Question

Completing regression modeling can require various steps in order to obtain the best results. The process...

Completing regression modeling can require various steps in order to obtain the best results. The process can involve creating a graph to illustrate the relationship between variables, determining best-fit lines, and also approximating or producing smooth-fit lines to represent the data. What are the necessary commands required to carry out such an analysis in R? Please provide an example to illustrate your assertions.

Consider, the Rest program given below,

# Create the predictor and response variable.

x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131)

y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48)

relation <- lm(y~x)

# Give the chart file a name.

png(file = "linearregression.png")

# Plot the chart.

plot(y,x,col = "blue",main = "Height & Weight Regression",

abline(lm(x~y)),cex = 1.3,pch = 16,xlab = "Weight in Kg",ylab = "Height in cm")

# Save the file.

dev.off()

This will give you plot of x and y as given below and by observing this plot one can easily test the fit.

Thank you.

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