To add a linear model (for those unfamiliar, see my HPMoR examples) which will really just recapitulate the simple averages calculation:

R> lw2009 <- read.csv("lw-2009.csv")
R> lw2011 <- read.csv("lw-2011.csv")
R> lw2012 <- read.csv("lw-2012.csv")
R>
R> # lwi2009 <- lw2009$IQ[!is.na(lw2009$IQ)]
R> # hand-cleaned:
R> lwi2009 <- c(120,125,128,129,130,130,130,130,130,130,130,130,130,131,132,132,133,134,136,138,138,139,139,140,
R> 140,140,140,140,140,140,140,140,141,142,144,145,145,145,148,148,150,150,150,150,152,154,154,
R> 155,155,155,156,158,158,160,160,160,160,162,163,164,165,166,170,171,173,180)
R> lwi2011 <- lw2011$IQ[!is.na(lw2011$IQ)]
R> lwi2012 <- lw2012$IQ[!is.na(lw2012$IQ)]
R>
R> xs <- c(rep(as.Date("2009-03-01"), length(lwi2009)), rep(as.Date("2011-11-01"), length(lwi2011)), rep(as.Date("2012-11-01"), length(lwi2012)))
R> ys <- c(lwi2009, lwi2011, lwi2012)
R> model <- lm(ys ~ xs)
R> summary(model)
Call:
lm(formula = ys ~ xs)
Residuals:
Min 1Q Median 3Q Max
-38.29 -8.29 -0.29 6.73 63.81
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 219.49064 19.42751 11.30 < 2e-16
xs -0.00519 0.00126 -4.11 4.5e-05
Residual standard error: 12.9 on 741 degrees of freedom
Multiple R-squared: 0.0222, Adjusted R-squared: 0.0209
F-statistic: 16.9 on 1 and 741 DF, p-value: 4.48e-05