I have a large dataset (500000 data, V1 column include all the data).
x <- read.csv("mydata.csv", header=F) hist(x)
Looking at the data, I believe it is not a normal distribution. As a further check, I constructed a qqplot:
x_norm <- (x$V1 - mean(x$V1))/sd(x$V1) qqnorm(x_norm); abline(0, 1)
To check the goodness of fit of x$V1 (rawdata) to a normal distribution, I used:
rnorm <- rnorm(500000, mean(x$V1), sd(x$V1)) cc <- cbind(rnorm, x$V1) g <- goodfit(cc, method="MinChisq") summary(g) Goodness-of-fit test for poisson distribution X^2 df P(> X^2) Pearson 914.5227 17 1.679266e-183 Warning message: In summary.goodfit(g) : Chi-squared approximation may be incorrect
Does this seem correct? Can I confidently conclude my dataset
X$V1 is or is not a normal distribution?
Based on the above analysis, what other distribution should I test?