <- log(mpg_ciudad)
ulog <- mean(log_mpg)
ulog## [1] 2.846708## [1] 0.2732083# Histograma log-normal
HistogramaLogNormal <- hist(mpg_ciudad, freq = FALSE,
main = "Gráfica No.4: Histograma
de City MPG con curva Log-Normal",
xlab = "City MPG",
ylab = "Densidad",
col = "#FAEBD7")
x <- seq(min(mpg_ciudad), max(mpg_ciudad), 0.01)
curve(dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpg_ciudad)
X2_log <- sum((FO - FElog)^2 / FElog)
X2_log## [1] 71653593361## [1] 21.02607## [1] FALSE3 Unrounded City MPG (FT1)
# Limpieza de datos
MPGcity <- datos$Unrounded.City.MPG..FT1.
MPGcity <- MPGcity[!is.na(MPGcity) & MPGcity != "" & MPGcity != -1]
MPGcity <- as.numeric(MPGcity)
MPGcity <- MPGcity[MPGcity > 0]
u <- mean(MPGcity)
u## [1] 20.77443## [1] 11.20033# Histograma con densidad
HistogramaNormal <- hist(MPGcity, freq = FALSE,
main = "Gráfica No.5: Histograma
de MPG Ciudad (FT1) con Curva Normal",
xlab = "MPG en Ciudad",
ylab = "Densidad",
col = "aquamarine")
x <- seq(min(MPGcity), max(MPGcity), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(MPGcity)
valores_validos <- FE > 0
X2_normal <- sum(((FO[valores_validos] - FE[valores_validos])^2) / FE[valores_validos])
X2_normal## [1] 1.234317e+13## [1] 9## [1] 16.91898## [1] FALSEModelo Log-Normal:
## [1] 2.966193## [1] 0.3237831HistogramaLogNormal <- hist(MPGcity, freq = FALSE,
main = "Gráfica No.6: Histograma
de MPG Ciudad (FT1) con Curva Log-Normal",
xlab = "MPG en Ciudad",
ylab = "Densidad",
col = "aquamarine")
x <- seq(min(MPGcity), max(MPGcity), 0.01)
curve(dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(MPGcity)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 20233304## [1] 12## [1] 21.02607## [1] FALSEModelo Exponencial:
## [1] 0.0481361HistExp <- hist(MPGcity, freq=FALSE,
main="Gráfica No.7: Histograma
de MPG Ciudad (FT1) con Curva Exponencial",
xlab="MPG en Ciudad",
ylab="Densidad",
col="aquamarine")
x <- seq(min(MPGcity), max(MPGcity), 0.01)
curve(dexp(x, rate = lambda), col = "red", lwd = 2, add = TRUE)
Test:
## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(MPGcity)
valores_validos_exp <- FEexp > 0
X2_exp <- sum((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2 / FEexp[valores_validos_exp])
X2_exp## [1] 11626.9## [1] 13## [1] 22.36203## [1] FALSE4 City MPG (FT2)
# Limpieza de datos
mpg_ciudad2 <- datos$City.MPG..FT2.
mpg_ciudad2 <- mpg_ciudad2[!is.na(mpg_ciudad2) & mpg_ciudad2 != "" & mpg_ciudad2 != -1]
mpg_ciudad2 <- as.numeric(mpg_ciudad2)
mpg_ciudad2 <- mpg_ciudad2[mpg_ciudad2 > 0]
u <- mean(mpg_ciudad2)
u## [1] 14.47705## [1] 15.68556# Histograma con densidad
HistogramaNormal <- hist(mpg_ciudad2, freq = FALSE,
main = "Gráfica No.8: Histograma
de MPG Ciudad (FT2) con Curva Normal",
xlab = "MPG en Ciudad (FT2)",
ylab = "Densidad",
col = "#F0FFFF")
x <- seq(min(mpg_ciudad2), max(mpg_ciudad2), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(mpg_ciudad2)
valores_validos <- FE > 0
X2_normal <- sum(((FO[valores_validos] - FE[valores_validos])^2) / FE[valores_validos])
X2_normal## [1] 1.254054e+12## [1] 12## [1] 21.02607## [1] FALSEModelo Log-Normal:
## [1] 2.49527## [1] 0.4494793HistogramaLogNormal <- hist(mpg_ciudad2, freq = FALSE,
main = "Gráfica No.9: Histograma
de MPG Ciudad (FT2) con Curva Log-Normal",
xlab = "MPG en Ciudad (FT2)",
ylab = "Densidad",
col = "#F0FFFF")
x <- seq(min(mpg_ciudad2), max(mpg_ciudad2), 0.01)
curve(dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpg_ciudad2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 247602## [1] 12## [1] 21.02607## [1] FALSEModelo Exponencial:
## [1] 0.06907484HistExp <- hist(mpg_ciudad2, freq=FALSE,
main="Gráfica No.10: Histograma
de MPG Ciudad (FT2) con Curva Exponencial",
xlab="MPG en Ciudad (FT2)",
ylab="Densidad",
col="#F0FFFF")
x <- seq(min(mpg_ciudad2), max(mpg_ciudad2), 0.01)
curve(dexp(x, rate = lambda), col = "red", lwd = 2, add = TRUE)
Test:
## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(mpg_ciudad2)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 1076## [1] 13## [1] 22.36203## [1] FALSE5 Unrounded City MPG (FT2)
# Limpieza de datos
MPGcity2 <- datos$Unrounded.City.MPG..FT2.
MPGcity2 <- MPGcity2[!is.na(MPGcity2) & MPGcity2 != "" & MPGcity2 != -1]
MPGcity2 <- as.numeric(MPGcity2)
MPGcity2 <- MPGcity2[MPGcity2 > 0]
u <- mean(MPGcity2)
u## [1] 16.59429## [1] 19.2699# Histograma con densidad
HistogramaNormal <- hist(MPGcity2, freq = FALSE,
main = "Gráfica No.11: Histograma
de MPG Ciudad (FT2) con Curva Normal",
xlab = "MPG en Ciudad (FT2)",
ylab = "Densidad",
col = "#FFE4C4")
x <- seq(min(MPGcity2), max(MPGcity2), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(MPGcity2)
valores_validos <- FE > 0
X2_normal <- sum(((FO[valores_validos] - FE[valores_validos])^2) / FE[valores_validos])
X2_normal## [1] 15834894## [1] 12## [1] 21.02607## [1] FALSEModelo Log-Normal:
## [1] 2.571681## [1] 0.5345132HistogramaLogNormal <- hist(MPGcity2, freq = FALSE,
main = "Gráfica No.12: Histograma
de MPG Ciudad (FT2) con Curva Log-Normal",
xlab = "MPG en Ciudad (FT2)",
ylab = "Densidad",
col = "#FFE4C4")
x <- seq(min(MPGcity2), max(MPGcity2), 0.01)
curve(dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(MPGcity2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 7913.653## [1] 12## [1] 21.02607## [1] FALSEModelo Exponencial:
## [1] 0.06026171HistExp <- hist(MPGcity2, freq=FALSE,
main="Gráfica No.13: Histograma
de MPG Ciudad (FT2) con Curva Exponencial",
xlab="MPG en Ciudad (FT2)",
ylab="Densidad",
col="#F0FFFF")
x <- seq(min(MPGcity2), max(MPGcity2), 0.01)
curve(dexp(x, rate = lambda), col = "blue", lwd = 2, add = TRUE)
Test:
## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(MPGcity2)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 1050.895## [1] 13## [1] 22.36203## [1] FALSE6 City Gasoline Consumption (CD)
consumoMPG <- datos$City.Gasoline.Consumption..CD.
