```{r setup}
# add libraries that you require here
```
## Exercise 01
Generate 20 samples from a normal distribution, choose mean and standard deviation yourself. Plot histogram and adding a red vertical line to indicate the true mean of the distribution and a blue one for the sample mean. Report number of samples and sample mean in the title.
```{r exercise-01}
```
## Exercise 02
Use `sample()` to resample data from exercise 01 with replacement.
```{r exercise-02}
```
## Exercise 03
Turn code of the previous exercise into a function. Call it to ensure that it works correctly.
```{r exercise-03}
```
## Exercise 04
Use `replicate()` and `sample()` to compute mean for 1000 resampled values. Plot the distribution of _means_ and compute percentile 97% confidence interval via `quantile()`. Put quantitative information into the figure caption.
```{r exercise-04}
```
## Exercise 05
Repeat exercise 04 using for loop. You can copy-paste the plotting part.
```{r exercise-05}
```
## Exercise 06
Repeat exercise 04 using a mapping function from purrr library. You can copy-paste the plotting part.
```{r exercise-06}
```
## Exercise 07
Repeat exercise 04 but using `boot` library.
```{r exercise-07}
```
## Exercise 08
Use your code from the chapter on factors that you created to
1. read in likert-scale.csv file
2. count number of responses per condition and response level and compute proportion of response per condition and level
3. plot these results.
Convert step 2 into a function that takes a table with original data as an input and returns the counts and proportions table. It should have a second parameter `resample` set to `FALSE` by default (we will use it later).
```{r exercise-08}
```
## Exercise 09
Modify your function so that if `resample` parameter is `TRUE`, it samples the _entire_ table with replacement. Test your updated function by calling it with `resample = TRUE` and checking that you get _different_ averages every time.
```{r exercise-09}
```
## Exercise 10
Use map() and list_rbind() to compute counts and proportions of resampled data N time. Start with 2 and without list_rbind() to check whether it works. Up it to 1000 and combine into a single table, once you are confident the code works correctly.
```{r exercise-10}
```
## Exercise 11
Compute lower and upper limits for 97% confidence interval for the proportion of response per condition and level.
```{r exercise-11}
```
## Exercise 12
Add confidence intervals to the plot as either error bars or ribbons.
```{r exercise-12}
```