NUMERAL 1

library(stargazer)
## 
## 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
load("C:/Users/Logistica4sv/Desktop/ALAN HERNANDEZ/UES 2021/ECONOMETRIA/Guia de trabajo 1/datos_ventas.RData")
modelo_ventas<-lm(formula = ventas~tv+radio+periodico,data = datos_ventas)
stargazer(modelo_ventas,title = "Ecuación de Ventas",type = 'html')
## 
## <table style="text-align:center"><caption><strong>Ecuación de Ventas</strong></caption>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>ventas</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">tv</td><td>0.045</td></tr>
## <tr><td style="text-align:left"></td><td>(0.118)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">radio</td><td>-3.450<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.206)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">periodico</td><td>18.485<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.563)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-33.289<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(7.172)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>200</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.847</td></tr>
## <tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.844</td></tr>
## <tr><td style="text-align:left">Residual Std. Error</td><td>33.875 (df = 196)</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>360.758<sup>***</sup> (df = 3; 196)</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>

NUMERAL 2

matriz_x<-model.matrix(modelo_ventas)
matriz_M<-diag(200)-matriz_x%*%solve(t(matriz_x)%*%matriz_x)%*%t(matriz_x)

head(matriz_M%*%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  45.16139

NUMERAL 3

confint(modelo_ventas,parm = "tv",level = 0.968)
##         1.6 %    98.4 %
## tv -0.2097376 0.2998052