Lab1

#install.packages("tidyverse")
library(tidyverse)

Primera parte ejercicio manual Pagina 1

Pagina 2

Ejercicio 2 problema 1

y <- c(250, 220, 200, 350, 210, 205,285,190)
x1 <- c(76,61,50,94,55,61,80,52)
x2 <- c(80,72,70,122,75,95,120,68)
x3 <- c(13.5,12.1,11.6,12.5,13.5,14,12.5,14.5)
misDatos <- data.frame(x1,x2,x3,y)

x1_y

x1_y<-misDatos %>% select(`x1`, `y`)
ggplot(x1_y, aes(x=`x1`, y=`y`)) + geom_point() + geom_smooth()  + geom_smooth(method = "lm", col = "red")

x2_y

x2_y<-misDatos %>% select(`x2`, `y`)
ggplot(x2_y, aes(x=`x2`, y=`y`)) + geom_point() + geom_smooth()  + geom_smooth(method = "lm", col = "red")

x3_y

x3_y<-misDatos %>% select(`x3`, `y`)
ggplot(x3_y, aes(x=`x3`, y=`y`)) + geom_point() + geom_smooth()  + geom_smooth(method = "lm", col = "red")

pregunta 2 mostrar resumen:

Resumen general de todos los datos

summary(misDatos)

       x1              x2               x3              y        
 Min.   :50.00   Min.   : 68.00   Min.   :11.60   Min.   :190.0  
 1st Qu.:54.25   1st Qu.: 71.50   1st Qu.:12.40   1st Qu.:203.8  
 Median :61.00   Median : 77.50   Median :13.00   Median :215.0  
 Mean   :66.12   Mean   : 87.75   Mean   :13.03   Mean   :238.8  
 3rd Qu.:77.00   3rd Qu.:101.25   3rd Qu.:13.62   3rd Qu.:258.8  
 Max.   :94.00   Max.   :122.00   Max.   :14.50   Max.   :350.0  

Resumen de la modelo linea simple por variable

fit1 <- lm(data = x1_y, `y`~`x1`)
fit2 <- lm(data = x2_y, `y`~`x2`)
fit3 <- lm(data = x3_y, `y`~`x3`)
summary(fit1)

Call:
lm(formula = y ~ x1, data = x1_y)

Residuals:
     Min       1Q   Median       3Q      Max 
-22.1306  -5.1759  -0.8275  10.5814  17.0238 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  15.2268    24.8824   0.612    0.563    
x1            3.3803     0.3675   9.199  9.3e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.16 on 6 degrees of freedom
Multiple R-squared:  0.9338,    Adjusted R-squared:  0.9228 
F-statistic: 84.63 on 1 and 6 DF,  p-value: 9.303e-05

summary(fit2)

Call:
lm(formula = y ~ x2, data = x2_y)

Residuals:
   Min     1Q Median     3Q    Max 
-49.09 -10.72  -1.48  17.85  38.77 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  53.0637    46.2261   1.148  0.29470   
x2            2.1161     0.5127   4.128  0.00616 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 30.08 on 6 degrees of freedom
Multiple R-squared:  0.7396,    Adjusted R-squared:  0.6961 
F-statistic: 17.04 on 1 and 6 DF,  p-value: 0.006164

summary(fit3)

Call:
lm(formula = y ~ x3, data = x3_y)

Residuals:
   Min     1Q Median     3Q    Max 
-63.79 -25.88 -18.51  23.95 102.03 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   467.59     275.05   1.700    0.140
x3            -17.57      21.06  -0.834    0.436

Residual standard error: 55.79 on 6 degrees of freedom
Multiple R-squared:  0.1039,    Adjusted R-squared:  -0.04543 
F-statistic: 0.6958 on 1 and 6 DF,  p-value: 0.4361

Problema 3,4: calculo de la correlacion y su grafica

library(corrplot)
correlacion<-round(cor(x = misDatos , method = "pearson"), 3)
correlacion

       x1     x2     x3      y
x1  1.000  0.858 -0.189  0.966
x2  0.858  1.000 -0.173  0.860
x3 -0.189 -0.173  1.000 -0.322
y   0.966  0.860 -0.322  1.000

library(corrplot)
corrplot(correlacion, method="number", type="upper")