#Data input
price <- c(100,110,120,130,140,150,160,170,180,190)
marketing_cost <- c(20,25,22,30,35,40,42,45,50,55)
stores <- c(50,52,55,58,60,62,65,67,70,72)
income <- c(5000,5200,5400,5600,5800,6000,6200,6400,6600,6800)
sales_volume <- c(200,220,210,230,240,250,260,270,280,290)
#Membuat Data Frame
data <- data.frame(price, marketing_cost,stores,income,sales_volume)
#Model Regresi Linear Berganda
model <- lm(sales_volume ~ price + marketing_cost + stores + income, data=data)
#Ringkasan Model
summary(model)
##
## Call:
## lm(formula = sales_volume ~ price + marketing_cost + stores +
## income, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5778 -1.5081 -0.0229 1.4289 4.4748
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 172.6154 83.1662 2.076 0.0832 .
## price 0.5164 0.9521 0.542 0.6071
## marketing_cost 1.9689 0.5688 3.462 0.0134 *
## stores -1.2139 3.4233 -0.355 0.7350
## income NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.059 on 6 degrees of freedom
## Multiple R-squared: 0.9932, Adjusted R-squared: 0.9898
## F-statistic: 291.8 on 3 and 6 DF, p-value: 6.882e-07
#Pair plot
pairs(data, main = "Scatterplot Matrix", pch=19)
#Residual Plot
plot(model$residuals, main="Residual Plot", ylab="Residuals", xlab="Fitted Values")
abline(h = 0, col="red", lwd=2)
#Data Baru untuk Prediksi
new_data <- data.frame(price=175, marketing_cost=48, stores=68, income=6500)
#Prediksi Volume Penjualan
predicted_sales <- predict(model, newdata=new_data)
print(predicted_sales)
## 1
## 274.9569