consumoMPG <- consumoMPG[!is.na(consumoMPG) & consumoMPG != "" & consumoMPG != -1]
consumoMPG <- as.numeric(consumoMPG)
consumoMPG <- consumoMPG[consumoMPG > 0]
lambda <- 1 / mean(consumoMPG)
lambda## [1] 0.8495764# Histograma con curva exponencial
HistExp <- hist(consumoMPG, freq=FALSE,
main="Gráfica No.8: Histograma
de Consumo de Gasolina en Ciudad con Curva Exponencial",
xlab="Consumo de Gasolina (CD)",
ylab="Densidad",
col="#0000FF")
x <- seq(min(consumoMPG), max(consumoMPG), 0.01)
curve(dexp(x, rate = lambda), col = "red", lwd = 2, add = TRUE)
Test:
## [1] 0 1 2 3 4 5## [1] 1 2 3 4 5 6Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(consumoMPG)
valores_validos <- FEexp > 0
X2_exp <- sum(((FO[valores_validos] - FEexp[valores_validos])^2) / FEexp[valores_validos])
X2_exp## [1] 38.8027## [1] 4## [1] 9.487729## [1] FALSE7 City Electricity Consumption
# Limpieza de datos
consumoelec <- datos$City.Electricity.Consumption
consumoelec <- consumoelec[!is.na(consumoelec) & consumoelec != "" & consumoelec != -1]
consumoelec <- as.numeric(consumoelec)
consumoelec <- consumoelec[consumoelec > 0]
u <- mean(consumoelec)
u## [1] 39.61481## [1] 14.94801# Histograma con densidad
HistogramaNormal <- hist(consumoelec, freq = FALSE,
main = "Gráfica No.9: Histograma
de Consumo de Energía con Curva Normal",
xlab = "Consumo de Energía (kWh)",
ylab = "Densidad",
col = "#A52A2A")
x <- seq(min(consumoelec), max(consumoelec), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "blue", lwd = 2, add = TRUE)
## [1] 1Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80 90 100 110 120## [1] 30 40 50 60 70 80 90 100 110 120 130P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(consumoelec)
FE## [1] 3.273252e+01 4.954700e+01 4.873102e+01 3.114142e+01 1.292635e+01
## [6] 3.483008e+00 6.087099e-01 6.892975e-02 5.051996e-03 2.393769e-04
## [11] 7.324411e-06valores_validos <- FE > 0
X2_normal <- sum(((FO[valores_validos] - FE[valores_validos])^2) / FE[valores_validos])
X2_normal## [1] 136602.7## [1] 8## [1] 15.50731## [1] FALSEModelo Log-Normal:
## [1] 3.623627## [1] 0.3183241HistogramaLogNormal <- hist(consumoelec, freq = FALSE,
main = "Gráfica No.10: Histograma
de Consumo de Energía con Curva Log-Normal",
xlab = "Consumo de Energía (kWh)",
ylab = "Densidad",
col = "#A52A2A")
x <- seq(min(consumoelec), max(consumoelec), 0.01)
curve(dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "blue", lwd = 2, add = TRUE)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80 90 100 110 120## [1] 30 40 50 60 70 80 90 100 110 120 130Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(consumoelec)
FElog ## [1] 43.17932136 67.09340271 46.79102490 22.34771140 8.86680961 3.21257310
## [7] 1.11693419 0.38297680 0.13154305 0.04567205 0.01611401valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 86.64989## [1] 8## [1] 15.50731## [1] FALSEModelo Exponencial:
## [1] 0.02524309HistExp <- hist(consumoelec, freq = FALSE,
main = "Gráfica No.11: Histograma
de Consumo de Energía con Curva Exponencial",
xlab = "Consumo de Energía (kWh)",
ylab = "Densidad",
col = "#A52A2A")
x <- seq(min(consumoelec), max(consumoelec), 0.01)
curve(dexp(x, rate = lambda), col = "blue", lwd = 2, add = TRUE)
Test:
## [1] 20 30 40 50 60 70 80 90 100 110 120## [1] 30 40 50 60 70 80 90 100 110 120 130Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(consumoelec)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 230.6118## [1] 9## [1] 16.91898## [1] FALSE8 City Utility Factor
utilidad <- datos$City.Utility.Factor
utilidad <- as.numeric(utilidad)
utilidad <- utilidad[!is.na(utilidad) & utilidad > 0]
u <- mean(utilidad)
u## [1] 0.4857585## [1] 0.1618752HistogramaNormal <- hist(utilidad, freq = FALSE,
main = "Gráfica No.12: Histograma
de Utilidad Urbana con Curva Normal",
xlab = "Utilidad Urbana",
ylab = "Densidad",
col = "#CDAA7D")
x <- seq(min(utilidad), max(utilidad), 0.01)
curve(dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0.3 0.4 0.5 0.6 0.7 0.8## [1] 0.4 0.5 0.6 0.7 0.8 0.9P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], u, sigma) - pnorm(liminf[i], u, sigma)
}
FE <- P * length(utilidad)
valores_validos <- FE > 0
X2_normal <- sum(((FO[valores_validos] - FE[valores_validos])^2) / FE[valores_validos])
X2_normal## [1] 42.53847## [1] 3## [1] 7.814728## [1] FALSEModelo Log-Normal:
HistogramaLogNormal <- hist(utilidad, freq = FALSE,
main = "Gráfica No.13: Histograma
de Utilidad Urbana con Curva Log-Normal",
xlab = "Utilidad Urbana",
ylab = "Densidad",
col = "#CDAA7D")
x <- seq(min(utilidad), max(utilidad), 0.01)
curve(dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0.3 0.4 0.5 0.6 0.7 0.8## [1] 0.4 0.5 0.6 0.7 0.8 0.9Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(utilidad)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 25.40164## [1] 3## [1] 7.814728## [1] FALSEModelo Exponencial:
## [1] 2.058636HistExp <- hist(utilidad, freq = FALSE,
main = "Gráfica No.14: Histograma
de Utilidad Urbana con Curva Exponencial",
xlab = "Utilidad Urbana",
ylab = "Densidad",
col = "#CDAA7D")
x <- seq(min(utilidad), max(utilidad), 0.01)
curve(dexp(x, rate = lambda), col = "red", lwd = 2, add = TRUE)
Test:
FO <- HistExp$counts
liminf <- HistExp$breaks[-length(HistExp$breaks)]
limsup <- HistExp$breaks[-1]
Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(utilidad)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 112.5837## [1] 4## [1] 9.487729## [1] FALSE9 Highway MPG (FT1)
# Limpieza de datos
carretera <- datos$Highway.MPG..FT1.
carretera <- as.numeric(carretera)
carretera <- carretera[!is.na(carretera) & carretera > 0]
u <- mean(carretera)
sigma <- sd(carretera)# Histograma y curva normal
HistogramaNormal <- hist(carretera, freq = FALSE,
main = "Gráfica No.15: Histograma
de MPG Carretera con Curva Normal",
xlab = "MPG Carretera",
ylab = "Densidad",
col = "#0000EE")
x <- seq(min(carretera), max(carretera), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
## [20] 100 105 110 115 120## [1] 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
## [20] 105 110 115 120 125P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(carretera)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 473164240605df_normal <- sum(valores_validos) - 1 - 2 # -2 por estimar media y sd
Vc_normal <- qchisq(0.95, df = df_normal)
Vc_normal## [1] 22.36203## [1] FALSEModelo Log-Normal:
# Histograma y curva log-normal
HistogramaLogNormal <- hist(carretera, freq = FALSE,
main = "Gráfica No.16: Histograma
de MPG Carretera con Curva Log-Normal",
xlab = "MPG Carretera",
ylab = "Densidad",
col = "#0000EE")
x <- seq(min(carretera), max(carretera), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
## [20] 100 105 110 115 120## [1] 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
## [20] 105 110 115 120 125Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(carretera)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 7691938df_log <- sum(valores_validos_log) - 1 - 2 # -2 por media y sd log
Vc_log <- qchisq(0.95, df = df_log)
Vc_log## [1] 32.67057## [1] FALSE10 Unrounded Highway MPG (FT1)
# Limpieza de datos
Mpgcarretera <- datos$Unrounded.Highway.MPG..FT1.
Mpgcarretera <- as.numeric(Mpgcarretera)
Mpgcarretera <- Mpgcarretera[!is.na(Mpgcarretera) & Mpgcarretera > 0]
u <- mean(Mpgcarretera)
u## [1] 27.6541## [1] 9.710954# Histograma y curva normal
HistogramaNormal <- hist(Mpgcarretera, freq = FALSE,
main = "Gráfica No.17:
Histograma de MPG Carretera con Curva Normal",
xlab = "MPG Carretera",
ylab = "Densidad",
col = "#A52A2A")
x <- seq(min(Mpgcarretera), max(Mpgcarretera), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "blue", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120## [1] 20 30 40 50 60 70 80 90 100 110 120 130P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(Mpgcarretera)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 3.10525e+12df_normal <- sum(valores_validos) - 1 - 2 # 2 parámetros estimados: media y desviación estándar
Vc_normal <- qchisq(0.95, df = df_normal)
Vc_normal## [1] 14.06714## [1] FALSEModelo Log-Normal:
# Histograma y curva log-normal
HistogramaLogNormal <- hist(Mpgcarretera, freq = FALSE,
main = "Gráfica No.18:
Histograma de MPG Carretera con Curva Log-Normal",
xlab = "MPG Carretera",
ylab = "Densidad",
col = "#A52A2A")
x <- seq(min(Mpgcarretera), max(Mpgcarretera), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "blue", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120## [1] 20 30 40 50 60 70 80 90 100 110 120 130Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(Mpgcarretera)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1090253## [1] 16.91898## [1] FALSE11 Highway MPG (FT2)
# Limpieza de datos
Mpgcarretera2 <- datos$Highway.MPG..FT2.
Mpgcarretera2 <- as.numeric(Mpgcarretera2)
Mpgcarretera2 <- Mpgcarretera2[!is.na(Mpgcarretera2) & Mpgcarretera2 > 0]
u <- mean(Mpgcarretera2)
u## [1] 18.85605## [1] 14.08596HistogramaNormal <- hist(Mpgcarretera2, freq = FALSE,
main = "Gráfica No.19: Histograma
de MPG Carretera (FT2) con Curva Normal",
xlab = "MPG Carretera (FT2)",
ylab = "Densidad",
col = "cadetblue1")
x <- seq(min(Mpgcarretera2), max(Mpgcarretera2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120## [1] 20 30 40 50 60 70 80 90 100 110 120 130P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(Mpgcarretera2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 2028539434## [1] 16.91898## [1] FALSEModelo Log-Normal:
## [1] 2.822761## [1] 0.3918665HistogramaLogNormal <- hist(Mpgcarretera2, freq = FALSE,
main = "Gráfica No.20: Histograma
de MPG Carretera (FT2) con Curva Log-Normal",
xlab = "MPG Carretera (FT2)",
ylab = "Densidad",
col = "cadetblue1")
x <- seq(min(Mpgcarretera2), max(Mpgcarretera2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120## [1] 20 30 40 50 60 70 80 90 100 110 120 130Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(Mpgcarretera2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 55930.21## [1] 16.91898## [1] FALSEModelo Exponencial:
## [1] 0.05303338HistExp <- hist(Mpgcarretera2, freq = FALSE,
main = "Gráfica No.21: Histograma
de MPG Carretera (FT2) con Curva Exponencial",
xlab = "MPG Carretera (FT2)",
ylab = "Densidad",
col = "cadetblue1")
x <- seq(min(Mpgcarretera2), max(Mpgcarretera2), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 10 20 30 40 50 60 70 80 90 100 110 120## [1] 20 30 40 50 60 70 80 90 100 110 120 130Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(Mpgcarretera2)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 2272.227df_exp <- sum(valores_validos_exp) - 1 - 1 # 1 parámetro estimado (lambda)
Vc_exp <- qchisq(0.95, df = df_exp)
Vc_exp## [1] 18.30704## [1] FALSE12 Unrounded Highway MPG (FT2)
# Limpieza de datos
Mpgcarretera22 <- datos$Unrounded.Highway.MPG..FT2.
