Name : Farhan Dzaffa Arfianto
NIM : 220605110099
Class : C
Institue : Maulana Malik Ibrahim Islamic State University of
Malang
Lecture : Prof. Dr. Suhartono, M.Kom
library(dplyr)
##
## 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
library(ggplot2)
X <- c(1, 2, 3, 4, 5)
Y <- c(3, 5, 7, 9, 11)
data <- data.frame(X, Y)
model <- lm(Y ~ X, data = data)
summary(model)
## Warning in summary.lm(model): essentially perfect fit: summary may be
## unreliable
##
## Call:
## lm(formula = Y ~ X, data = data)
##
## Residuals:
## 1 2 3 4 5
## 1.403e-15 -1.892e-15 -4.904e-17 1.631e-16 3.753e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000e+00 1.448e-15 6.905e+14 <2e-16 ***
## X 2.000e+00 4.366e-16 4.581e+15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381e-15 on 3 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 2.098e+31 on 1 and 3 DF, p-value: < 2.2e-16
ggplot(data, aes(X, Y)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Variabel X", y = "Variabel Y", title = "Regresi Linear")
## `geom_smooth()` using formula = 'y ~ x'
new_X <- data.frame(X = 6)
predicted_Y <- predict(model, newdata = new_X)
print(predicted_Y)
## 1
## 13