We fit a linear regression for the economic journals demand model.

```{r regression}
data("Journals", package = "AER")
journals_lm <- lm(log(subs) ~ log(price/citations), data = Journals)
journals_lm
```

A scatter plot with the fitted regression line is shown in the figure below.

```{r visualization, echo=FALSE, fig.height=5, fig.width=6}
plot(log(subs) ~ log(price/citations), data = Journals)
abline(journals_lm)
```

The fitted regression line is

\[
  \log(\mbox{subscriptions}) \quad = \quad
    `r round(coef(journals_lm)[1], digits = 2)`
    `r if(coef(journals_lm)[2] < 0) "-" else "+"`
    `r abs(round(coef(journals_lm)[2], digits = 2))`
    \cdot \log(\mbox{price per citation})
\]

The table below summarizes the results of the model along with a slightly
extended model in type of table commonly used in economics and social science
publications. This can be easily produced with the _modelsummary_ package.

```{r summary-table}
journals_lm2 <- lm(log(subs) ~ log(price/citations) + foundingyear, data = Journals)
library("modelsummary")
msummary(journals_lm2, stars = TRUE)
```