Mpgcarretera22 <- as.numeric(Mpgcarretera22)
Mpgcarretera22 <- Mpgcarretera22[!is.na(Mpgcarretera22) & Mpgcarretera22 > 0]
lambda <- 1 / mean(Mpgcarretera22)
lambda## [1] 0.04739992HistExp <- hist(Mpgcarretera22, freq = FALSE,
main = "Gráfica No.22: Histograma
de MPG Carretera sin Red (FT2) con Curva Exponencial",
xlab = "MPG Carretera (FT2)",
ylab = "Densidad",
col = "#7FFF00")
x <- seq(min(Mpgcarretera22), max(Mpgcarretera22), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 10 20 30 40 50 60 70 80 90 100 110 120## [1] 20 30 40 50 60 70 80 90 100 110 120 130Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(Mpgcarretera2)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 558.2622df_exp <- sum(valores_validos_exp) - 1 - 1 # 1 parámetro estimado (lambda)
Vc_exp <- qchisq(0.95, df = df_exp)
Vc_exp## [1] 18.30704## [1] FALSE13 Highway Gasoline Consumption (CD)
# Limpieza de datos
carreteragas <- datos$Highway.Gasoline.Consumption..CD.
carreteragas <- as.numeric(carreteragas)
carreteragas <- carreteragas[!is.na(carreteragas) & carreteragas > 0]
lambda <- 1 / mean(carreteragas)
lambda## [1] 1.222426# Histograma y curva exponencial
HistExp <- hist(carreteragas, freq = FALSE,
main = "Gráfica No.23: Histograma de
Consumo de Gasolina en Carretera con Curva Exponencial",
xlab = "Consumo de Gasolina (gal)",
ylab = "Densidad",
col = "#FF7F24")
x <- seq(min(carreteragas), max(carreteragas), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 0 1 2 3 4## [1] 1 2 3 4 5Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(carreteragas)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 67.92282df_exp <- sum(valores_validos_exp) - 1 - 1 # -1 parámetro estimado (lambda)
Vc_exp <- qchisq(0.95, df = df_exp)
Vc_exp## [1] 7.814728## [1] FALSE14 Highway Electricity Consumption
# Limpieza de datos
carreteragas <- datos$Highway.Electricity.Consumption
carreteragas <- as.numeric(carreteragas)
carreteragas <- carreteragas[!is.na(carreteragas) & carreteragas > 0]
u <- mean(carreteragas)
u## [1] 42.19774## [1] 14.9563HistogramaNormal <- hist(carreteragas, freq = FALSE,
main = "Gráfica No.24: Histograma
de Consumo Eléctrico en Carretera con Curva Normal",
xlab = "Consumo Eléctrico (kWh)",
ylab = "Densidad",
col = "#FF7F50")
x <- seq(min(carreteragas), max(carreteragas), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80 90 100 110## [1] 30 40 50 60 70 80 90 100 110 120P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(carreteragas)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 3455.249## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 3.697229## [1] 0.2804669HistogramaLogNormal <- hist(carreteragas, freq = FALSE,
main = "Gráfica No.25: Histograma
de Consumo Eléctrico en Carretera con Curva Log-Normal",
xlab = "Consumo Eléctrico (kWh)",
ylab = "Densidad",
col = "#FF7F50")
x <- seq(min(carreteragas), max(carreteragas), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80 90 100 110## [1] 30 40 50 60 70 80 90 100 110 120Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(carreteragas)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 277.5895## [1] 14.06714## [1] FALSEModelo Exponencial:
## [1] 0.02369795HistExp <- hist(carreteragas, freq = FALSE,
main = "Gráfica No.26: Histograma
de Consumo Eléctrico en Carretera con Curva Exponencial",
xlab = "Consumo Eléctrico (kWh)",
ylab = "Densidad",
col = "#FF7F50")
x <- seq(min(carreteragas), max(carreteragas), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 20 30 40 50 60 70 80 90 100 110## [1] 30 40 50 60 70 80 90 100 110 120Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(carreteragas)
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 486.6106## [1] 15.50731## [1] FALSE15 Highway Utility Factor
# Limpieza de datos
utilidadcarre <- datos$Highway.Utility.Factor
utilidadcarre <- as.numeric(utilidadcarre)
utilidadcarre <- utilidadcarre[!is.na(utilidadcarre) & utilidadcarre > 0]
u <- mean(utilidadcarre)
u## [1] 0.4641385## [1] 0.1567848HistogramaNormal <- hist(utilidadcarre, freq = FALSE,
main = "Gráfica No.27: Histograma
de Utility Factor en Carretera con Curva Normal",
xlab = "Utility Factor",
ylab = "Densidad",
col = "#FFF8DC")
x <- seq(min(utilidadcarre), max(utilidadcarre), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8## [1] 0.3 0.4 0.5 0.6 0.7 0.8 0.9P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(utilidadcarre)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 34.32042## [1] 9.487729## [1] FALSEModelo Log-Normal:
## [1] -0.8192646## [1] 0.318924HistogramaLogNormal <- hist(utilidadcarre, freq = FALSE,
main = "Gráfica No.28: Histograma
de Utility Factor en Carretera con Curva Log-Normal",
xlab = "Utility Factor",
ylab = "Densidad",
col = "#FFF8DC")
x <- seq(min(utilidadcarre), max(utilidadcarre), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8## [1] 0.3 0.4 0.5 0.6 0.7 0.8 0.9Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(utilidadcarre)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 20.20751## [1] 9.487729## [1] FALSE16 Unadjusted City MPG (FT1)
# Limpieza de datos
cuidadmpg <- datos$Unadjusted.City.MPG..FT1.
cuidadmpg <- as.numeric(cuidadmpg)
cuidadmpg <- cuidadmpg[!is.na(cuidadmpg) & cuidadmpg > 0]
u <- mean(cuidadmpg)
u## [1] 22.66826## [1] 9.635877HistogramaNormal <- hist(cuidadmpg, freq = FALSE,
main = "Gráfica No.29: Histograma
de City MPG (FT1) con Curva Normal",
xlab = "City MPG (FT1)",
ylab = "Densidad",
col = "cyan")
x <- seq(min(cuidadmpg), max(cuidadmpg), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
## [20] 190 200 210 220## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
## [20] 200 210 220 230P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(cuidadmpg)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 189189554798## [1] 15.50731## [1] FALSEModelo Log-Normal:
## [1] 3.071552## [1] 0.2914421HistogramaLogNormal <- hist(cuidadmpg, freq = FALSE,
main = "Gráfica No.30: Histograma
de City MPG (FT1) con Curva Log-Normal",
xlab = "City MPG (FT1)",
ylab = "Densidad",
col = "cyan")
x <- seq(min(cuidadmpg), max(cuidadmpg), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
## [20] 190 200 210 220## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
## [20] 200 210 220 230Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(cuidadmpg)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 136881620285## [1] 31.41043## [1] FALSE17 Unadjusted Highway MPG (FT1)
# Limpieza de datos
carreteraMpg <- datos$Unadjusted.Highway.MPG..FT1.
carreteraMpg <- as.numeric(carreteraMpg)
carreteraMpg <- carreteraMpg[!is.na(carreteraMpg) & carreteraMpg > 0]
u <- mean(carreteraMpg)
u## [1] 33.70009## [1] 10.19994HistogramaNormal <- hist(carreteraMpg, freq = FALSE,
main = "Gráfica No.31: Histograma
de Unadjusted Highway MPG (FT1) con Curva Normal",
xlab = "Highway MPG (FT1)",
ylab = "Densidad",
col = "darkgoldenrod1")
x <- seq(min(carreteraMpg), max(carreteraMpg), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(carreteraMpg)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 159318655606## [1] 15.50731## [1] FALSEModelo Log-Normal:
## [1] 3.480057## [1] 0.2686124HistogramaLogNormal <- hist(carreteraMpg, freq = FALSE,
main = "Gráfica No.32: Histograma
de Unadjusted Highway MPG (FT1) con Curva Log-Normal",
xlab = "Highway MPG (FT1)",
ylab = "Densidad",
col = "darkgoldenrod1")
x <- seq(min(carreteraMpg), max(carreteraMpg), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(carreteraMpg)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 2964975## [1] 24.99579## [1] FALSE18 Unadjusted City MPG (FT2)
# Limpieza de datos
cuidadmpg2 <- datos$Unadjusted.City.MPG..FT2.
