library(readr)
ejemplo_regresion <- read_csv("C:/Users/Molin/Downloads/ejemplo_regresion.csv")
## Rows: 25 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): X1, X2, Y
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(ejemplo_regresion, n=6)
## # A tibble: 6 × 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
library(stargazer)
## 
## Please cite as:
##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
options(scipen = 9999)
modelo_lineal<-lm(formula = Y~X1+X2, data = ejemplo_regresion)
summary(modelo_lineal)
## 
## 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
stargazer(modelo_lineal, title = "Ejemplo de Regreción Multiple", type = "text", digits = 8)
## 
## Ejemplo de Regreción 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

##Vector de coeficientes estimados B:

options(scipen = 999)
modelo_lineal$coefficients
##   (Intercept)            X1            X2 
##  1.5644967711  0.2371974748 -0.0002490793

##Matriz de Varianza - Covarianza de los parametros V[B]

var_covar<-vcov(modelo_lineal)
print(var_covar)
##                  (Intercept)              X1                 X2
## (Intercept)  0.0063037218732  0.000240996434 -0.000000982806321
## X1           0.0002409964344  0.003086843196 -0.000001675537651
## X2          -0.0000009828063 -0.000001675538  0.000000001027106
confint(object = modelo_lineal,level = .95)
##                     2.5 %        97.5 %
## (Intercept)  1.3998395835  1.7291539588
## X1           0.1219744012  0.3524205485
## X2          -0.0003155438 -0.0001826148
plot(modelo_lineal$fitted.values, main = "Valores Ajustados", ylab = "Y", xlab = "casos")