# Real life examples of difference between independence and correlation

It is well known that independence of random variables implies zero correlation but zero correlation need not imply independence.

I came across plenty of mathematical examples demonstrating dependence despite zero correlation. Are there any real life examples to support this fact?

• Be careful, only zero correlation and jointly normal variables imply independence. Mar 2, 2016 at 9:06
• @Siddesh "But as volume is not linear function of length they are not correlated." Well, not perfectly correlated. But they would be positively correlated. Mar 2, 2016 at 9:57
• @Siddhesh: that will work only if $E[\mathrm{length}^4]-E[\mathrm{length}]E[\mathrm{length}^3]=0$... Mar 2, 2016 at 11:05
• Feel free to put the comment about the normal distribution back in if you disagree with my edit. But I thought that it would be better removed as (1) it's a distracting side-issue to your main question, (2) it has (I think) already been asked on CV before so would be a duplicate of existing material here, (3) I didn't want it to cause confusion among future readers. I've tried to edit the question in such a way that would increase its chances of being reopened: I think this question is quite distinct from the "mathematical statistics" ones on the same topic. Mar 2, 2016 at 15:53
• I still think this question is really nice, and might attract some further interesting answers if it could be reopened (which might involve some editing to clearly distinguish it from the thread it is currently deemed a duplicate of). I have raised a thread on Meta about what it would take for this question to be reopened. All comments welcome. Mar 4, 2016 at 21:10

Stock returns are a decent real-life example of what you're asking for. There's very close to zero correlation between today's and yesterday's S&P 500 return. However, there is clear dependence: squared returns are positively autocorrelated; periods of high volatility are clustered in time.

R code:

library(ggplot2)
library(grid)
library(quantmod)

symbols   <- new.env()
date_from <- as.Date("1960-01-01")
date_to   <- as.Date("2016-02-01")
getSymbols("^GSPC", env=symbols, src="yahoo", from=date_from, to=date_to)  # S&P500

df <- data.frame(close=as.numeric(symbols$GSPC$GSPC.Close),
date=index(symbols$GSPC)) df$log_return     <- c(NA, diff(log(df$close))) df$log_return_lag <- c(NA, head(df$log_return, nrow(df) - 1)) cor(df$log_return,   df$log_return_lag, use="pairwise.complete.obs") # 0.02 cor(df$log_return^2, df$log_return_lag^2, use="pairwise.complete.obs") # 0.14 acf(df$log_return,     na.action=na.pass)  # Basically zero autocorrelation
acf((df\$log_return^2), na.action=na.pass)  # Squared returns positively autocorrelated

p <- (ggplot(df, aes(x=date, y=log_return)) +
geom_point(alpha=0.5) +
theme_bw() + theme(panel.border=element_blank()))
p
ggsave("log_returns_s&p.png", p, width=10, height=8)


The timeseries of log returns on the S&P 500:

If returns were independent through time (and stationary), it would be very unlikely to see those patterns of clustered volatility, and you wouldn't see autocorrelation in squared log returns.

Another example is the relationship between stress and grades on an exam. The relationship is an inverse U shape and the correlation is very low even though causation seems pretty clear.

• That's a neat example. Do you have data or this just based on introspection / teaching experience? Mar 2, 2016 at 11:51
• I saw a study of this, but I saw it many years ago so I don't have the citation or the actual data. Mar 2, 2016 at 11:52