cuidadmpg2 <- as.numeric(cuidadmpg2)
cuidadmpg2 <- cuidadmpg2[!is.na(cuidadmpg2) & cuidadmpg2 > 0]
lambda <- 1 / mean(cuidadmpg2)
lambda## [1] 0.05303699# Histograma y curva exponencial
HistExp <- hist(cuidadmpg2, freq = FALSE,
main = "Gráfica No.33: Histograma
de MPG Ciudad (FT2) con Curva Exponencial",
xlab = "MPG Ciudad (FT2)",
ylab = "Densidad",
col = "#A2CD5A")
x <- seq(min(cuidadmpg2), max(cuidadmpg2), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 0 20 40 60 80 100 120 140 160 180 200## [1] 20 40 60 80 100 120 140 160 180 200 220Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(cuidadmpg2)
# Prueba de bondad de ajuste
valores_validos_exp <- FEexp > 0
X2_exp <- sum(((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2) / FEexp[valores_validos_exp])
X2_exp## [1] 1070.92df_exp <- sum(valores_validos_exp) - 1 - 1 # 1 parámetro estimado: lambda
Vc_exp <- qchisq(0.95, df = df_exp)
Vc_exp## [1] 16.91898## [1] FALSE19 Unadjusted Highway MPG (FT2)
# Limpieza de datos
carreteraMpg2 <- datos$Unadjusted.Highway.MPG..FT2.
carreteraMpg2 <- as.numeric(carreteraMpg2)
carreteraMpg2 <- carreteraMpg2[!is.na(carreteraMpg2) & carreteraMpg2 > 0]
u <- mean(carreteraMpg2)
u## [1] 26.62751## [1] 20.75965# Histograma y curva normal
HistogramaNormal <- hist(carreteraMpg2, freq = FALSE,
main = "Gráfica No.34: Histograma de
Unadjusted Highway MPG (FT2) con Curva Normal",
xlab = "Highway MPG (FT2)",
ylab = "Densidad",
col = "darkolivegreen4")
x <- seq(min(carreteraMpg2), max(carreteraMpg2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(carreteraMpg2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 308641136## [1] 23.68479## [1] FALSEModelo Log-Normal:
## [1] 3.157438## [1] 0.4102108# Histograma y curva log-normal
HistogramaLogNormal <- hist(carreteraMpg2, freq = FALSE,
main = "Gráfica No.35: Histograma de
Unadjusted Highway MPG (FT2) con Curva Log-Normal",
xlab = "Highway MPG (FT2)",
ylab = "Densidad",
col = "darkolivegreen4")
x <- seq(min(carreteraMpg2), max(carreteraMpg2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(carreteraMpg2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 31893.76## [1] 23.68479## [1] FALSE20 Combined MPG (FT1)
# Limpieza de datos
mpgcombinado <- datos$Combined.MPG..FT1.
mpgcombinado <- as.numeric(mpgcombinado)
mpgcombinado <- mpgcombinado[!is.na(mpgcombinado) & mpgcombinado > 0]
u <- mean(mpgcombinado)
u## [1] 20.21607## [1] 6.772655# Histograma y curva normal
HistogramaNormal <- hist(mpgcombinado, freq = FALSE,
main = "Gráfica No.36: Histograma
de Combined MPG (FT1) con Curva Normal",
xlab = "Combined MPG (FT1)",
ylab = "Densidad",
col = "#00BFFF")
x <- seq(min(mpgcombinado), max(mpgcombinado), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgcombinado)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 32202911516## [1] 11.0705## [1] FALSEModelo Log-Normal:
## [1] 2.968419## [1] 0.2633737# Histograma y curva log-normal
HistogramaLogNormal <- hist(mpgcombinado, freq = FALSE,
main = "Gráfica No.37: Histograma
de Combined MPG (FT1) con Curva Log-Normal",
xlab = "Combined MPG (FT1)",
ylab = "Densidad",
col = "#00BFFF")
x <- seq(min(mpgcombinado), max(mpgcombinado), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgcombinado)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1240732936## [1] 19.67514## [1] FALSE21 Unrounded Combined MPG (FT1)
# Limpieza de datos
mpgcombinado1 <- datos$Unrounded.Combined.MPG..FT1.
mpgcombinado1 <- as.numeric(mpgcombinado1)
mpgcombinado1 <- mpgcombinado1[!is.na(mpgcombinado1) & mpgcombinado1 > 0]
u <- mean(mpgcombinado1)
u## [1] 23.29061## [1] 10.42525# Histograma y curva normal
HistogramaNormal <- hist(mpgcombinado1, freq = FALSE,
main = "Gráfica No.38: Histograma
de Unrounded Combined MPG (FT1) con Curva Normal",
xlab = "Unrounded Combined MPG (FT1)",
ylab = "Densidad",
col = "#FFD700")
x <- seq(min(mpgcombinado1), max(mpgcombinado1), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgcombinado1)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 792807587176## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 3.094357## [1] 0.2960055# Histograma y curva log-normal
HistogramaLogNormal <- hist(mpgcombinado, freq = FALSE,
main = "Gráfica No.39: Histograma
de Unrounded Combined MPG (FT1) con Curva Log-Normal",
xlab = "Unrounded Combined MPG (FT1)",
ylab = "Densidad",
col = "#FFD700")
x <- seq(min(mpgcombinado1), max(mpgcombinado1), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgcombinado)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1032727## [1] 19.67514## [1] FALSE22 Combined MPG (FT2)
# Limpieza de datos
mpgcombinado2 <- datos$Combined.MPG..FT2.
mpgcombinado2 <- as.numeric(mpgcombinado2)
mpgcombinado2 <- mpgcombinado2[!is.na(mpgcombinado2) & mpgcombinado2 > 0]
u <- mean(mpgcombinado2)
u## [1] 16.08484## [1] 14.86491# Histograma y curva normal
HistogramaNormal <- hist(mpgcombinado2, freq = FALSE,
main = "Gráfica No.40: Histograma
de Combined MPG (FT2) con Curva Normal",
xlab = "Combined MPG (FT2)",
ylab = "Densidad",
col = "#838B83")
x <- seq(min(mpgcombinado2), max(mpgcombinado2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgcombinado2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 76520244882## [1] 19.67514## [1] FALSEModelo Log-Normal:
## [1] 2.631469## [1] 0.4231796# Histograma y curva log-normal
HistogramaLogNormal <- hist(mpgcombinado2, freq = FALSE,
main = "Gráfica No.41: Histograma de
Combined MPG (FT2) con Curva Log-Normal",
xlab = "Combined MPG (FT2)",
ylab = "Densidad",
col = "#838B83")
x <- seq(min(mpgcombinado2), max(mpgcombinado2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgcombinado2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 123150.5## [1] 19.67514## [1] FALSE23 Unrounded Combined MPG (FT2)
# Limpieza de datos
mpgcombinado2 <- datos$Unrounded.Combined.MPG..FT1.
mpgcombinado2 <- as.numeric(mpgcombinado2)
mpgcombinado2 <- mpgcombinado2[!is.na(mpgcombinado2) & mpgcombinado2 > 0]
u <- mean(mpgcombinado2)
u## [1] 23.29061## [1] 10.42525# Histograma y curva normal
HistogramaNormal <- hist(mpgcombinado2, freq = FALSE,
main = "Gráfica No.42: Histograma
de Unrounded Combined MPG (FT2) con Curva Normal",
xlab = "Unrounded Combined MPG (FT2)",
ylab = "Densidad",
col = "#FF69B4")
x <- seq(min(mpgcombinado2), max(mpgcombinado2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgcombinado2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 792807587176## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 3.094357## [1] 0.2960055# Histograma y curva log-normal
HistogramaLogNormal <- hist(mpgcombinado2, freq = FALSE,
main = "Gráfica No.43: Histograma
de Unrounded Combined MPG (FT2) con Curva Log-Normal",
xlab = "Unrounded Combined MPG (FT2)",
ylab = "Densidad",
col = "#FF69B4")
x <- seq(min(mpgcombinado2), max(mpgcombinado2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130## [1] 20 30 40 50 60 70 80 90 100 110 120 130 140Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgcombinado)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1041385## [1] 18.30704## [1] FALSE24 Combined Electricity Consumption
# Limpieza de datos
combinadoelec <- datos$Combined.Electricity.Consumption
combinadoelec <- as.numeric(combinadoelec)
combinadoelec <- combinadoelec[!is.na(combinadoelec) & combinadoelec > 0]
u <- mean(combinadoelec)
u## [1] 40.72339## [1] 14.72142# Histograma y curva normal
HistogramaNormal <- hist(combinadoelec, freq = FALSE,
main = "Gráfica No.44: Histograma
de Combined Electricity Consumption con Curva Normal",
xlab = "Combined Electricity Consumption (kWh)",
ylab = "Densidad",
col = "lavenderblush1")
x <- seq(min(combinadoelec), max(combinadoelec), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80 90 100 110 120## [1] 30 40 50 60 70 80 90 100 110 120 130P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(combinadoelec)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 142305.6## [1] 15.50731## [1] FALSEModelo Log-Normal:
## [1] 3.657823## [1] 0.2954045# Histograma y curva log-normal
HistogramaLogNormal <- hist(combinadoelec, freq = FALSE,
main = "Gráfica No.45: Histograma de Combined
Electricity Consumption con Curva Log-Normal",
xlab = "Combined Electricity Consumption (kWh)",
ylab = "Densidad",
col = "lavenderblush1")
x <- seq(min(combinadoelec), max(combinadoelec), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80 90 100 110 120## [1] 30 40 50 60 70 80 90 100 110 120 130Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(combinadoelec)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 163.6557## [1] 15.50731## [1] FALSE25 Combined Gasoline Consumption (CD)
# Limpieza de datos
combinadogas <- datos$Combined.Gasoline.Consumption..CD.
