> mdata <-read.table("mm.csv", header=T, sep=",")
> topix <-mdata[,1]
> a     <-mdata[,2] 
> b     <-mdata[,3] 
> topix <-ts(topix)
> a     <-ts(a)
> y <-diff(a)/lag(a, k=-1)*100
> x <-diff(topix)/lag(topix, k=-1)*100
> x <-as.numeric(x)
> y <-as.numeric(y)
> ols_result <-lm(y~x)
> summary(ols_result)

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3307 -0.6262 -0.2243  0.8661  3.1369 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.08355    0.15171   0.551    0.584    
x            1.05237    0.10959   9.603 1.15e-13 ***
---
Signif. codes:  0 �e***�f 0.001 �e**�f 0.01 �e*�f 0.05 �e.�f 0.1 �e �f 1

Residual standard error: 1.182 on 59 degrees of freedom
Multiple R-squared:  0.6098,    Adjusted R-squared:  0.6032 
F-statistic: 92.22 on 1 and 59 DF,  p-value: 1.147e-13

> plot(x,y)
> abline(ols_result)


> mm.conf <- predict(ols_result, interval="confidence", level=0.95)
> mm.conf <- as.data.frame(mm.conf)
> z <- cbind(x,y, mm.conf)
> z <-z[order(z[,1]),]
> matplot(z$x, cbind(z$upr,z$fit,z$lwr), type="l", lty=c(1,1,1), lwd=c(2,2,2), col=c("blue", "black","blue"), xlab="X",ylab="Y")
> points(z$x, z$y)