Para una empresa se ha estimado un modelo que relaciona las ventas de 200 empresas, con su gasto en tv, radio, periódicos y la interacción entre tv y periódicos
options(scipen = 99999999)
load("C:/Users/DELL/Desktop/Programacion-en-R/Econometria/Guia_1_Ejercicios/modelo_ventas.RData")
Matriz_x_1 <- model.matrix(modelo_ventas)
Matriz_xx_1 <- t(Matriz_x_1)%*%Matriz_x_1options(scipen = 99999999)
Matriz_a_1 <- solve(Matriz_xx_1)%*%t(Matriz_x_1)
Matriz_a_1[1:4,1:4]## 1 2 3 4
## (Intercept) -0.01128647020 0.01410377973 0.0350639188 0.0004283381
## tv -0.00006704103 0.00003094914 -0.0006120193 -0.0002120642
## periodico 0.00139818182 -0.00190724690 -0.0025468816 0.0002293243
## radio -0.00058002134 0.00064866654 -0.0001093284 -0.0001899710Matriz_p_1 <- Matriz_x_1%*%Matriz_a_1
Matriz_p_1[1:4,1:4]## 1 2 3 4
## 1 0.03181459 0.00370346 0.01758786 0.02250872
## 2 0.00370346 0.02460480 0.03447285 0.01212022
## 3 0.01758786 0.03447285 0.06766822 0.02641047
## 4 0.02250872 0.01212022 0.02641047 0.02031981n <- nrow(Matriz_x_1)
Matriz_m_1 <- diag(n)- Matriz_p_1
Matriz_m_1[1:4,1:4]## 1 2 3 4
## 1 0.96818541 -0.00370346 -0.01758786 -0.02250872
## 2 -0.00370346 0.97539520 -0.03447285 -0.01212022
## 3 -0.01758786 -0.03447285 0.93233178 -0.02641047
## 4 -0.02250872 -0.01212022 -0.02641047 0.97968019library(magrittr)
Reciduos_Medelo_Ventas <- modelo_ventas$residuals
Datos_Modelos <- modelo_ventas$model
Reciduos_Matrices <- Matriz_m_1%*%Datos_Modelos$ventas
Comparacion <- cbind(Reciduos_Matrices, Reciduos_Medelo_Ventas, Reciduos_Medelo_Ventas - Reciduos_Matrices) %>% round(digits = 2) %>% as.data.frame()
names(Comparacion) <- c("Por_Matrices","En_modelo","Diferencias")
head(Comparacion, n= 10)## Por_Matrices En_modelo Diferencias
## 1 -15.93 -15.93 0
## 2 19.33 19.33 0
## 3 38.02 38.02 0
## 4 -15.43 -15.43 0
## 5 5.16 5.16 0
## 6 80.22 80.22 0
## 7 -16.35 -16.35 0
## 8 -22.89 -22.89 0
## 9 -34.40 -34.40 0
## 10 46.09 46.09 0Descomposicion <- eigen(x=Matriz_xx_1, symmetric = TRUE)
Auto_Valores <- (Descomposicion$values)
print(Auto_Valores)## [1] 311421698.6388 70252.5341 40973.4590 3714.3627 12.7735print(Auto_Valores>0)## [1] TRUE TRUE TRUE TRUE TRUEPara una empresa se desea estimar un modelo que relaciona el tiempo (en minutos) en acomodar cajas en una bodega, en función de la distancia (en metros) y del número de cajas nota: las cajas son todas iguales. Los datos se encuentra en “datos_cajas.RData”
options(scipen = 99999999)
load("C:/Users/DELL/Desktop/Programacion-en-R/Econometria/Guia_1_Ejercicios/datos_cajas.RData")
Modelo_cajas <- lm(formula = Tiempo~Distancia+N_cajas,data = datos_cajas)
summary(Modelo_cajas)##
