minor fixes

Author: mca91

02-ch2.Rmd

@@ -399,7 +399,7 @@ g <- function(x) x * f(x)
 h <- function(x) x^2 * f(x)
 ```
 
-Next, we use `r ttcode("integrate()")` and set lower and upper limits of integration to $1$ and $\infty$ using arguments `r ttcode("lower")` and `r ttcode("upper")`. By default, `r ttcode("integrate()")` prints the result along with an estimate of the approximation error to the console. However, the outcome is not a numeric value one can readily do further calculation with. In order to get only a numeric value of the integral, we need to use the `r ttcode("$")` operator in conjunction with `r ttcode("value")`. The `r ttcode("$")` operator is used to extract elements by name from an object of type `r ttcode("list")`. 
+Next, we use `r ttcode("integrate()")` and set lower and upper limits of integration to $1$ and $\infty$ using arguments `r ttcode("lower")` and `r ttcode("upper")`. By default, `r ttcode("integrate()")` prints the result along with an estimate of the approximation error to the console. However, the outcome is not a numeric value one can readily do further calculation with. In order to get only a numeric value of the integral, we need to use the `r ttcode("\\$")` operator in conjunction with `r ttcode("value")`. The `r ttcode("\\$")` operator is used to extract elements by name from an object of type `r ttcode("list")`. 
 
 ```{r, echo = T, eval = T, message = F, warning = F}
 # compute area under the density curve
@@ -613,7 +613,14 @@ By moving the cursor over the plot you can see that the density is rotationally
 
 The normal distribution has some remarkable characteristics. For example, for two jointly normally distribued variables $X$ and $Y$, the conditional expectation function is linear: one can show that $$ E(Y\vert X) = E(Y) + \rho \frac{\sigma_Y}{\sigma_X} (X - E(X)). $$ The application below shows standard bivariate normally distributed sample data along with the conditional expectation function $E(Y\vert X)$ and the marginal densities of $X$ and $Y$. All elements adjust accordingly as you vary the parameters.
 
-<iframe height="880" width="770" frameborder="0" scrolling="no" src="bivariatenormalv4.html"></iframe>
+```{r, echo=FALSE, results='asis'}
+if (my_output=="html"){
+  cat('
+<iframe height="880" width="770" frameborder="0" scrolling="no" src="bivariatenormalv4.html"></iframe>')
+} else {
+  cat("\\begin{center}\\textit{This interactive part of URFITE is only available in the HTML version.}\\end{center}")
+}
+```
 
 <a name="chisquare"></a>
 
@@ -1199,7 +1206,7 @@ Draw a large number of random samples, $10000$ say, of size $n$ from the Bernoul
 
 In `r ttcode("R")`, realize this as follows:
 
-1. We start by defining that the next four subsequently generated figures shall be drawn in a $2\times2$ array such that they can be easily compared. This is done by calling `r ttcode("par(mfrow = c(2, 2))")` before the figures are generated.
+1. We start by defining that the next four subsequently generated figures shall be drawn in a $2\times2$ array such that they can be easily compared. This is done by calling `par(mfrow = c(2, 2))` before generating the figures.
 
 2. We define the number of repetitions `r ttcode("reps")` as $10000$ and create a vector of sample sizes named `r ttcode("sample.sizes")`. We consider samples of sizes $2$, $10$, $50$ and $100$.
 

_bookdown_files/ITER_cache/html/unnamed-chunk-143_bba73a0bf9b38da3d5b3273929eb0b09.RData

@@ -0,0 +1,31 @@
+base
+knitr
+devtools
+readxl
+dplyr
+bindrcpp
+MASS
+kableExtra
+carData
+car
+zoo
+lmtest
+sandwich
+survival
+AER
+scales
+mvtnorm
+stargazer
+Formula
+plm
+tidyr
+np
+rddtools
+dynlm
+urca
+xts
+TTR
+quantmod
+forecast
+orcutt
+nlme