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# Load the necessary library
library(ggplot2)

# Multiple linear regression model: Predicting Petal Length based on Petal Width and Sepal Length
model <- lm(Petal.Length ~ Petal.Width + Sepal.Length, data = iris)

# Summary of the model to check coefficients, R-squared, and other statistics
summary(model)
## 
## Call:
## lm(formula = Petal.Length ~ Petal.Width + Sepal.Length, data = iris)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.15506 -0.21920 -0.02115  0.25986  1.35204 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.50714    0.33696  -4.473 1.54e-05 ***
## Petal.Width   1.74810    0.07533  23.205  < 2e-16 ***
## Sepal.Length  0.54226    0.06934   7.820 9.41e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4032 on 147 degrees of freedom
## Multiple R-squared:  0.9485, Adjusted R-squared:  0.9478 
## F-statistic:  1354 on 2 and 147 DF,  p-value: < 2.2e-16
# Load necessary libraries
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(ggplot2)

# Multiple linear regression model: Predicting Petal Length based on Petal Width and Sepal Length
model <- lm(Petal.Length ~ Petal.Width + Sepal.Length, data = iris)

# Create a grid of values for Petal Width and Sepal Length
petal.width.range <- seq(min(iris$Petal.Width), max(iris$Petal.Width), length.out = 100)
sepal.length.range <- seq(min(iris$Sepal.Length), max(iris$Sepal.Length), length.out = 100)
grid <- expand.grid(Petal.Width = petal.width.range, Sepal.Length = sepal.length.range)

# Predict Petal Length for the grid
grid$Petal.Length <- predict(model, newdata = grid)

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