## Call:
## lm(formula = Tiempo ~ Distancia + N_cajas, data = datos_cajas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.2716 -0.5405 0.5212 1.4051 2.9381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3112 5.8573 0.395 0.70007
## Distancia 0.4559 0.1468 3.107 0.00908 **
## N_cajas 0.8772 0.1530 5.732 0.0000943 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.141 on 12 degrees of freedom
## Multiple R-squared: 0.7368, Adjusted R-squared: 0.6929
## F-statistic: 16.8 on 2 and 12 DF, p-value: 0.0003325library(magrittr)
Matriz_x_2 <- model.matrix(Modelo_cajas)
Matriz_xx_2 <- t(Matriz_x_2)%*%Matriz_x_2
Matriz_a_2 <- solve(Matriz_xx_2)%*%t(Matriz_x_2)
print(Matriz_a_2)## 1 2 3 4 5
## (Intercept) 0.459747079 0.505626389 -0.317731768 0.707001469 0.053149816
## Distancia -0.003015297 -0.009318829 0.018819615 -0.019989342 -0.006641453
## N_cajas -0.017147338 -0.009890695 -0.007919488 -0.004479623 0.011082085
## 6 7 8 9 10
## (Intercept) -0.166576988 0.633594572 -0.125532551 0.1260628274 -0.90735239
## Distancia 0.006550474 -0.009903692 0.009409808 0.0003379213 0.02334256
## N_cajas 0.002768355 -0.016090251 -0.003959744 -0.0038254420 0.01780152
## 11 12 13 14 15
## (Intercept) 0.277217608 0.368482344 0.487274665 -0.3674581822 -0.73350489
## Distancia -0.011931220 -0.007473259 -0.006797416 0.0001559637 0.01645417
## N_cajas 0.006862401 -0.005142468 -0.012793352 0.0238754370 0.01885861Matriz_p_2 <- Matriz_x_2%*%Matriz_a_2
Matriz_p_2## 1 2 3 4 5 6
## 1 0.19781478 0.127154573 0.16766180 0.062524965 -0.03527291 0.057620774
## 2 0.12715457 0.124295239 0.03396629 0.140073563 0.05334477 0.038710181
## 3 0.16766180 0.033966286 0.35585795 -0.137368460 -0.10168744 0.123125512
## 4 0.06252497 0.140073563 -0.13736846 0.257600846 0.15524536 0.006698639
## 5 -0.03527291 0.053344771 -0.10168744 0.155245361 0.18408997 0.046742309
## 6 0.05762077 0.038710181 0.12312551 0.006698639 0.04674231 0.086318088
## 7 0.17558129 0.144648497 0.07654437 0.133523089 0.01345706 0.036955589
## 8 0.11716423 0.050316476 0.21126231 -0.035350897 -0.01751039 0.094896089
## 9 0.09794605 0.077129229 0.10132526 0.055636570 0.03786105 0.067680430
## 10 -0.02906036 -0.056765574 0.20436525 -0.131155907 0.05122193 0.136694350
## 11 -0.01209498 0.081873124 -0.13140718 0.199703669 0.18629079 0.030873007
## 12 0.09285990 0.104513848 0.01812731 0.131114317 0.07550894 0.044246890
## 13 0.15541865 0.125438973 0.08744449 0.109054124 0.01789770 0.046274418
## 14 -0.12402490 -0.005427535 -0.12246527 0.112857904 0.23285894 0.067134558
## 15 -0.05129385 -0.039271650 0.11324781 -0.060157783 0.09995191 0.116029165
## 7 8 9 10 11 12
## 1 0.17558129 0.11716423 0.09794605 -0.02906036 -0.01209498 0.092859897
## 2 0.14464850 0.05031648 0.07712923 -0.05676557 0.08187312 0.104513848
## 3 0.07654437 0.21126231 0.10132526 0.20436525 -0.13140718 0.018127310
## 4 0.13352309 -0.03535090 0.05563657 -0.13115591 0.19970367 0.131114317
## 5 0.01345706 -0.01751039 0.03786105 0.05122193 