Merge branch 'data-8:main' into main

data-8 · Jun 9, 2022 · ca0ed10 · ca0ed10
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diff --git a/_config.yml b/_config.yml
@@ -1,7 +1,7 @@
 #######################################################################################
 # Book settings
 title: Computational and Inferential Thinking
-author: Ani Adhikari and John DeNero
+author: Ani Adhikari and John DeNero and David Wagner
 email: ''
 logo                        : "favicon.png"  # A path to the book logo
 exclude_patterns            : ["README.md"]  # Patterns to skip when building the book. Can be glob-style (e.g. "*skip.ipynb")

diff --git a/_toc.yml b/_toc.yml
@@ -126,11 +126,13 @@ sections:
     title: Sampling from a Population
   - file: chapters/10/3/Empirical_Distribution_of_a_Statistic
     title: Empirical Distibution of a Statistic
+  - file: chapters/10/4/Random_Sampling_in_Python
+    title: Random Sampling in Python
 - file: chapters/11/Testing_Hypotheses
   title: Testing Hypotheses
   sections:
-  - file: chapters/11/1/Assessing_Models
-    title: Assessing Models
+  - file: chapters/11/1/Assessing_a_Model
+    title: Assessing a Model
   - file: chapters/11/2/Multiple_Categories
     title: Multiple Categories
   - file: chapters/11/3/Decisions_and_Uncertainty
@@ -142,10 +144,10 @@ sections:
   sections:
   - file: chapters/12/1/AB_Testing
     title: A/B Testing
-  - file: chapters/12/2/Deflategate
-    title: Deflategate
-  - file: chapters/12/3/Causality
+  - file: chapters/12/2/Causality
     title: Causality
+  - file: chapters/12/3/Deflategate
+    title: Deflategate
 - file: chapters/13/Estimation
   title: Estimation
   sections:

diff --git a/assets/data/family_heights.csv b/assets/data/family_heights.csv
diff --git a/assets/data/san_francisco_2019.csv b/assets/data/san_francisco_2019.csv
diff --git a/chapters/08/1/Applying_a_Function_to_a_Column.ipynb b/chapters/08/1/Applying_a_Function_to_a_Column.ipynb
diff --git a/chapters/09/3/Simulation.ipynb b/chapters/09/3/Simulation.ipynb
diff --git a/chapters/09/4/Monty_Hall_Problem.ipynb b/chapters/09/4/Monty_Hall_Problem.ipynb
diff --git a/chapters/09/5/Finding_Probabilities.ipynb b/chapters/09/5/Finding_Probabilities.ipynb
diff --git a/chapters/10/1/Empirical_Distributions.ipynb b/chapters/10/1/Empirical_Distributions.ipynb
diff --git a/chapters/10/2/Sampling_from_a_Population.ipynb b/chapters/10/2/Sampling_from_a_Population.ipynb
@@ -30,7 +30,7 @@
     "\n",
     "As an example, we will study a population of flight delay times. The table `united` contains data for United Airlines domestic flights departing from San Francisco in the summer of 2015. The data are made publicly available by the [Bureau of Transportation Statistics](http://www.transtats.bts.gov/Fields.asp?Table_ID=293) in the United States Department of Transportation.\n",
     "\n",
-    "There are 13,825 rows, each corresponding to a flight. The columns are the date of the flight, the flight number, the destination airport code, and the departure delay time in minutes. Some delay times are negative; those flights left early."
+    "There are 13,825 rows, each corresponding to a flight. The columns are the date of the flight, the flight number, the destination airport code, and the departure delay time in minutes. Some delay times are negative: those flights left early."
    ]
   },
   {
@@ -373,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,

diff --git a/chapters/10/3/Empirical_Distribution_of_a_Statistic.ipynb b/chapters/10/3/Empirical_Distribution_of_a_Statistic.ipynb
@@ -266,7 +266,7 @@
     "\n",
     "**Step 1: Decide which statistic to simulate.** We have already decided that: we are going to simulate the median of a random sample of size 1000 drawn from the population of flight delays.\n",
     "\n",
-    "**Step 2: Write the code to generate one value of the statistic.** Draw a random sample of size 1000 and compute the median of the sample. We did this in the code cell above. Here it is again, encapsulated in a function."
+    "**Step 2: Define a function that returns one simulated value of the statistic.** Draw a random sample of size 1000 and compute the median of the sample. We did this in the code cell above. Here it is again, encapsulated in a function."
    ]
   },
   {
@@ -285,7 +285,7 @@
    "source": [
     "**Step 3: Decide how many simulated values to generate.** Let's do 5,000 repetitions.\n",
     "\n",
-    "**Step 4: Write the code to generate an array of simulated values.** As in all simulations, we start by creating an empty array in which we will collect our results. We will then set up a `for` loop for generating all the simulated values. The body of the loop will consist of generating one simulated value of the sample median, and appending it to our collection array.\n",
+    "**Step 4: Use a `for` loop to generate an array of simulated values.** As usual, we will start by creating an empty array in which to collect our results. We will then set up a `for` loop for generating all the simulated values. The body of the loop will consist of generating one simulated value of the sample median, and appending it to our collection array.\n",
     "\n",
     "The simulation takes a noticeable amount of time to run. That is because it is performing 5000 repetitions of the process of drawing a sample of size 1000 and computing its median. That's a lot of sampling and repeating!"
    ]
@@ -463,7 +463,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,