In exams: Automatic Generation of Exams in R

## regression parameters
n <- sample(40:90, 1)
b <- sample(c(-1, 1), 1) * runif(1, 1, 2) * sample(c(0.1, 0.5, 1), 1)
s <- sample(c(0.5, 1, 2), 1)

## data and regression
d <- data.frame(
  x = rnorm(n),
  err = rnorm(n, sd = s)
)
d$y <- 0 + b * d$x + d$err

## different types
type <- sample(c("linear", "semi-logarithmic", "log-log"), 1)
if(type == "linear") {
  m <- lm(y ~ x, data = d)
  xunit <- "unit" 
  yunit <- "units"
  eff <- round(coef(m)[2], digits = 2)
} else if(type == "semi-logarithmic") {
  d$y <- exp(d$y)
  m <- lm(log(y) ~ x, data = d)
  xunit <- "unit" 
  yunit <- "percent"
  eff <- round(100 * exp(coef(m)[2]) - 100, digits = 2)
} else if(type == "log-log") {
  d$y <- exp(d$y)
  d$x <- exp(d$x)
  m <- lm(log(y) ~ log(x), data = d)
  xunit <- "percent" 
  yunit <- "percent"
  eff <- round(100 * exp(0.01 * coef(m)[2]) - 100, digits = 2)
}

## summaries
direct <- if(coef(m)[2] > 0) "increases" else "decreases"
if(summary(m)$coefficients[2, 4] < 0.05) {
  sign1 <- "Also"
  sign2 <- ""
} else {
  sign1 <- "However"
  sign2 <- "_not_"
}

Question

Consider the following regression results:

summary(m)

Describe how the response y depends on the regressor x.

Solution

The presented results describe a r type regression.

The mean of the response y r direct with increasing x.

If x increases by 1 r xunit then a change of y by about r eff r yunit can be expected.

r sign1, the effect of x is r sign2 significant at the 5 percent level.

Meta-information

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