TEST

Author

AS

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Running Code

  • When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:
  • asf
  • das
  • dfas
1 + 1
[1] 2

You can add options to executable code like this

[1] 4

The echo: false option disables the printing of code (only output is displayed).

Now, I am loading my first dataset.

remove(list=ls())
?datasets()

library(help = "datasets")

?Formaldehyde 

df <- Formaldehyde 
require(stats) # Loads the 'stats' package, which contains statistical functions like linear regression (lm).
require(graphics) # Loads the 'graphics' package, which provides plotting functions.

# Create a scatter plot of 'optden' (optical density) against 'carb' (carbohydrate) from the 'Formaldehyde' dataset.
plot(optden ~ carb, data = Formaldehyde,
     xlab = "Carbohydrate (ml)", ylab = "Optical Density", # Set axis labels.
     main = "Formaldehyde data", col = 4, las = 1) # Set the title, color of points (4 is blue), and axis label orientation (las=1 makes labels horizontal).

# Add the linear regression line to the plot.
abline(fm1 <- lm(optden ~ carb, data = Formaldehyde)) # 'lm' fits a linear model, and 'abline' adds the regression line. 'fm1' stores the model.

# Display the summary of the linear regression model.
summary(fm1) # Provides information about the model, such as coefficients, p-values, and R-squared.

Call:
lm(formula = optden ~ carb, data = Formaldehyde)

Residuals:
        1         2         3         4         5         6 
-0.006714  0.001029  0.002771  0.007143  0.007514 -0.011743 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.005086   0.007834   0.649    0.552    
carb        0.876286   0.013535  64.744 3.41e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.008649 on 4 degrees of freedom
Multiple R-squared:  0.999, Adjusted R-squared:  0.9988 
F-statistic:  4192 on 1 and 4 DF,  p-value: 3.409e-07
# Set up a 2x2 grid of plots.
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0)) # 'par' sets or queries graphical parameters. 'mfrow' arranges plots in a grid. 'oma' sets outer margins. 'opar' stores the original parameters.

# Generate diagnostic plots for the linear regression model.
plot(fm1) # Produces four plots: residuals vs. fitted values, Q-Q plot of residuals, scale-location plot, and residuals vs. leverage.

# Restore the original graphical parameters.
par(opar) # Resets the graphical parameters to their original values.