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Trying to compare two distributions

I found this code on the Internet that compares the normal distribution with various student distributions:

x <- seq(-4, 4, length=100)
hx <- dnorm(x)

degf <- c(1, 3, 8, 30)
colors <- c("red", "blue", "darkgreen", "gold", "black")
labels <- c("df=1", "df=3", "df=8", "df=30", "normal")

plot(x, hx, type="l", lty=2, xlab="x value",
  ylab="Density", main="Comparison of t Distributions")

for (i in 1:4){
  lines(x, dt(x,degf[i]), lwd=2, col=colors[i])
}

I would like to adapt this to my situation when I would like to compare my data with a normal distribution. This is my data:

library(quantmod)
getSymbols("^NDX",src="yahoo", from='1997-6-01', to='2012-6-01')
daily<- allReturns(NDX) [,c('daily')]
dailySerieTemporel<-ts(data=daily)
ss<-na.omit(dailySerieTemporel)

The object should see if my data is normal or not ... Can someone help me a little with this? Thank you very much, I really appreciate it!

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2 answers

, , Jarque-Bera. , , . . jarque.bera.test.

 library(tseries)
 jarque.bera.test(ss)

    Jarque Bera Test

data:  ss 
X-squared = 4100.781, df = 2, p-value < 2.2e-16

, , , null 1%.

, , :

 library(fBasics)
 basicStats(ss)
                     ss
nobs        3776.000000
NAs            0.000000
Minimum       -0.105195
Maximum        0.187713
1. Quartile   -0.009417
3. Quartile    0.010220
Mean           0.000462
Median         0.001224
Sum            1.745798
SE Mean        0.000336
LCL Mean      -0.000197
UCL Mean       0.001122
Variance       0.000427
Stdev          0.020671
Skewness       0.322820
Kurtosis       5.060026

, ss , . -.

, , , , , , , , - :

 plot(density(ss, kernel='epanechnikov'))
 set.seed(125)
 lines(density(rnorm(length(ss), mean(ss), sd(ss)), kernel='epanechnikov'), col=2)

enter image description here

, .

, @Alex Reynolds, , , , . , . , , , Jarque-Bera .

+8

Q-Q, - KS, , .

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