Creating a data
frame:
# Assuming 'dat' is your data frame
Week = c(22,24,25,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44)
Plan = c(1.57,1.49,1.50,1.50, 1.50, 1.50, 1.51, 1.41, 1.33, 1.48, 1.50, 1.37, 1.49, 1.43, 1.46, 1.40, 1.37, 1.40, 1.39, 1.42)
Bottles = c(20,18,16,16, 17, 18, 15, 15, 11, 17, 17, 16, 18, 17, 17, 15, 15, 15, 16, 15)
dat <- data.frame(Week,Plan,Bottles)
print(dat)
## Week Plan Bottles
## 1 22 1.57 20
## 2 24 1.49 18
## 3 25 1.50 16
## 4 28 1.50 16
## 5 29 1.50 17
## 6 30 1.50 18
## 7 31 1.51 15
## 8 32 1.41 15
## 9 33 1.33 11
## 10 34 1.48 17
## 11 35 1.50 17
## 12 36 1.37 16
## 13 37 1.49 18
## 14 38 1.43 17
## 15 39 1.46 17
## 16 40 1.40 15
## 17 41 1.37 15
## 18 42 1.40 15
## 19 43 1.39 16
## 20 44 1.42 15
Using Regression
Model:
model <- lm(Bottles ~ Plan, data = dat)
Evaluate the
Model:
predictions <- predict(model, newdata = dat)
r_squared <- 1 - (sum((predictions - dat$Bottles)^2) / sum((mean(dat$Bottles) - dat$Bottles)^2))
print(paste("R-squared (R2) Score:", r_squared))
## [1] "R-squared (R2) Score: 0.584204470572811"
Making
Predictions:
new_production_figures <- data.frame(Plan = c(1.65, 1.75, 1.85,1.95)) # Replace with your desired production figures
new_predictions <- predict(model, newdata = new_production_figures)
Results:
predictions_table <- data.frame(new_production_figures = new_production_figures$Plan, Predicted_Bottles = new_predictions)
print(predictions_table)
## new_production_figures Predicted_Bottles
## 1 1.65 20.66380
## 2 1.75 22.90692
## 3 1.85 25.15004
## 4 1.95 27.39316