Update inference-one-mean.qmd (#502)

some typos.

Author: gungorMetehan

inference-one-mean.qmd

@@ -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")
@@ -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.
@@ -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]
 :::
 
@@ -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%