library(readxl)
library(readr)
## Warning: package 'readr' was built under R version 4.3.2
data <- read.csv("C:\\Users\\Muhammad Rizqa Salas\\Downloads\\data liver.csv", sep=";")



```r
y<-data$Y
x1<-data$X1
x2<-data$X2
x3<-data$X3
x4<-data$X4 
x5<-data$X5
x6<-data$X6
data<-data.frame(cbind(y,x1,x2,x3,x4,x5,x6))
head(data)
##        y    x1    x2   x3    x4    x5   x6
## 1 158.76 16.36  8.90 3.47  6.02 57.42 1.11
## 2 197.19 26.68 21.22 3.53 12.07 61.38 1.36
## 3 144.73 12.49 16.62 2.00  8.88 67.42 1.47
## 4 140.06  8.45 22.86 6.71  7.46 69.94 1.31
## 5 129.71 10.19 14.23 4.75  2.06 65.68 1.25
## 6 162.59 19.53 17.35 1.95  7.54 59.63 1.14
n<-nrow(data)
n
## [1] 36

[1] 36

p<-ncol(data)
p
## [1] 7

[1] 7

plot(x1,y)

summary(y)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   120.9   143.9   160.7   169.7   191.8   247.4

Min. 1st Qu. Median Mean 3rd Qu. Max.

120.9 143.9 160.7 169.7 191.8 247.4

boxplot(y)

summary(x1)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.550   8.502  12.265  14.680  19.810  35.410
b1<-(sum(x1*y)-sum(x1)*sum(y)/n)/(sum(x1^2)-(sum(x1)^2/n))
b1
## [1] 2.10446
b0<-mean(y)-b1*mean(x1)
b0
## [1] 138.8326
r<-(sum(x1*y)-sum(x1)*sum(y)/n)/
  sqrt((sum(x1^2)-(sum(x1)^2/n))*(sum(y^2)-(sum(y)^2/n)))
Koef_det<-r^2
Koef_det
## [1] 0.2243414
Adj_R2<-1-((1-Koef_det)*(n-1)/(n-1-1))
Adj_R2
## [1] 0.2015279
galat<-y-(b0+b1*x1)
ragam_galat<-sum(galat^2)/(n-2)

se_b1<-sqrt(ragam_galat/sum((x1-mean(x1))^2))
se_b1
## [1] 0.6710916
se_b0<-sqrt(ragam_galat*(1/n+mean(x1)^2/sum((x1-mean(x1))^2)))
se_b0
## [1] 11.0032
t_b0<-b0/se_b0
t_b0
## [1] 12.61748
t_b1<-b1/se_b1
t_b1
## [1] 3.135875
2*pt(-abs(t_b0 ),df<-n-2)
## [1] 2.215576e-14
2*pt(-abs(t_b1 ),df<-n-2)
## [1] 0.00352412
galat<-y-(b0+b1*x1)

JKG <- sum((y - (b0+b1*x1))^2)
JKReg <- sum(((b0+b1*x1)- mean(y))^2)
JKT <- sum((y - mean(y))^2)
JKT <- JKReg+JKG

dbReg<-1
dbg<-n-2
dbt<-n-1

Fhit<-(JKReg/dbReg)/(JKG/dbg)
Fhit
## [1] 9.833715
P.value<-1-pf(Fhit, dbReg, dbg, lower.tail <- F)
P.value
## [1] 0.00352412
model<-lm(y~x1,data<-data)
summary(model)
## 
## Call:
## lm(formula = y ~ x1, data = data <- data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -66.25 -17.37  -4.84  13.90  67.96 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 138.8326    11.0032  12.617 2.22e-14 ***
## x1            2.1045     0.6711   3.136  0.00352 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29.4 on 34 degrees of freedom
## Multiple R-squared:  0.2243, Adjusted R-squared:  0.2015 
## F-statistic: 9.834 on 1 and 34 DF,  p-value: 0.003524
anova(model)
## Analysis of Variance Table
## 
## Response: y
##           Df  Sum Sq Mean Sq F value   Pr(>F)   
## x1         1  8501.9  8501.9  9.8337 0.003524 **
## Residuals 34 29395.4   864.6                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##Nilai Y = 143.8205 + 2.243073X2 menunjukkan bahwa b0, atau konstanta, adalah 143.8205. Ini berarti bahwa jika tidak ada pengaruh dari faktor lain, nilai variabel dependen (y) akan menjadi 143.8205. Nilai b1 menunjukkan bahwa jika x berubah satu unit, perkiraan nilai rata-rata y akan meningkat sebanyak 2.243073 unit.

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