0.18629079 0.075508940
## 6 0.03695559 0.09489609 0.06768043 0.13669435 0.03087301 0.044246890
## 7 0.18301556 0.07160552 0.08894348 -0.08682757 0.04935470 0.112467995
## 8 0.07160552 0.13896449 0.08399596 0.13551596 -0.03237026 0.042396988
## 9 0.08894348 0.08399596 0.07465547 0.05440619 0.04101064 0.069478345
## 10 -0.08682757 0.13551596 0.05440619 0.34795579 -0.01326471 -0.021162536
## 11 0.04935470 -0.03237026 0.04101064 -0.01326471 0.20329083 0.095597926
## 12 0.11246799 0.04239699 0.06947834 -0.02116254 0.09559793 0.094228911
## 13 0.15702161 0.07705558 0.08545596 -0.04568349 0.04428588 0.099852268
## 14 -0.07689788 -0.02789930 0.01907176 0.16357209 0.20867158 0.042323339
## 15 -0.07939330 0.08995724 0.04540362 0.29018859 0.04818497 -0.001554438
## 13 14 15
## 1 0.15541865 -0.124024902 -0.051293849
## 2 0.12543897 -0.005427535 -0.039271650
## 3 0.08744449 -0.122465266 0.113247813
## 4 0.10905412 0.112857904 -0.060157783
## 5 0.01789770 0.232858944 0.099951911
## 6 0.04627442 0.067134558 0.116029165
## 7 0.15702161 -0.076897883 -0.079393301
## 8 0.07705558 -0.027899299 0.089957240
## 9 0.08545596 0.019071756 0.045403621
## 10 -0.04568349 0.163572088 0.290188586
## 11 0.04428588 0.208671580 0.048184973
## 12 0.09985227 0.042323339 -0.001554438
## 13 0.13743085 -0.052866482 -0.044080529
## 14 -0.05286648 0.352392093 0.210699107
## 15 -0.04408053 0.210699107 0.262089133n_2 <- nrow(Matriz_x_2)
Matriz_m_2 <- diag(n_2) - Matriz_p_2
print(Matriz_m_2)## 1 2 3 4 5 6
## 1 0.80218522 -0.127154573 -0.16766180 -0.062524965 0.03527291 -0.057620774
## 2 -0.12715457 0.875704761 -0.03396629 -0.140073563 -0.05334477 -0.038710181
## 3 -0.16766180 -0.033966286 0.64414205 0.137368460 0.10168744 -0.123125512
## 4 -0.06252497 -0.140073563 0.13736846 0.742399154 -0.15524536 -0.006698639
## 5 0.03527291 -0.053344771 0.10168744 -0.155245361 0.81591003 -0.046742309
## 6 -0.05762077 -0.038710181 -0.12312551 -0.006698639 -0.04674231 0.913681912
## 7 -0.17558129 -0.144648497 -0.07654437 -0.133523089 -0.01345706 -0.036955589
## 8 -0.11716423 -0.050316476 -0.21126231 0.035350897 0.01751039 -0.094896089
## 9 -0.09794605 -0.077129229 -0.10132526 -0.055636570 -0.03786105 -0.067680430
## 10 0.02906036 0.056765574 -0.20436525 0.131155907 -0.05122193 -0.136694350
## 11 0.01209498 -0.081873124 0.13140718 -0.199703669 -0.18629079 -0.030873007
## 12 -0.09285990 -0.104513848 -0.01812731 -0.131114317 -0.07550894 -0.044246890
## 13 -0.15541865 -0.125438973 -0.08744449 -0.109054124 -0.01789770 -0.046274418
## 14 0.12402490 0.005427535 0.12246527 -0.112857904 -0.23285894 -0.067134558
## 15 0.05129385 0.039271650 -0.11324781 0.060157783 -0.09995191 -0.116029165
## 7 8 9 10 11 12
## 1 -0.17558129 -0.11716423 -0.09794605 0.02906036 0.01209498 -0.092859897
## 2 -0.14464850 -0.05031648 -0.07712923 0.05676557 -0.08187312 -0.104513848
## 3 -0.07654437 -0.21126231 -0.10132526 -0.20436525 0.13140718 -0.018127310
## 4 -0.13352309 0.03535090 -0.05563657 0.13115591 -0.19970367 -0.131114317
## 5 -0.01345706 0.01751039 -0.03786105 -0.05122193 -0.18629079 -0.075508940
## 6 -0.03695559 -0.09489609 -0.06768043 -0.13669435 -0.03087301 -0.044246890
## 7 0.81698444 -0.07160552 -0.08894348 0.08682757 -0.04935470 -0.112467995
## 8 -0.07160552 0.86103551 -0.08399596 -0.13551596 0.03237026 -0.042396988
## 9 -0.08894348 -0.08399596 0.92534453 -0.05440619 -0.04101064 -0.069478345
## 10 0.08682757 -0.13551596 -0.05440619 0.65204421 0.01326471 0.021162536
## 11 -0.04935470 0.03237026 -0.04101064 0.01326471 0.79670917 -0.095597926
## 12 -0.11246799 -0.04239699 -0.06947834 0.02116254 -0.09559793 0.905771089
## 13 -0.15702161 -0.07705558 -0.08545596 0.04568349 -0.04428588 -0.099852268
## 14 0.07689788 0.02789930 -0.01907176 -0.16357209 -0.20867158 -0.042323339
## 15 0.07939330 -0.08995724 -0.04540362 -0.29018859 -0.04818497 0.001554438
## 13 14 15
## 1 -0.15541865 0.124024902 0.051293849
## 2 -0.12543897 0.005427535 0.039271650
## 3 -0.08744449 0.122465266 -0.113247813
## 4 -0.10905412 -0.112857904 0.060157783
## 5 -0.01789770 -0.232858944 -0.099951911
## 6 -0.04627442 -0.067134558 -0.116029165
## 7 -0.15702161 0.076897883 0.079393301
## 8 -0.07705558 0.027899299 -0.089957240
## 9 -0.08545596 -0.019071756 -0.045403621
## 10 0.04568349 -0.163572088 -0.290188586
## 11 -0.04428588 -0.208671580 -0.048184973
## 12 -0.09985227 -0.042323339 0.001554438
## 13 0.86256915 0.052866482 0.044080529
## 14 0.05286648 0.647607907 -0.210699107
## 15 0.04408053 -0.210699107 0.737910867Reciduos_Medelo_Cajas <- Modelo_cajas$residuals
Datos_Modelos <- Modelo_cajas$model
Reciduos_Matrices_2 <- Matriz_m_2%*%datos_cajas$Tiempo
Comparacion_2 <- cbind(Reciduos_Matrices_2,Reciduos_Medelo_Cajas, Reciduos_Medelo_Cajas - Reciduos_Matrices_2) %>% round(digits = 2) %>% as.data.frame()
names(Comparacion_2) <- c("Por_Matrices","En_modelo","Diferencias")
head(Comparacion_2, n= 10)## Por_Matrices En_modelo Diferencias
## 1 -0.76 -0.76 0
## 2 0.13 0.13 0
## 3 -0.32 -0.32 0
## 4 2.94 2.94 0
## 5 -9.27 -9.27 0
## 6 0.77 0.77 0
## 7 1.31 1.31 0
## 8 -2.09 -2.09 0
## 9 1.43 1.43 0
## 10 0.52 0.52 0Descomposicion_2 <- eigen(x=Matriz_xx_2, symmetric = TRUE)
Auto_Valores_2 <- (Descomposicion_2$values)
print(Auto_Valores_2)## [1] 16976.7781334 709.9345923 0.2872743print(Auto_Valores_2>0)## [1] TRUE TRUE TRUEPara los EEUU se ha estimado un modelo que relaciona el “número de crímenes” en un estado con el “Nivel de pobreza” y la cantidad de solteros en el mismo. Dicho modelo se encuentra en “modelo_estimado.RData”
options(scipen = 99999999)
load("C:/Users/DELL/Desktop/Programacion-en-R/Econometria/Guia_1_Ejercicios/modelo_estimado.RData")
