library(stats) 
library( psych ) 
library(readxl) 
library(MASS) 
library(ISLR)

base=read_xlsx("mlp.xlsx") 

describe(base)

##   vars  n mean   sd median trimmed  mad  min  max range  skew kurtosis
## z    1 39 1.43 0.76   1.74    1.46 0.59 0.07 2.48  2.41 -0.50    -1.33
## y    2 39 0.41 0.50   0.00    0.39 0.00 0.00 1.00  1.00  0.35    -1.92
##     se
## z 0.12
## y 0.08

modelolp <- lm(formula =y ~ ., data = base ) 
 
summary(modelolp) 

## 
## Call:
## lm(formula = y ~ ., data = base)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.93878 -0.18488 -0.06348  0.13711  0.77831 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.02465    0.13128   7.805 2.47e-09 ***
## z           -0.42868    0.08109  -5.286 5.81e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3812 on 37 degrees of freedom
## Multiple R-squared:  0.4303, Adjusted R-squared:  0.4149 
## F-statistic: 27.94 on 1 and 37 DF,  p-value: 5.809e-06

solicitud=data.frame(z=1.9) 

predict(modelolp,solicitud) 

##         1 
## 0.2101575