## 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_" }
Consider the following regression results:
summary(m)
Describe how the response y
depends on the regressor x
.
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.
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