Matriz_x_3 <- model.matrix(modelo_estimado_1)
Matriz_xx_3 <- t(Matriz_x_3)%*%Matriz_x_3Matriz_a_3 <- solve(Matriz_xx_3)%*%t(Matriz_x_3)
Matriz_a_3[,1:4]## 1 2 3 4
## (Intercept) -0.12023796 0.007496216 0.043732382 -0.019624196
## poverty -0.01182361 0.003994776 0.008825494 0.000303668
## single 0.02723384 -0.003960021 -0.013241432 0.003081729Matriz_p_3 <- Matriz_x_3%*%Matriz_a_3
Matriz_p_3[1:4,1:4]## 1 2 3 4
## 1 0.16161108 -0.01277963 -0.06530809 0.02720791
## 2 -0.01277963 0.03146508 0.04501952 0.02109951
## 3 -0.06530809 0.04501952 0.07855895 0.01942366
## 4 0.02720791 0.02109951 0.01942366 0.02234121n_3 <- nrow(Matriz_x_3)
Matriz_m_3 <- diag(n_3) - Matriz_p_3
Matriz_m_3[1:4,1:4]## 1 2 3 4
## 1 0.83838892 0.01277963 0.06530809 -0.02720791
## 2 0.01277963 0.96853492 -0.04501952 -0.02109951
## 3 0.06530809 -0.04501952 0.92144105 -0.01942366
## 4 -0.02720791 -0.02109951 -0.01942366 0.97765879Reciduos_Medelo_Estimado <- modelo_estimado_1$residuals
Datos_Modelo_Estimado <- modelo_estimado_1$model
Reciduos_Matrices_3 <- Matriz_m_3%*%Datos_Modelo_Estimado$crime
Comparacion_3 <- cbind(Reciduos_Matrices_3,Reciduos_Medelo_Estimado, Reciduos_Medelo_Estimado - Reciduos_Matrices_3) %>% round(digits = 2) %>% as.data.frame()
names(Comparacion_3) <- c("Por_Matrices","En_modelo","Diferencias")
head(Comparacion_3, n= 10)## Por_Matrices En_modelo Diferencias
## 1 -311.71 -311.71 0
## 2 116.80 116.80 0
## 3 45.25 45.25 0
## 4 -34.45 -34.45 0
## 5 243.00 243.00 0
## 6 -145.12 -145.12 0
## 7 86.13 86.13 0
## 8 88.31 88.31 0
## 9 689.82 689.82 0
## 10 -163.29 -163.29 0Descomposicion_3 <- eigen(x=Matriz_xx_3, symmetric = TRUE)
Auto_Valores_3 <- (Descomposicion_3$values)
print(Auto_Valores_3)## [1] 17956.580914 279.157317 1.681762print(Auto_Valores_3>0)## [1] TRUE TRUE TRUEDentro del archivo “Investiment_Equation.xlsx” se encuentran datos para estimar una función de inversión, para un país, y contiene las siguientes variables:
library(readxl)
library(stargazer)
Investiment_Equation <- read_excel("C:/Users/DELL/Desktop/Programacion-en-R/Econometria/Guia_1_Ejercicios/Data/Investiment_Equation.xlsx")
Ecuacion_Inversion <- lm(formula = InvReal~Trend-Inflation+PNBr+Interest, data = Investiment_Equation)
stargazer(Ecuacion_Inversion, title = "Ecuación de Inversión", type = "html")| Dependent variable: | |
| InvReal | |
| Trend | -0.016*** |
| (0.002) | |
| PNBr | 0.665*** |
| (0.052) | |
| Interest | -0.239** |
| (0.102) | |
| Constant | -0.503*** |
| (0.052) | |
| Observations | 15 |
| R2 | 0.973 |
| Adjusted R2 | 0.966 |
| Residual Std. Error | 0.006 (df = 11) |
| F Statistic | 132.127*** (df = 3; 11) |
| Note: | p<0.1; p<0.05; p<0.01 |
Matriz_x_4 <- model.matrix(Ecuacion_Inversion)
n <- nrow(Matriz_x_4)
Matriz_m_4 <- diag(n) - Matriz_x_4 %*% solve(t(Matriz_x_4)%*%Matriz_x_4) %*% t(Matriz_x_4)
