set seed for all bootsrap examples. But I didn't render the quarto file. Fixes #65 (#123)

Author: aluxh

learn/statistics/infer/index.qmd

@@ -165,6 +165,8 @@ Continuing on with our example above, about the average number of hours worked a
 #| label: "generate-point"
 #| warning: false
 #| message: false
+set.seed(571)
+
 gss %>%
   specify(response = hours) %>%
   hypothesize(null = "point", mu = 40) %>%
@@ -179,6 +181,8 @@ To generate a null distribution for the independence of two variables, we could
 #| label: "generate-permute"
 #| warning: false
 #| message: false
+set.seed(328)
+
 gss %>%
   specify(partyid ~ age) %>%
   hypothesize(null = "independence") %>%
@@ -193,6 +197,8 @@ Depending on whether you're carrying out computation-based inference or theory-b
 #| label: "calculate-point"
 #| warning: false
 #| message: false
+set.seed(265)
+
 gss %>%
   specify(response = hours) %>%
   hypothesize(null = "point", mu = 40) %>%
@@ -206,6 +212,8 @@ The output of `calculate()` here shows us the sample statistic (in this case, th
 #| label: "specify-diff-in-means"
 #| warning: false
 #| message: false
+set.seed(174)
+
 gss %>%
   specify(age ~ college) %>%
   hypothesize(null = "independence") %>%
@@ -227,6 +235,8 @@ point_estimate <- gss %>%
   calculate(stat = "mean")
 
 # generate a null distribution
+set.seed(693)
+
 null_dist <- gss %>%
   specify(response = hours) %>%
   hypothesize(null = "point", mu = 40) %>%
@@ -301,6 +311,8 @@ Generally, to find a null distribution using theory-based methods, use the same
 ```{r}
 #| message: false
 #| warning: false
+set.seed(533)
+
 null_f_distn <- gss %>%
    specify(age ~ partyid) %>%
    hypothesize(null = "independence") %>%