PRACTICA ll
Yohana Argueta
28 de marzo de 2019
EJEMPLO DE REGRESION
Datos
## Warning: package 'readr' was built under R version 3.5.3
## Warning: package 'dplyr' was built under R version 3.5.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Parsed with column specification:
## cols(
## X1 = col_double(),
## X2 = col_double(),
## Y = col_double()
## )
## # A tibble: 6 x 3
## X1 X2 Y
## <dbl> <dbl> <dbl>
## 1 3.92 7298 0.75
## 2 3.61 6855 0.71
## 3 3.32 6636 0.66
## 4 3.07 6506 0.61
## 5 3.06 6450 0.7
## 6 3.11 6402 0.72
Modelo de Regresion Multiple
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
##
## Call:
## lm(formula = Y ~ X1 + X2, data = ejemplo_regresion)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085090 -0.039102 -0.003341 0.030236 0.105692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.56449677 0.07939598 19.705 0.00000000000000182 ***
## X1 0.23719747 0.05555937 4.269 0.000313 ***
## X2 -0.00024908 0.00003205 -7.772 0.00000009508790794 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0533 on 22 degrees of freedom
## Multiple R-squared: 0.8653, Adjusted R-squared: 0.8531
## F-statistic: 70.66 on 2 and 22 DF, p-value: 0.000000000265
##
## regresion multiple
## ===============================================
## Dependent variable:
## ---------------------------
## Y
## -----------------------------------------------
## X1 0.23719750***
## (0.05555937)
##
## X2 -0.00024908***
## (0.00003205)
##
## Constant 1.56449700***
## (0.07939598)
##
## -----------------------------------------------
## Observations 25
## R2 0.86529610
## Adjusted R2 0.85305030
## Residual Std. Error 0.05330222 (df = 22)
## F Statistic 70.66057000*** (df = 2; 22)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Objetos dentro del modelo
Vector de coeficientes estimados
## (Intercept) X1 X2
## 1.5644967711 0.2371974748 -0.0002490793
Matriz de varianza-covarianza de los parametros
## (Intercept) X1 X2
## (Intercept) 0.0063037218732 0.000240996434 -0.000000982806321
## X1 0.0002409964344 0.003086843196 -0.000001675537651
## X2 -0.0000009828063 -0.000001675538 0.000000001027106
Intervalos de confianza
## 2.5 % 97.5 %
## (Intercept) 1.3998395835 1.7291539588
## X1 0.1219744012 0.3524205485
## X2 -0.0003155438 -0.0001826148
Valores ajustados
## [,1]
## 1 0.6765303
## 2 0.7133412
## 3 0.6991023
## 4 0.6721832
## 5 0.6837597
## 6 0.7075753
## 7 0.7397638
## 8 0.7585979
## 9 0.7943078
## 10 0.7935605
## 11 0.7984347
## 12 0.8272778
## 13 0.8021665
## 14 0.7992462
## 15 0.7544349
## 16 0.7339716
## 17 0.7048866
## 18 0.6930338
## 19 0.6350898
## 20 0.6127185
## 21 0.5701215
## 22 0.4796371
## 23 0.4374811
## 24 0.3953981
## 25 0.3773799
Residuos
## [,1]
## 1 0.073469743
## 2 -0.003341163
## 3 -0.039102258
## 4 -0.062183196
## 5 0.016240338
## 6 0.012424659
## 7 0.030236216
## 8 -0.018597878
## 9 0.105692240
## 10 0.026439478
## 11 -0.048434733
## 12 -0.057277771
## 13 -0.022166535
## 14 0.040753758
## 15 0.035565142
## 16 -0.033971640
## 17 -0.024886579
## 18 0.026966239
## 19 -0.085089833
## 20 0.017281530
## 21 -0.010121525
## 22 -0.069637086
## 23 0.072518915
## 24 0.074601871
## 25 -0.057379932
EJERCICIO PRACTICA II
Datos
## Parsed with column specification:
## cols(
## Y = col_double(),
## x1 = col_double(),
## x2 = col_double()
## )
## # A tibble: 14 x 3
## Y x1 x2
## <dbl> <dbl> <dbl>
## 1 320 50 7.4
## 2 450 53 5.1
## 3 370 60 4.2
## 4 470 63 3.9
## 5 420 69 1.4
## 6 500 82 2.2
## 7 570 100 7
## 8 640 104 5.7
## 9 670 113 13.1
## 10 780 130 16.4
## 11 690 150 5.1
## 12 700 181 2.9
## 13 910 202 4.5
## 14 930 217 6.2
## # A tibble: 14 x 4
## Y x1 x2 x3
## <dbl> <dbl> <dbl> <dbl>
## 1 320 50 7.4 370
## 2 450 53 5.1 270.
## 3 370 60 4.2 252
## 4 470 63 3.9 246.
## 5 420 69 1.4 96.6
## 6 500 82 2.2 180.
## 7 570 100 7 700
## 8 640 104 5.7 593.
## 9 670 113 13.1 1480.
## 10 780 130 16.4 2132
## 11 690 150 5.1 765
## 12 700 181 2.9 525.
## 13 910 202 4.5 909
## 14 930 217 6.2 1345.
Modelo de regresion multiple
##
## Call:
## lm(formula = Y ~ x1 + x2, data = practicayo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -99.635 -15.569 1.323 41.777 68.843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 187.0587 38.3192 4.882 0.000486 ***
## x1 3.1297 0.2668 11.731 0.000000147 ***
## x2 10.2828 3.6579 2.811 0.016934 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53.78 on 11 degrees of freedom
## Multiple R-squared: 0.9332, Adjusted R-squared: 0.921
## F-statistic: 76.79 on 2 and 11 DF, p-value: 0.0000003449
##
## Modelo de Regresion Lineal
## ===============================================
## Dependent variable:
## ---------------------------
## Y
## -----------------------------------------------
## x1 3.12967900***
## (0.26678150)
##
## x2 10.28276000**
## (3.65785100)
##
## Constant 187.05870000***
## (38.31922000)
##
## -----------------------------------------------
## Observations 14
## R2 0.93316020
## Adjusted R2 0.92100750
## Residual Std. Error 53.77889000 (df = 11)
## F Statistic 76.78626000*** (df = 2; 11)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Objetos dentro del modelo de regresion
Vector de Coeficientes estimados
## (Intercept) x1 x2
## 187.058732 3.129679 10.282761
Matriz de Varianza Covarianza
## (Intercept) x1 x2
## (Intercept) 1468.362494 -7.41376385 -70.45407922
## x1 -7.413764 0.07117239 -0.09674089
## x2 -70.454079 -0.09674089 13.37987393
Intervalos de Confianza
## 2.5 % 97.5 %
## (Intercept) 102.718701 271.398763
## x1 2.542497 3.716861
## x2 2.231885 18.333636
Valores Ajustados
## [,1]
## 1 419.6351
## 2 405.3738
## 3 418.0271
## 4 424.3313
## 5 417.4025
## 6 466.3145
## 7 572.0060
## 8 571.1571
## 9 675.4166
## 10 762.5543
## 11 708.9527
## 12 783.3506
## 13 865.5263
## 14 929.9522
Residuos del Modelo
## [,1]
## 1 -99.63511326
## 2 44.62619914
## 3 -48.02706958
## 4 45.66872148
## 5 2.59754889
## 6 33.68551274
## 7 -2.00596133
## 8 68.84291136
## 9 -5.41662903
## 10 17.44571696
## 11 -18.95266838
## 12 -83.35064535
## 13 44.47367760
## 14 0.04779875