combinadogas <- as.numeric(combinadogas)
combinadogas <- combinadogas[!is.na(combinadogas) & combinadogas > 0]
lambda <- 1 / mean(combinadogas)
lambda## [1] 1.357527HistExp <- hist(combinadogas, freq = FALSE,
main = "Gráfica No.46: Histograma de Combined
Gasoline Consumption (CD) con Curva Exponencial",
xlab = "Consumo de Gasolina Combinado (galones)",
ylab = "Densidad",
col = "#FF83FA")
x <- seq(min(combinadogas), max(combinadogas), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 0 1 2 3 4## [1] 1 2 3 4 5Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(combinadogas)
valores_validos_exp <- FEexp > 0
X2_exp <- sum((FO[valores_validos_exp] - FEexp[valores_validos_exp])^2 / FEexp[valores_validos_exp])
X2_exp## [1] 69.40032## [1] 7.814728## [1] FALSE26 Combined Utility Factor
# Limpieza de datos
combinadouti <- datos$Combined.Utility.Factor
combinadouti <- as.numeric(combinadouti)
combinadouti <- combinadouti[!is.na(combinadouti) & combinadouti > 0]
u <- mean(combinadouti)
u## [1] 0.4761246## [1] 0.1584115HistogramaNormal <- hist(combinadouti, freq = FALSE,
main = "Gráfica No.47: Histograma de
Combined Utility Factor con Curva Normal",
xlab = "Combined Utility Factor",
ylab = "Densidad",
col = "#FFC0CB")
x <- seq(min(combinadouti), max(combinadouti), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8## [1] 0.3 0.4 0.5 0.6 0.7 0.8 0.9P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(combinadouti)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 31.6868## [1] 9.487729## [1] FALSEModelo Log-Normal:
## [1] -0.7915008## [1] 0.3101012HistogramaLogNormal <- hist(combinadouti, freq = FALSE,
main = "Gráfica No.48: Histograma
de Combined Utility Factor con Curva Log-Normal",
xlab = "Combined Utility Factor",
ylab = "Densidad",
col = "#FFC0CB")
x <- seq(min(combinadouti), max(combinadouti), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8## [1] 0.3 0.4 0.5 0.6 0.7 0.8 0.9Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(combinadouti)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 20.27282## [1] 9.487729## [1] FALSE27 Annual Fuel Cost (FT1)
# Limpieza de datos
costoanual <- datos$Annual.Fuel.Cost..FT1.
costoanual <- as.numeric(costoanual)
costoanual <- costoanual[!is.na(costoanual) & costoanual > 0]
u <- mean(costoanual)
u## [1] 1970.675## [1] 532.5552HistogramaNormal <- hist(costoanual, freq = FALSE,
main = "Gráfica No.49: Histograma de
Annual Fuel Cost (FT1) con Curva Normal",
xlab = "Annual Fuel Cost (FT1)",
ylab = "Densidad",
col = "#87CEEB")
x <- seq(min(costoanual), max(costoanual), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000## [1] 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(costoanual)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 34151630848## [1] 16.91898## [1] FALSEModelo Log-Normal:
## [1] 7.550055## [1] 0.2711438HistogramaLogNormal <- hist(costoanual, freq = FALSE,
main = "Gráfica No.50: Histograma
de Annual Fuel Cost (FT1) con Curva Log-Normal",
xlab = "Annual Fuel Cost (FT1)",
ylab = "Densidad",
col = "#87CEEB")
x <- seq(min(costoanual), max(costoanual), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000## [1] 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(costoanual)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1058.643## [1] 16.91898## [1] FALSE28 Annual Fuel Cost (FT2)
# Limpieza de datos
costoanual2 <- datos$Annual.Fuel.Cost..FT2.
costoanual2 <- as.numeric(costoanual2)
costoanual2 <- costoanual2[!is.na(costoanual2) & costoanual2 > 0]
u <- mean(costoanual2)
u## [1] 2290.668## [1] 494.6834HistogramaNormal <- hist(costoanual2, freq = FALSE,
main = "Gráfica No.51: Histograma de
Annual Fuel Cost (FT2) con Curva Normal",
xlab = "Annual Fuel Cost (FT2)",
ylab = "Densidad",
col = "#D2B48C")
x <- seq(min(costoanual2), max(costoanual2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200
## [16] 3400 3600## [1] 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400
## [16] 3600 3800P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(costoanual2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 695.0383## [1] 23.68479## [1] FALSEModelo Log-Normal:
## [1] 7.709663## [1] 0.2446102HistogramaLogNormal <- hist(costoanual2, freq = FALSE,
main = "Gráfica No.52: Histograma
de Annual Fuel Cost (FT2) con Curva Log-Normal",
xlab = "Annual Fuel Cost (FT2)",
ylab = "Densidad",
col = "#D2B48C")
x <- seq(min(costoanual2), max(costoanual2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200
## [16] 3400 3600## [1] 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400
## [16] 3600 3800Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(costoanual2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 648308## [1] 23.68479## [1] FALSE29 Save or Spend (5 Year)
## [1] -3101.75## [1] 2664.985HistogramaNormal <- hist(ahorrargas, freq = FALSE,
main = "Gráfica No.53: Histograma
de Save or Spend (5 Year) con Curva Normal",
xlab = "Save or Spend (5 Year)",
ylab = "Densidad",
col = "#FFFF00")
x <- seq(min(ahorrargas), max(ahorrargas), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] -24000 -22000 -20000 -18000 -16000 -14000 -12000 -10000 -8000 -6000
## [11] -4000 -2000 0 2000 4000## [1] -22000 -20000 -18000 -16000 -14000 -12000 -10000 -8000 -6000 -4000
## [11] -2000 0 2000 4000 6000P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(ahorrargas)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 990916658## [1] 21.02607## [1] FALSEModelo Log-Normal:
## Warning in log(ahorrargas): Se han producido NaNs## [1] NaN## [1] NAHistogramaLogNormal <- hist(ahorrargas, freq = FALSE,
main = "Gráfica No.54: Histograma
de Save or Spend (5 Year) con Curva Log-Normal",
xlab = "Save or Spend (5 Year)",
ylab = "Densidad",
col = "#FFFF00")
x <- seq(min(ahorrargas), max(ahorrargas), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] -24000 -22000 -20000 -18000 -16000 -14000 -12000 -10000 -8000 -6000
## [11] -4000 -2000 0 2000 4000## [1] -22000 -20000 -18000 -16000 -14000 -12000 -10000 -8000 -6000 -4000
## [11] -2000 0 2000 4000 6000Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(ahorrargas)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log ## [1] NA## [1] NA## [1] NA30 Annual Consumption in Barrels (FT1)
# Limpieza de datos
consumobarr <- datos$Annual.Consumption.in.Barrels..FT1.
consumobarr <- as.numeric(consumobarr)
consumobarr <- consumobarr[!is.na(consumobarr) & consumobarr > 0]
u <- mean(consumobarr)
u## [1] 17.51763## [1] 4.577019HistogramaNormal <- hist(consumobarr, freq = FALSE,
main = "Gráfica No.55: Histograma de Annual
Consumption in Barrels (FT1) con Curva Normal",
xlab = "Annual Consumption in Barrels (FT1)",
ylab = "Densidad",
col = "#F5DEB3")
x <- seq(min(consumobarr), max(consumobarr), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46## [1] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(consumobarr)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 2935688## [1] 32.67057## [1] FALSEModelo Log-Normal:
## [1] 2.814365## [1] 0.4092279HistogramaLogNormal <- hist(consumobarr, freq = FALSE,
main = "Gráfica No.56: Histograma de Annual
Consumption in Barrels (FT1) con Curva Log-Normal",
xlab = "Annual Consumption in Barrels (FT1)",
ylab = "Densidad",
col = "#F5DEB3")
x <- seq(min(consumobarr), max(consumobarr), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46## [1] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(consumobarr)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 9362532## [1] 32.67057## [1] FALSE31 Annual Consumption in Barrels (FT2)
# Limpieza de datos
consumobarr2 <- datos$Annual.Consumption.in.Barrels..FT2.
