Added missing tex macros to examples/ nbs; fix stats overview sns plots

minireference · Jan 20, 2024 · 2f2a189 · 2f2a189
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Showing 9 changed files with 434 additions and 118 deletions.
diff --git a/examples/ANOVA.ipynb b/examples/ANOVA.ipynb
@@ -5,23 +5,63 @@
    "id": "5b41ceb6-d385-4837-b5e0-5e21e643d6e6",
    "metadata": {},
    "source": [
-    "# Analysis of variance (ANOVA)"
+    "### Analysis of variance (ANOVA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3a91e1db-04f3-4c28-9707-e0a7c6df2d6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6e1aa2b2-ce9b-420f-8813-e0d0f9ac327a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2030c8e7-a0d8-4a4d-b227-a0a81f11e02d",
+   "metadata": {},
+   "source": [
+    "$\\def\\stderr#1{\\mathbf{se}_{#1}}$\n",
+    "$\\def\\stderrhat#1{\\hat{\\mathbf{se}}_{#1}}$\n",
+    "$\\newcommand{\\Mean}{\\textbf{Mean}}$\n",
+    "$\\newcommand{\\Var}{\\textbf{Var}}$\n",
+    "$\\newcommand{\\Std}{\\textbf{Std}}$\n",
+    "$\\newcommand{\\Freq}{\\textbf{Freq}}$\n",
+    "$\\newcommand{\\RelFreq}{\\textbf{RelFreq}}$\n",
+    "$\\newcommand{\\DMeans}{\\textbf{DMeans}}$\n",
+    "$\\newcommand{\\Prop}{\\textbf{Prop}}$\n",
+    "$\\newcommand{\\DProps}{\\textbf{DProps}}$"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "e41da4dc-40bc-4074-b3ca-5f2a2534fdcf",
    "metadata": {},
    "source": [
-    "## Definitions"
+    "#### Definitions"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "442020c3-458a-469b-a82e-c7f3beafb812",
    "metadata": {},
    "source": [
-    "## Formulas"
+    "#### Formulas"
    ]
   },
   {
@@ -39,31 +79,31 @@
    "id": "60311b0a-7c91-4df0-be4a-accc69028d8c",
    "metadata": {},
    "source": [
-    "## Example"
+    "#### Example"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "2edd0ff1-e544-41a3-845a-c0471e345d41",
    "metadata": {},
    "source": [
-    "## Explanations"
+    "#### Explanations"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "bf5f1bcf-d993-47e8-9f1b-52902c63c9e3",
    "metadata": {},
    "source": [
-    "## Discussion"
+    "#### Discussion"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "c6bfa142-4e3f-46a0-bbd9-212692dd1b3d",
    "metadata": {},
    "source": [
-    "### Equivalence between ANOVA and OLS\n",
+    "#### Equivalence between ANOVA and OLS\n",
     "\n",
     "via https://stats.stackexchange.com/questions/175246/why-is-anova-equivalent-to-linear-regression"
    ]

diff --git a/examples/attachments/equiv_test_hypotheses.png b/examples/attachments/equiv_test_hypotheses.png
diff --git a/examples/one_sample_t-test.ipynb b/examples/one_sample_t-test.ipynb
@@ -36,6 +36,23 @@
     "%config InlineBackend.figure_format = 'retina'"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e2969712-ded6-408c-a30a-3948fdb27afb",
+   "metadata": {},
+   "source": [
+    "$\\def\\stderr#1{\\mathbf{se}_{#1}}$\n",
+    "$\\def\\stderrhat#1{\\hat{\\mathbf{se}}_{#1}}$\n",
+    "$\\newcommand{\\Mean}{\\textbf{Mean}}$\n",
+    "$\\newcommand{\\Var}{\\textbf{Var}}$\n",
+    "$\\newcommand{\\Std}{\\textbf{Std}}$\n",
+    "$\\newcommand{\\Freq}{\\textbf{Freq}}$\n",
+    "$\\newcommand{\\RelFreq}{\\textbf{RelFreq}}$\n",
+    "$\\newcommand{\\DMeans}{\\textbf{DMeans}}$\n",
+    "$\\newcommand{\\Prop}{\\textbf{Prop}}$\n",
+    "$\\newcommand{\\DProps}{\\textbf{DProps}}$"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "09804789-d86e-4df9-a3d9-4106bfbf76d1",
@@ -109,7 +126,7 @@
     "Calculate the sample mean $\\overline{\\mathbf{x}} = \\Mean(\\mathbf{x})$\n",
     "and the sample standard deviation $s_{\\mathbf{x}} = \\Std(\\mathbf{x})$.\n",
     "We can then calculate the estimated standard error of the mean\n",
-    "$\\stderrhat{\\overline{\\mathbf{x}}} =  \\frac{ s_{\\mathbf{x}} }{ \\sqrt{n} }$."
+    "$\\stderrhat{\\overline{\\mathbf{x}}} =  \\frac{ s_{\\mathbf{x}} }{ \\sqrt{n} }$. "
    ]
   },
   {
@@ -122,7 +139,7 @@
     "Compute $t = \\frac{\\overline{\\mathbf{x}} - \\mu_0}{ \\stderrhat{\\overline{\\mathbf{x}}} }$ from\n",
     "the sample mean $\\overline{\\mathbf{x}}$,\n",
     "the theoretical population mean $\\mu_0$,\n",
-    "the estimated standard error $\\stderrhat{\\overline{\\mathbf{x}}}$."
+    "the estimated standard error $\\stderrhat{\\overline{\\mathbf{x}}}$. "
    ]
   },
   {