Y <- Investiment_Equation$InvReal
Residuos <- Matriz_m_4%*%Y
print(Residuos)## [,1]
## 1 -0.0100797395
## 2 -0.0009395423
## 3 0.0029590400
## 4 0.0078509093
## 5 0.0027863240
## 6 0.0006169256
## 7 0.0076387748
## 8 -0.0054613455
## 9 -0.0037407700
## 10 0.0006919072
## 11 0.0020015571
## 12 -0.0001177806
## 13 -0.0101857096
## 14 0.0068691861
## 15 -0.0008897366confint(object = Ecuacion_Inversion, parm = "PNBr", level = .93)## 3.5 % 96.5 %
## PNBr 0.5610827 0.7680701Dentro del archivo “consumption_equation.RData” se encuentran objetos relacionados a una función de consumo, que se construyó usando las variables:
options(scipen = 99999999)
load("C:/Users/DELL/Desktop/Programacion-en-R/Econometria/Guia_1_Ejercicios/consumption_equation.RData")
n_5 <- nrow(P)
Matriz_m_5 <- diag(n_5) - P
Residuos_5 <- Matriz_m_5%*%C
print(Residuos_5)## [,1]
## 1 -5.859103
## 2 2.605057
## 3 45.765735
## 4 31.102448
## 5 -21.037889
## 6 7.008120
## 7 17.859663
## 8 10.705631
## 9 22.002328
## 10 -2.689665
## 11 7.784083
## 12 -13.127696
## 13 17.521565
## 14 17.304695
## 15 -16.308260
## 16 -5.255508
## 17 2.788211
## 18 -16.379339
## 19 -14.327554
## 20 11.749135
## 21 -31.424669
## 22 -23.329596
## 23 22.171806
## 24 -5.040038
## 25 -36.191398
## 26 -25.211753
## 27 -21.411271
## 28 1.410519
## 29 -24.229564
## 30 20.971808
## 31 43.342653
## 32 36.808458
## 33 17.882297
## 34 -33.100273
## 35 -37.819995
## 36 -49.370820
## 37 23.456143
## 38 -25.510341
## 39 -11.960629
## 40 -9.234201
## 41 21.949616
## 42 3.211123
## 43 -14.511436
## 44 3.197576
## 45 -62.396763
## 46 -66.854500
## 47 8.330745
## 48 91.963380
## 49 61.620735
## 50 48.148861
## 51 -10.717721
## 52 -84.069717
## 53 -56.426627
## 54 125.113605k <- 4
Varianza_Error <- t(Residuos_5)%*%Residuos_5/(n_5-k)
print(Varianza_Error)## [,1]
## [1,] 1428.746Varianza_Error <- as.vector(Varianza_Error)
Varianza_Covarianza <- Varianza_Error*solve(XX)
print(Varianza_Covarianza)## (Intercept) Yd W I
## (Intercept) 164.522304918 -0.09333539523 0.009670913575 10.5186890800
## Yd -0.093335395 0.00018911268 -0.000032769561 -0.0072901023
## W 0.009670914 -0.00003276956 0.000006165749 0.0004193421
## I 10.518689080 -0.00729010228 0.000419342092 5.3203789879C_Estimada <- P%*%C
Cuadro <- as.data.frame(cbind(C, C_Estimada, Residuos_5))
names(Cuadro) <- c("C", "C_Estimada", "Residuos")
print(Cuadro)## C C_Estimada Residuos