consumobarr2 <- as.numeric(consumobarr2)
consumobarr2 <- consumobarr2[!is.na(consumobarr2) & consumobarr2 > 0]
u <- mean(consumobarr2)
u## [1] 5.782774## [1] 1.579549HistogramaNormal <- hist(consumobarr2, freq = FALSE,
main = "Gráfica No.57: Histograma de Annual
Consumption in Barrels (FT2) con Curva Normal",
xlab = "Annual Consumption in Barrels (FT2)",
ylab = "Densidad",
col = "turquoise2")
x <- seq(min(consumobarr2), max(consumobarr2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 2 4 6 8 10 12 14 16 18## [1] 2 4 6 8 10 12 14 16 18 20P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(consumobarr2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 533080771615## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 1.662494## [1] 0.6148547HistogramaLogNormal <- hist(consumobarr2, freq = FALSE,
main = "Gráfica No.58: Histograma de Annual
Consumption in Barrels (FT2) con Curva Log-Normal",
xlab = "Annual Consumption in Barrels (FT2)",
ylab = "Densidad",
col = "turquoise2")
x <- seq(min(consumobarr2), max(consumobarr2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 2 4 6 8 10 12 14 16 18## [1] 2 4 6 8 10 12 14 16 18 20Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(consumobarr2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1279.505## [1] 14.06714## [1] FALSE32 Tailpipe CO2 (FT1)
# Limpieza de datos
co2tubo <- datos$Tailpipe.CO2..FT1.
co2tubo <- as.numeric(co2tubo)
co2tubo <- co2tubo[!is.na(co2tubo) & co2tubo > 0]
u <- mean(co2tubo)
u## [1] 411.8011## [1] 103.5849HistogramaNormal <- hist(co2tubo, freq = FALSE,
main = "Gráfica No.59: Histograma de
Tailpipe CO2 (FT1) con Curva Normal",
xlab = "Tailpipe CO2 (FT1)",
ylab = "Densidad",
col = "#CDC9C9")
x <- seq(min(co2tubo), max(co2tubo), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800## [1] 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(co2tubo)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 476.0377## [1] 23.68479## [1] FALSEModelo Log-Normal:
## [1] 5.987002## [1] 0.2693047HistogramaLogNormal <- hist(co2tubo, freq = FALSE,
main = "Gráfica No.60: Histograma de
Tailpipe CO2 (FT1) con Curva Log-Normal",
xlab = "Tailpipe CO2 (FT1)",
ylab = "Densidad",
col = "#CDC9C9")
x <- seq(min(co2tubo), max(co2tubo), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800## [1] 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(co2tubo)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 405626382680## [1] 23.68479## [1] FALSE33 Tailpipe CO2 in Grams/Mile (FT1)
## [1] 472.7614## [1] 122.2004HistogramaNormal <- hist(co2tubo1, freq = FALSE,
main = "Gráfica No.61: Histograma de
Tailpipe CO2 (FT1) con Curva Normal",
xlab = "Tailpipe CO2 (g/mi)",
ylab = "Densidad",
col = "#8B8989")
x <- seq(min(co2tubo1), max(co2tubo1), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 100 200 300 400 500 600 700 800 900 1000 1100 1200## [1] 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(co2tubo1)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 727805.1## [1] 18.30704## [1] FALSEModelo Log-Normal:
## [1] -Inf## [1] NaNHistogramaLogNormal <- hist(co2tubo1, freq = FALSE,
main = "Gráfica No.62: Histograma de
Tailpipe CO2 (FT1) con Curva Log-Normal",
xlab = "Tailpipe CO2 (g/mi)",
ylab = "Densidad",
col = "#8B8989")
x <- seq(min(co2tubo1), max(co2tubo1), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 100 200 300 400 500 600 700 800 900 1000 1100 1200## [1] 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(co2tubo1)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] NA## [1] NA## [1] NA34 Tailpipe CO2 (FT2)
# Limpieza de datos
co2tubo2 <- datos$Tailpipe.CO2..FT1.
co2tubo2 <- as.numeric(co2tubo2)
co2tubo2 <- co2tubo2[!is.na(co2tubo2) & co2tubo2 > 0]
u <- mean(co2tubo2)
u## [1] 411.8011## [1] 103.5849HistogramaNormal <- hist(co2tubo2, freq = FALSE,
main = "Gráfica No.63: Histograma de
Tailpipe CO2 (FT2) con Curva Normal",
xlab = "Tailpipe CO2 (FT2)",
ylab = "Densidad",
col = "#CDC9C9")
x <- seq(min(co2tubo2), max(co2tubo2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800## [1] 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(co2tubo2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 476.0377## [1] 23.68479## [1] FALSEModelo Log-Normal:
## [1] 5.987002## [1] 0.2693047HistogramaLogNormal <- hist(co2tubo2, freq = FALSE,
main = "Gráfica No.64: Histograma de
Tailpipe CO2 (FT2) con Curva Log-Normal",
xlab = "Tailpipe CO2 (FT2)",
ylab = "Densidad",
col = "#CDC9C9")
x <- seq(min(co2tubo2), max(co2tubo2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800## [1] 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(co2tubo2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 405626382680## [1] 23.68479## [1] FALSE35 Tailpipe CO2 in Grams/Mile (FT2)
# Limpieza de datos
co2tubo3 <- datos$Tailpipe.CO2.in.Grams.Mile..FT2.
co2tubo3 <- as.numeric(co2tubo3)
co2tubo3 <- co2tubo3[!is.na(co2tubo3) & co2tubo3 > 0]
u <- mean(co2tubo3)
u## [1] 495.5821## [1] 96.64509HistogramaNormal <- hist(co2tubo3, freq = FALSE,
main = "Gráfica No.65: Histograma de
Tailpipe CO2 (FT2) con Curva Normal",
xlab = "Tailpipe CO2 (g/mi)",
ylab = "Densidad",
col = "#FFFAFA")
x <- seq(min(co2tubo3), max(co2tubo3), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 250 300 350 400 450 500 550 600 650 700## [1] 300 350 400 450 500 550 600 650 700 750P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(co2tubo3)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 178.5956## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 6.185852## [1] 0.2023022HistogramaLogNormal <- hist(co2tubo3, freq = FALSE,
main = "Gráfica No.66: Histograma de
Tailpipe CO2 (FT2) con Curva Log-Normal",
xlab = "Tailpipe CO2 (g/mi)",
ylab = "Densidad",
col = "#FFFAFA")
x <- seq(min(co2tubo3), max(co2tubo3), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 250 300 350 400 450 500 550 600 650 700## [1] 300 350 400 450 500 550 600 650 700 750Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(co2tubo3)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 224.4972## [1] 14.06714## [1] FALSE36 2D Passenger Volume
# Limpieza de datos
volpasajeros2d <- datos$X2D.Passenger.Volume
volpasajeros2d <- as.numeric(volpasajeros2d)
volpasajeros2d <- volpasajeros2d[!is.na(volpasajeros2d) & volpasajeros2d > 0]
u <- mean(volpasajeros2d)
u## [1] 83.06513## [1] 12.9218HistogramaNormal <- hist(volpasajeros2d, freq = FALSE,
main = "Gráfica No.67: Histograma
de 2D Passenger Volume con Curva Normal",
xlab = "2D Passenger Volume (ft³)",
ylab = "Densidad",
col = "#FF6347")
x <- seq(min(volpasajeros2d), max(volpasajeros2d), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "blue", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
## [20] 190## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
## [20] 200P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(volpasajeros2d)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 7.027915e+13## [1] 27.58711## [1] FALSEModelo Log-Normal:
HistogramaLogNormal <- hist(volpasajeros2d, freq = FALSE,
main = "Gráfica No.68: Histograma de
2D Passenger Volume con Curva Log-Normal",
xlab = "2D Passenger Volume (ft³)",
ylab = "Densidad",
col = "#FF6347")
x <- seq(min(volpasajeros2d), max(volpasajeros2d), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "blue", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
## [20] 190## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
## [20] 200Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(volpasajeros2d)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 3.620147e+14## [1] 27.58711## [1] FALSE37 2D Luggage Volume
# Limpieza de datos
volequipaje2d <- datos$X2D.Luggage.Volume
volequipaje2d <- as.numeric(volequipaje2d)
volequipaje2d <- volequipaje2d[!is.na(volequipaje2d) & volequipaje2d > 0]
u <- mean(volequipaje2d)
u## [1] 11.18998## [1] 3.763901# Histograma con curva normal
HistogramaNormal <- hist(volequipaje2d, freq = FALSE,
main = "Gráfica No.69: Histograma
de 2D Luggage Volume con Curva Normal",
xlab = "2D Luggage Volume (ft³)",
ylab = "Densidad",
col = "#D2B48C")
x <- seq(min(volequipaje2d), max(volequipaje2d), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40## [1] 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(volequipaje2d)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 16460760724df_normal <- sum(valores_validos) - 1 - 2 # -1 clases, -2 parámetros estimados
Vc_normal <- qchisq(0.95, df = df_normal)
Vc_normal## [1] 27.58711## [1] FALSEModelo Log-Normal:
## [1] 2.349035## [1] 0.3855433HistogramaLogNormal <- hist(volequipaje2d, freq = FALSE,
main = "Gráfica No.70: Histograma
de 2D Luggage Volume con Curva Log-Normal",
xlab = "2D Luggage Volume (ft³)",
ylab = "Densidad",
col = "#D2B48C")
x <- seq(min(volequipaje2d), max(volequipaje2d), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40## [1] 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) - plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(volequipaje2d)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 1968.986## [1] 27.58711## [1] FALSE38 4D Passenger Volume
# Limpieza de datos
volpasajeros4d <- datos$X4D.Passenger.Volume
volpasajeros4d <- as.numeric(volpasajeros4d)
volpasajeros4d <- volpasajeros4d[!is.na(volpasajeros4d) & volpasajeros4d > 0]
u <- mean(volpasajeros4d)
