<<echo=FALSE, results=hide>>=
d <- data.frame(x = runif(100, -1, 1))
a <- 0
b <- sample(c(-1, 1), 1) * sample(c(0, 0.6, 0.9), 1)
d$y <- a + b * d$x + rnorm(100, sd = 0.25)
write.csv(d, "regression.csv", row.names = FALSE, quote = FALSE)

m <- lm(y ~ x, data = d)
bhat <- coef(m)[2]
bpvl <- summary(m)$coefficients[2, 4]
bsol <- c(bpvl >= 0.05, (bpvl < 0.05) & (bhat > 0), (bpvl < 0.05) & (bhat < 0))
@    

\begin{question}
Using the data provided in \url{regression.csv} estimate a linear regression of
\texttt{y} on \texttt{x} and answer the following questions.

\begin{answerlist}
  \item \texttt{x} and \texttt{y} are not significantly correlated
  \item \texttt{y} increases significantly with \texttt{x}
  \item \texttt{y} decreases significantly with \texttt{x}
  \item Estimated slope with respect to \texttt{x}:
\end{answerlist}
 
\end{question}

\begin{solution}
<<echo=FALSE, results=hide, fig=TRUE>>=
plot(y ~ x, data = d)
abline(m)
legend(if(bhat > 0) "topleft" else "topright", bty = "n",
  paste0("b = ", fmt(bhat, 3), "\np = ", fmt(bpvl, 3)))
@ 

To replicate the analysis in R:
\begin{verbatim}
## data
d <- read.csv("regression.csv")
## regression
m <- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)
\end{verbatim}

\end{solution}

%% \exname{Linear regression}
%% \extype{cloze}
%% \exsolution{\Sexpr{mchoice2string(bsol)}|\Sexpr{fmt(bhat, 3)}}
%% \exclozetype{schoice|num}
%% \extol{0.01}