## 1 976.4 982.2591 -5.859103
## 2 998.1 995.4949 2.605057
## 3 1025.3 979.5343 45.765735
## 4 1090.9 1059.7976 31.102448
## 5 1107.1 1128.1379 -21.037889
## 6 1142.4 1135.3919 7.008120
## 7 1197.2 1179.3403 17.859663
## 8 1221.9 1211.1944 10.705631
## 9 1310.4 1288.3977 22.002328
## 10 1348.8 1351.4897 -2.689665
## 11 1381.8 1374.0159 7.784083
## 12 1393.0 1406.1277 -13.127696
## 13 1470.7 1453.1784 17.521565
## 14 1510.8 1493.4953 17.304695
## 15 1541.2 1557.5083 -16.308260
## 16 1617.3 1622.5555 -5.255508
## 17 1684.0 1681.2118 2.788211
## 18 1784.8 1801.1793 -16.379339
## 19 1897.6 1911.9276 -14.327554
## 20 2006.1 1994.3509 11.749135
## 21 2066.2 2097.6247 -31.424669
## 22 2184.2 2207.5296 -23.329596
## 23 2264.8 2242.6282 22.171806
## 24 2317.5 2322.5400 -5.040038
## 25 2405.2 2441.3914 -36.191398
## 26 2550.5 2575.7118 -25.211753
## 27 2675.9 2697.3113 -21.411271
## 28 2653.7 2652.2895 1.410519
## 29 2710.9 2735.1296 -24.229564
## 30 2868.9 2847.9282 20.971808
## 31 2992.1 2948.7573 43.342653
## 32 3124.7 3087.8915 36.808458
## 33 3203.2 3185.3177 17.882297
## 34 3193.0 3226.1003 -33.100273
## 35 3236.0 3273.8200 -37.819995
## 36 3275.5 3324.8708 -49.370820
## 37 3454.3 3430.8439 23.456143
## 38 3640.6 3666.1103 -25.510341
## 39 3820.9 3832.8606 -11.960629
## 40 3981.2 3990.4342 -9.234201
## 41 4113.4 4091.4504 21.949616
## 42 4279.5 4276.2889 3.211123
## 43 4393.7 4408.2114 -14.511436
## 44 4474.5 4471.3024 3.197576
## 45 4466.6 4528.9968 -62.396763
## 46 4594.5 4661.3545 -66.854500
## 47 4748.9 4740.5693 8.330745
## 48 4928.1 4836.1366 91.963380
## 49 5075.6 5013.9793 61.620735
## 50 5237.5 5189.3511 48.148861
## 51 5423.9 5434.6177 -10.717721
## 52 5683.7 5767.7697 -84.069717
## 53 5968.4 6024.8266 -56.426627
## 54 6257.8 6132.6864 125.113605Dentro del archivo “datos_ventas.RData” se encuentran los datos para estimar una función de ventas, para una empresa, y contiene las siguientes variables:
options(scipen = 99999999)
load("C:/Users/DELL/Desktop/Programacion-en-R/Econometria/Guia_1_Ejercicios/datos_ventas.RData")
Ecuacion_Ventas <- lm(formula = ventas~tv+radio+periodico, data = datos_ventas)
stargazer(Ecuacion_Ventas, title = "Ecuación de Ventas", type = "html")| Dependent variable: | |
| ventas | |
| tv | 0.045 |
| (0.118) | |
| radio | -3.450*** |
| (0.206) | |
| periodico | 18.485*** |
| (0.563) | |
| Constant | -33.289*** |
| (7.172) | |
| Observations | 200 |
| R2 | 0.847 |
| Adjusted R2 | 0.844 |
| Residual Std. Error | 33.875 (df = 196) |
| F Statistic | 360.758*** (df = 3; 196) |
| Note: | p<0.1; p<0.05; p<0.01 |
Matriz_x_6 <- model.matrix(Ecuacion_Ventas)
Matriz_a_6 <- t(Matriz_x_6)%*%Matriz_x_6 %>%
solve()%*%t(Matriz_x_6)
n_6 <- nrow(Matriz_x_6)
Matriz_m_6 <- diag(n_6) - Matriz_x_6%*%Matriz_a_6
head(Matriz_m_6%*%modelo_ventas$model$ventas, n=10)## [,1]
## 1 -17.85246
## 2 19.08216
## 3 33.79319
## 4 -17.35090
## 5 10.25721
## 6 74.20385
## 7 -15.24652
## 8 -23.42430
## 9 -39.64052
## 10 45.16139confint(object = Ecuacion_Ventas, parm = "tv", level = .968)## 1.6 % 98.4 %
## tv -0.2097376 0.2998052Indica que el gasto en de publicidad en tv no es significativo, es decir, no hay relación lenal ya que su intervalo incluye el cero.