library(readxl) Concrete_Data <- read_excel(“Concrete_Data.xls”) View(Concrete_Data) datos <- read_excel(“Concrete_Data.xls”)
Variables:
Variables predictoras (X) Cement (kg/m³) Blast Furnace Slag (kg/m³) Fly Ash (kg/m³) Water (kg/m³) Superplasticizer (kg/m³) Coarse Aggregate (kg/m³) Fine Aggregate (kg/m³) Age (days) Cement (kg/m³) Blast Furnace Slag (kg/m³) Fly Ash (kg/m³) Water (kg/m³) Superplasticizer (kg/m³) Coarse Aggregate (kg/m³) Fine Aggregate (kg/m³)
Variable Respuesta (Y): Concrete compressive strength (MPa)
#¿Cómo predecir la resistencia a la compresión del concreto en función de sus componentes y el tiempo de curado?
Este problema se puede abordar mediante un modelo de regresión lineal múltiple, donde la variable respuesta es continua. #Talque: Y=(X1,X2,…,X8)+Error #Donde Y es resistencia a la compresion #X1 -> X8 Variables predictoras #e-> Error Aleatorio
#3) Modelo # Ajustar el modelo modelo <- lm(
Concrete compressive strength(MPa, megapascals)~ ., data = datos)
Ajustar el modelo
modelo <- lm(
Concrete compressive strength(MPa, megapascals)~ ., data = datos)Ver resumen del modelo
summary(modelo)
Call: lm(formula =
Concrete compressive strength(MPa, megapascals) ~ ., data =
datos)
Residuals: Min 1Q Median 3Q Max -28.653 -6.303 0.704 6.562 34.446
Coefficients: Estimate (Intercept) -23.163756
Cement (component 1)(kg in a m^3 mixture) 0.119785
Blast Furnace Slag (component 2)(kg in a m^3 mixture)
0.103847 Fly Ash (component 3)(kg in a m^3 mixture)
0.087943 Water (component 4)(kg in a m^3 mixture)
-0.150298
Superplasticizer (component 5)(kg in a m^3 mixture)
0.290687
Coarse Aggregate (component 6)(kg in a m^3 mixture)
0.018030 Fine Aggregate (component 7)(kg in a m^3 mixture)
0.020154 Age (day) 0.114226 Std. Error (Intercept)
26.588421 Cement (component 1)(kg in a m^3 mixture)
0.008489
Blast Furnace Slag (component 2)(kg in a m^3 mixture)
0.010136 Fly Ash (component 3)(kg in a m^3 mixture)
0.012585 Water (component 4)(kg in a m^3 mixture) 0.040179
Superplasticizer (component 5)(kg in a m^3 mixture)
0.093460
Coarse Aggregate (component 6)(kg in a m^3 mixture)
0.009394 Fine Aggregate (component 7)(kg in a m^3 mixture)
0.010703 Age (day) 0.005427 t value Pr(>|t|) (Intercept)
-0.871 0.383851 Cement (component 1)(kg in a m^3 mixture)
14.110 < 2e-16
Blast Furnace Slag (component 2)(kg in a m^3 mixture)
10.245 < 2e-16
Fly Ash (component 3)(kg in a m^3 mixture) 6.988 5.03e-12
Water (component 4)(kg in a m^3 mixture) -3.741 0.000194
Superplasticizer (component 5)(kg in a m^3 mixture) 3.110
0.001921
Coarse Aggregate (component 6)(kg in a m^3 mixture) 1.919
0.055227 Fine Aggregate (component 7)(kg in a m^3 mixture)
1.883 0.059968 Age (day) 21.046 < 2e-16
(Intercept)
Cement (component 1)(kg in a m^3 mixture)
Blast Furnace Slag (component 2)(kg in a m^3 mixture)
Fly Ash (component 3)(kg in a m^3 mixture)
Water (component 4)(kg in a m^3 mixture)
Superplasticizer (component 5)(kg in a m^3 mixture) **
Coarse Aggregate (component 6)(kg in a m^3 mixture)
.
Fine Aggregate (component 7)(kg in a m^3 mixture) .
Age (day) *** — Signif. codes: 0 ‘’ 0.001
‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 10.4 on 1021 degrees of freedom Multiple R-squared: 0.6155, Adjusted R-squared: 0.6125 F-statistic: 204.3 on 8 and 1021 DF, p-value: < 2.2e-16
#GRAFICOS # Gráficos de diagnóstico del modelo par(mfrow = c(2, 2)) plot(modelo)
#CONCLUSIONES Las variables con mayor influencia pueden identificarse observando los coeficientes más significativos (valores p bajos). #CONCLUSIONES #Las variables con mayor influencia pueden identificarse observando los coeficientes más significativos (valores p bajos).
#El valor de R-squared nos indica cuánta variabilidad explica el modelo.
#El modelo permite predecir la resistencia del concreto según sus ingredientes y edad, útil para optimizar mezclas y materiales en ingeniería civil.