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10 changes: 5 additions & 5 deletions inference-one-mean.qmd
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Expand Up @@ -46,14 +46,14 @@ In order to go through the example more clearly, let's say that you are only abl
### Observed data

@fig-5cars shows a (small) random sample of observations from Awesome Auto.
The actual cars as well as their selling price is shown.
The actual cars as well as their selling price are shown.

```{r}
#| label: fig-5cars
#| fig-cap: A sample of five cars from Awesome Auto.
#| out-width: 70%
#| fig-alt: |
#| Photographs of 5 different automobiles. The cars are different color and
#| Photographs of 5 different automobiles. The cars are different colors and
#| different makes and models. On top of the image of each car is its price;
#| the five prices range from 9600 dollars to 27000 dollars.
include_graphics("images/5cars.png")
Expand Down Expand Up @@ -120,7 +120,7 @@ The distribution of $\bar{x}_{bs}$ for the Awesome Auto cars is shown in @fig-bo
```{r}
#| label: fig-bootmeans1mean
#| fig-cap: |
#| Because each of the bootstrap resamples respresents a different set of cars,
#| Because each of the bootstrap resamples represents a different set of cars,
#| the mean of the each bootstrap resample will be a different value. Each of the
#| bootstrapped means is calculated, and a histogram of the values describes the inherent
#| natural variability of the sample mean which is due to the sampling process.
Expand Down Expand Up @@ -247,7 +247,7 @@ terms_chp_19 <- c(terms_chp_19, "SE single mean", "SD of observations")
::: {.guidedpractice data-latex=""}
It turns out that the standard deviation of the bootstrapped means from @fig-carsbsmean is \$2,891.87 (a value which is an excellent approximation for the standard error of sample means if we were to take repeated samples from the population).
(Note: in R the calculation was done using the function `sd()`.)
The average of the observed prices is \$17,140, ad we will consider the sample average to be the best guess point estimate for $\mu.$
The average of the observed prices is \$17,140, and we will consider the sample average to be the best guess point estimate for $\mu.$
Find and interpret the confidence interval for $\mu$ (the true average cost of a car at Awesome Auto) using the bootstrap SE confidence interval formula.[^19-inference-one-mean-2]
:::

Expand Down Expand Up @@ -537,7 +537,7 @@ The extra thick tails of the $t$-distribution are exactly the correction needed
#| Two symmetric bell-shaped curves on top of one another.
#| One is a normal curve with smaller tails and a higher peak in
#| the middle. The other is a t-distribution with longer tails,
#| meaning that there are more more observations far from the
#| meaning that there are more observations far from the
#| center of a t-distribution than of a normal distribution.
#| fig-asp: 0.5
#| out-width: 60%
Expand Down