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I think the Shapiro-Wilk test is a great way to see if a variable is normally distributed. This is an important assumption in creating any sort of model and also evaluating models.

Let’s look at how to do this in R!

shapiro.test(data$CreditScore)

And here is the output:

Shapiro-Wilk normality test
data: data$CreditScore
W = 0.96945, p-value = 0.2198

So how do we read this? It looks like the p-value is too high. But it is not. The data is normal if the p-value is above 0.05. So we now know our variable is normally distributed.

Let’s make a histogram to take a look using base R graphics:

hist(data$CreditScore, main="Credit Score", xlab="Credit Score", border="light blue", col="blue", las=1, breaks=5)

It does look normal from our distribution here:

Great! Now we can make assumptions and perform more tests on our credit scores.

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