u## [1] 95.43927## [1] 8.653147HistogramaNormal <- hist(volpasajeros4d, freq = FALSE,
main = "Gráfica No.71: Histograma
de 4D Passenger Volume con Curva Normal",
xlab = "4D Passenger Volume (ft³)",
ylab = "Densidad",
col = "#00FF7F")
x <- seq(min(volpasajeros4d), max(volpasajeros4d), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
## [20] 190## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
## [20] 200P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(volpasajeros4d)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 6.854797e+19## [1] 23.68479## [1] FALSEModelo Log-Normal:
## [1] 4.553899## [1] 0.1044744HistogramaLogNormal <- hist(volpasajeros4d, freq = FALSE,
main = "Gráfica No.72: Histograma de
4D Passenger Volume con Curva Log-Normal",
xlab = "4D Passenger Volume (ft³)",
ylab = "Densidad",
col = "#00FF7F")
x <- seq(min(volpasajeros4d), max(volpasajeros4d), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
## [20] 190## [1] 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
## [20] 200Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(volpasajeros4d)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 6.845194e+99## [1] 27.58711## [1] FALSE39 4D Luggage Volume
# Limpieza de datos
volpequipaje4d <- datos$X4D.Luggage.Volume
volpequipaje4d <- as.numeric(volpequipaje4d)
volpequipaje4d <- volpequipaje4d[!is.na(volpequipaje4d) & volpequipaje4d > 0]
u <- mean(volpequipaje4d)
u## [1] 17.39152## [1] 8.467823HistogramaNormal <- hist(volpequipaje4d, freq = FALSE,
main = "Gráfica No.73: Histograma
de 4D Luggage Volume con Curva Normal",
xlab = "4D Luggage Volume (ft³)",
ylab = "Densidad",
col = "steelblue1")
x <- seq(min(volpequipaje4d), max(volpequipaje4d), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 5 10 15 20 25 30 35 40 45 50## [1] 5 10 15 20 25 30 35 40 45 50 55P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(volpequipaje4d)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 20779.14df_normal <- sum(valores_validos) - 1 - 2 # -1 clases, -2 parámetros estimados
Vc_normal <- qchisq(0.95, df = df_normal)
Vc_normal## [1] 15.50731## [1] FALSEModelo Log-Normal:
## [1] 2.769246## [1] 0.3915995HistogramaLogNormal <- hist(volpequipaje4d, freq = FALSE,
main = "Gráfica No.74: Histograma
de 4D Luggage Volume con Curva Log-Normal",
xlab = "4D Luggage Volume (ft³)",
ylab = "Densidad",
col = "steelblue1")
x <- seq(min(volpequipaje4d), max(volpequipaje4d), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 5 10 15 20 25 30 35 40 45 50## [1] 5 10 15 20 25 30 35 40 45 50 55Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(volpequipaje4d)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 5661.281## [1] 15.50731## [1] FALSE40 Hatchback Passenger Volume
# Limpieza de datos
volpasajeros <- datos$Hatchback.Passenger.Volume
volpasajeros <- as.numeric(volpasajeros)
volpasajeros <- volpasajeros[!is.na(volpasajeros) & volpasajeros > 0]
u <- mean(volpasajeros)
u## [1] 85.53276## [1] 8.930174# Histograma y curva normal
HistogramaNormal <- hist(volpasajeros, freq = FALSE,
main = "Gráfica No.75: Histograma
de Hatchback Passenger Volume con Curva Normal",
xlab = "Hatchback Passenger Volume (ft³)",
ylab = "Densidad",
col = "#FFFAFA")
x <- seq(min(volpasajeros), max(volpasajeros), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190## [1] 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(volpasajeros)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 8299.69## [1] 16.91898## [1] FALSEModelo Log-Normal:
## [1] 4.443429## [1] 0.10573HistogramaLogNormal <- hist(volpasajeros, freq = FALSE,
main = "Gráfica No.76: Histograma
de Hatchback Passenger Volume con Curva Log-Normal",
xlab = "Hatchback Passenger Volume (ft³)",
ylab = "Densidad",
col = "#FFFAFA")
x <- seq(min(volpasajeros), max(volpasajeros), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190## [1] 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(volpasajeros)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 238918868137## [1] 22.36203## [1] FALSE41 Hatchback Luggage Volume
# Limpieza de datos
volequipaje <- datos$Hatchback.Luggage.Volume
volequipaje <- as.numeric(volequipaje)
volequipaje <- volequipaje[!is.na(volequipaje) & volequipaje > 0]
u <- mean(volequipaje)
u## [1] 16.65719## [1] 6.910848HistogramaNormal <- hist(volequipaje, freq = FALSE,
main = "Gráfica No.77: Histograma
de Hatchback Luggage Volume con Curva Normal",
xlab = "Hatchback Luggage Volume (ft³)",
ylab = "Densidad",
col = "#EEAEEE")
x <- seq(min(volequipaje), max(volequipaje), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 5 10 15 20 25 30 35 40 45## [1] 5 10 15 20 25 30 35 40 45 50P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(volequipaje)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 3476.215## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 2.739865## [1] 0.3749482HistogramaLogNormal <- hist(volequipaje, freq = FALSE,
main = "Gráfica No.78: Histograma
de Hatchback Luggage Volume con Curva Log-Normal",
xlab = "Hatchback Luggage Volume (ft³)",
ylab = "Densidad",
col = "#EEAEEE")
x <- seq(min(volequipaje), max(volequipaje), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 5 10 15 20 25 30 35 40 45## [1] 5 10 15 20 25 30 35 40 45 50Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(volequipaje)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 551.4551## [1] 14.06714## [1] FALSE42 Composite City MPG
# Limpieza de datos
mpgurbano <- datos$Composite.City.MPG
mpgurbano <- as.numeric(mpgurbano)
mpgurbano <- mpgurbano[!is.na(mpgurbano) & mpgurbano > 0]
u <- mean(mpgurbano)
u## [1] 48.2## [1] 20.35482HistogramaNormal <- hist(mpgurbano, freq = FALSE,
main = "Gráfica No.79: Histograma
de Composite City MPG con Curva Normal",
xlab = "Composite City MPG",
ylab = "Densidad",
col = "#FFA500")
x <- seq(min(mpgurbano), max(mpgurbano), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90## [1] 20 30 40 50 60 70 80 90 100P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgurbano)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 51.08317## [1] 12.59159## [1] FALSEModelo Log-Normal:
## [1] 3.784142## [1] 0.4407043HistogramaLogNormal <- hist(mpgurbano, freq = FALSE,
main = "Gráfica No.80: Histograma
de Composite City MPG con Curva Log-Normal",
xlab = "Composite City MPG",
ylab = "Densidad",
col = "#FFA500")
x <- seq(min(mpgurbano), max(mpgurbano), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80 90## [1] 20 30 40 50 60 70 80 90 100Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgurbano)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 44.99491## [1] 12.59159## [1] FALSE43 Composite Highway MPG
# Limpieza de datos
mpgcompuesto <- datos$Composite.Highway.MPG
mpgcompuesto <- as.numeric(mpgcompuesto)
mpgcompuesto <- mpgcompuesto[!is.na(mpgcompuesto) & mpgcompuesto > 0]
u <- mean(mpgcompuesto)
u## [1] 47.43077## [1] 15.06649HistogramaNormal <- hist(mpgcompuesto, freq = FALSE,
main = "Gráfica No.81: Histograma
de Composite Highway MPG con Curva Normal",
xlab = "Composite Highway MPG",
ylab = "Densidad",
col = "#CD8C95")
x <- seq(min(mpgcompuesto), max(mpgcompuesto), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80## [1] 30 40 50 60 70 80 90P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgcompuesto)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 6.407509## [1] 9.487729## [1] TRUEModelo Log-Normal:
## [1] 3.807732## [1] 0.3293016HistogramaLogNormal <- hist(mpgcompuesto, freq = FALSE,
main = "Gráfica No.82: Histograma
de Composite Highway MPG con Curva Log-Normal",
xlab = "Composite Highway MPG",
ylab = "Densidad",
col = "#CD8C95")
x <- seq(min(mpgcompuesto), max(mpgcompuesto), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 20 30 40 50 60 70 80## [1] 30 40 50 60 70 80 90Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgcompuesto)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 3.599147## [1] 9.487729## [1] TRUE44 Composite Combined MPG
# Limpieza de datos
mpgcompuestocomb <- datos$Composite.Combined.MPG
mpgcompuestocomb <- as.numeric(mpgcompuestocomb)
mpgcompuestocomb <- mpgcompuestocomb[!is.na(mpgcompuestocomb) & mpgcompuestocomb > 0]
u <- mean(mpgcompuestocomb)
u## [1] 47.67692## [1] 17.7756HistogramaNormal <- hist(mpgcompuestocomb, freq = FALSE,
main = "Gráfica No.83: Histograma
de Composite Combined MPG con Curva Normal",
xlab = "Composite Combined MPG",
ylab = "Densidad",
col = "#FFFACD")
x <- seq(min(mpgcompuestocomb), max(mpgcompuestocomb), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80## [1] 20 30 40 50 60 70 80 90P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(mpgcompuestocomb)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 24.42201## [1] 11.0705## [1] FALSEModelo Log-Normal:
## [1] 3.793167## [1] 0.3887003HistogramaLogNormal <- hist(mpgcompuestocomb, freq = FALSE,
main = "Gráfica No.84: Histograma
de Composite Combined MPG con Curva Log-Normal",
xlab = "Composite Combined MPG",
ylab = "Densidad",
col = "#FFFACD")
x <- seq(min(mpgcompuestocomb), max(mpgcompuestocomb), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 10 20 30 40 50 60 70 80## [1] 20 30 40 50 60 70 80 90Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(mpgcompuestocomb)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 26.49009## [1] 11.0705## [1] FALSE45 Range (FT1)
# Limpieza de datos
alcance <- datos$Range..FT1.
