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data('mtcars')
cor.test(mtcars$mpg , mtcars$wt)##
## Pearson's product-moment correlation
##
## data: mtcars$mpg and mtcars$wt
## t = -9.559, df = 30, p-value = 1.294e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.9338264 -0.7440872
## sample estimates:
## cor
## -0.8676594# The more a car weighs, the less MPG it gets
# Load necessary library
library(plotly)## Loading required package: ggplot2##
## 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# Load the mtcars dataset
data(mtcars)
# Perform linear regression
model <- lm(mpg ~ wt, data = mtcars)
# Create a data frame with fitted values for the trend line
trendline_data <- data.frame(wt = mtcars$wt, mpg_fitted = predict(model))
# Create an interactive scatter plot with trend line
plot_ly(data = mtcars, x = ~wt, y = ~mpg, type = 'scatter', mode = 'markers',
marker = list(size = 10, color = 'blue', opacity = 0.7),
name = 'Data Points') %>%
add_trace(data = trendline_data, x = ~wt, y = ~mpg_fitted, type = 'scatter', mode = 'lines',
line = list(color = 'red'), name = 'Trend Line') %>%
layout(title = "MPG vs Weight (wt) in mtcars with Trend Line",
xaxis = list(title = "Weight (wt)"),
yaxis = list(title = "Miles Per Gallon (mpg)"))## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...