Regresi Linear Bergana dalam Perekonoiman

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)

data <- data.frame(price, marketing_cost, stores, income, sales_volume)
data

##    price marketing_cost stores income sales_volume
## 1    100             20     50   5000          200
## 2    110             25     52   5200          220
## 3    120             22     55   5400          210
## 4    130             30     58   5600          230
## 5    140             35     60   5800          240
## 6    150             40     62   6000          250
## 7    160             42     65   6200          260
## 8    170             45     67   6400          270
## 9    180             50     70   6600          280
## 10   190             55     72   6800          290

model <- lm(sales_volume ~ price + marketing_cost + stores + income, data = data)
model

## 
## Call:
## lm(formula = sales_volume ~ price + marketing_cost + stores + 
##     income, data = data)
## 
## Coefficients:
##    (Intercept)           price  marketing_cost          stores          income  
##       172.6154          0.5164          1.9689         -1.2139              NA

pairs(data, main = "Scatterplot Matrix", pch = 19)

plot(model$residuals,main="Residual Plot", ylab="Residuals",xlab="Fitted Values")
abline(h = 0, col="red", lwd=2)

new_data <- data.frame(price=175, marketing_cost=48, stores=68, income=6500)
predicted_sales <- predict(model, newdata = new_data)
print(predicted_sales)

##        1 
## 274.9569

predicted_sales

##        1 
## 274.9569