alcance <- as.numeric(alcance)
alcance <- alcance[!is.na(alcance) & alcance > 0]
u <- mean(alcance)
u## [1] 134.6015## [1] 84.03164HistogramaNormal <- hist(alcance, freq = FALSE,
main = "Gráfica No.85: Histograma
de Range (FT1) con Curva Normal",
xlab = "Range (FT1)",
ylab = "Densidad",
col = "#B4EEB4")
x <- seq(min(alcance), max(alcance), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300## [1] 50 100 150 200 250 300 350P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(alcance)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 136.8458## [1] 9.487729## [1] FALSEModelo Log-Normal:
## [1] 4.714095## [1] 0.6135099HistogramaLogNormal <- hist(alcance, freq = FALSE,
main = "Gráfica No.86: Histograma
de Range (FT1) con Curva Log-Normal",
xlab = "Range (FT1)",
ylab = "Densidad",
col = "#B4EEB4")
x <- seq(min(alcance), max(alcance), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300## [1] 50 100 150 200 250 300 350Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(alcance)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 94.64621## [1] 9.487729## [1] FALSE46 City Range (FT1)
# Limpieza de datos
alcancecity <- datos$City.Range..FT1.
alcancecity <- as.numeric(alcancecity)
alcancecity <- alcancecity[!is.na(alcancecity) & alcancecity > 0]
u <- mean(alcancecity)
u## [1] 153.2607## [1] 80.26296HistogramaNormal <- hist(alcancecity, freq = FALSE,
main = "Gráfica No.87: Histograma
de City Range (FT1) con Curva Normal",
xlab = "City Range (FT1)",
ylab = "Densidad",
col = "#EEAD0E")
x <- seq(min(alcancecity), max(alcancecity), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300## [1] 50 100 150 200 250 300 350P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(alcancecity)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 101.805## [1] 9.487729## [1] FALSEModelo Log-Normal:
## [1] 4.891591## [1] 0.5356542HistogramaLogNormal <- hist(alcancecity, freq = FALSE,
main = "Gráfica No.88: Histograma
de City Range (FT1) con Curva Log-Normal",
xlab = "City Range (FT1)",
ylab = "Densidad",
col = "#EEAD0E")
x <- seq(min(alcancecity), max(alcancecity), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300## [1] 50 100 150 200 250 300 350Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(alcancecity)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
df_log <- sum(valores_validos_log) - 1 - 2
Vc_log <- qchisq(0.95, df = df_log)
Vc_log## [1] 9.487729## [1] FALSE47 Highway Range (FT1)
# Limpieza de datos
autopista <- datos$Highway.Range..FT1.
autopista <- as.numeric(autopista)
autopista <- autopista[!is.na(autopista) & autopista > 0]
u <- mean(autopista)
u## [1] 150.7251## [1] 92.89936# Histograma y curva normal
HistogramaNormal <- hist(autopista, freq = FALSE,
main = "Gráfica No.89: Histograma
de Highway Range (FT1) con Curva Normal",
xlab = "Highway Range (FT1)",
ylab = "Densidad",
col = "#8B7355")
x <- seq(min(autopista), max(autopista), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300## [1] 50 100 150 200 250 300 350P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(autopista)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 149.6588## [1] 9.487729## [1] FALSEModelo Log-Normal:
## [1] 4.816218## [1] 0.6403917HistogramaLogNormal <- hist(autopista, freq = FALSE,
main = "Gráfica No.90: Histograma
de Highway Range (FT1) con Curva Log-Normal",
xlab = "Highway Range (FT1)",
ylab = "Densidad",
col = "#8B7355")
x <- seq(min(autopista), max(autopista), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300## [1] 50 100 150 200 250 300 350Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(autopista)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 111.4179## [1] 9.487729## [1] FALSE48 Range (FT2)
## Warning: NAs introducidos por coerción## [1] 282.4438## [1] 79.10371HistogramaNormal <- hist(alcance2, freq = FALSE,
main = "Gráfica No.91: Histograma
de Range (FT2) con Curva Normal",
xlab = "Range (FT2)",
ylab = "Densidad",
col = "#C1CDCD")
x <- seq(min(alcance2), max(alcance2), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300 350 400## [1] 50 100 150 200 250 300 350 400 450P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(alcance2)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 2496.866## [1] 12.59159## [1] FALSEModelo Log-Normal:
## [1] 5.528548## [1] 0.660844HistogramaLogNormal <- hist(alcance2, freq = FALSE,
main = "Gráfica No.92: Histograma
de Range (FT2) con Curva Log-Normal",
xlab = "Range (FT2)",
ylab = "Densidad",
col = "#C1CDCD")
x <- seq(min(alcance2), max(alcance2), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 50 100 150 200 250 300 350 400## [1] 50 100 150 200 250 300 350 400 450Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(alcance2)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 2443.823## [1] 12.59159## [1] FALSE49 City Range (FT2)
# Limpieza de datos
rangocity <- datos$City.Range..FT2.
rangocity <- as.numeric(rangocity)
rangocity <- rangocity[!is.na(rangocity) & rangocity > 0]
lambda <- 1 / mean(rangocity)
lambda## [1] 0.03878411HistExp <- hist(rangocity, freq = FALSE,
main = "Gráfica No.93: Histograma
de City Range (FT2) con Curva Exponencial",
xlab = "City Range (FT2)",
ylab = "Densidad",
col = "darkolivegreen1")
x <- seq(min(rangocity), max(rangocity), 0.01)
lines(x, dexp(x, rate = lambda), col = "red", lwd = 2)
Test:
## [1] 10 20 30 40 50 60 70 80 90 100## [1] 20 30 40 50 60 70 80 90 100 110Pexp <- numeric(length(FO))
for (k in 1:length(FO)) {
Pexp[k] <- pexp(limsup[k], rate = lambda) - pexp(liminf[k], rate = lambda)
}
FEexp <- Pexp * length(rangocity)
valores_validos <- FEexp > 0
X2_exp <- sum((FO[valores_validos] - FEexp[valores_validos])^2 / FEexp[valores_validos])
X2_exp## [1] 40.79176## [1] 15.50731## [1] FALSE50 Highway Range (FT2)
# Limpieza de datos
autoalcance <- datos$Highway.Range..FT2.
autoalcance <- as.numeric(autoalcance)
autoalcance <- autoalcance[!is.na(autoalcance) & autoalcance > 0]
u <- mean(autoalcance)
u## [1] 23.48428## [1] 15.96889HistogramaNormal <- hist(autoalcance, freq = FALSE,
main = "Gráfica No.94: Histograma
de Highway Range (FT2) con Curva Normal",
xlab = "Highway Range (FT2)",
ylab = "Densidad",
col = "#FFD700")
x <- seq(min(autoalcance), max(autoalcance), 0.01)
lines(x, dnorm(x, mean = u, sd = sigma), col = "red", lwd = 2)
Test:
FO <- HistogramaNormal$counts
liminf <- HistogramaNormal$breaks[-length(HistogramaNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90## [1] 10 20 30 40 50 60 70 80 90 100P <- numeric(length(FO))
for (i in 1:length(FO)) {
P[i] <- pnorm(limsup[i], mean = u, sd = sigma) - pnorm(liminf[i], mean = u, sd = sigma)
}
FE <- P * length(autoalcance)
valores_validos <- FE > 0
X2_normal <- sum((FO[valores_validos] - FE[valores_validos])^2 / FE[valores_validos])
X2_normal## [1] 1117.535## [1] 14.06714## [1] FALSEModelo Log-Normal:
## [1] 2.995425## [1] 0.5334381HistogramaLogNormal <- hist(autoalcance, freq = FALSE,
main = "Gráfica No.95: Histograma
de Highway Range (FT2) con Curva Log-Normal",
xlab = "Highway Range (FT2)",
ylab = "Densidad",
col = "#FFD700")
x <- seq(min(autoalcance), max(autoalcance), 0.01)
lines(x, dlnorm(x, meanlog = ulog, sdlog = sigmalog), col = "red", lwd = 2)
Test:
FO <- HistogramaLogNormal$counts
liminf <- HistogramaLogNormal$breaks[-length(HistogramaLogNormal$breaks)]
liminf## [1] 0 10 20 30 40 50 60 70 80 90## [1] 10 20 30 40 50 60 70 80 90 100Plog <- numeric(length(FO))
for (j in 1:length(FO)) {
Plog[j] <- plnorm(limsup[j], meanlog = ulog, sdlog = sigmalog) -
plnorm(liminf[j], meanlog = ulog, sdlog = sigmalog)
}
FElog <- Plog * length(autoalcance)
valores_validos_log <- FElog > 0
X2_log <- sum((FO[valores_validos_log] - FElog[valores_validos_log])^2 / FElog[valores_validos_log])
X2_log## [1] 40.30501## [1] 14.06714## [1] FALSE