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_sources/basic_empirics/LogisticReg.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "52bc2323",
+   "id": "077072d0",
    "metadata": {},
    "source": [
     "(Chap_LogIntro)=\n",
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "917b152b",
+   "id": "0b4dc462",
    "metadata": {
     "tags": [
      "hide-input",
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45254cc4",
+   "id": "b8de6c68",
    "metadata": {},
    "source": [
     "```{figure} ../../../images/basic_empirics/logit/logit_gen.png\n",
@@ -204,7 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "503dbc1c",
+   "id": "497af849",
    "metadata": {
     "tags": []
    },
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bf310a2",
+   "id": "aa6beead",
    "metadata": {},
    "source": [
     "The variable descriptions are the following:\n",
@@ -386,7 +386,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "e0bc954f",
+   "id": "9ab34fcf",
    "metadata": {
     "tags": []
    },
@@ -412,7 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "b39d1316",
+   "id": "26934b9d",
    "metadata": {
     "tags": [
      "remove-output"
@@ -448,7 +448,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "825bdda4",
+   "id": "703538da",
    "metadata": {},
    "source": [
     "```{figure} ../../../images/basic_empirics/logit/survived_count.png\n",
@@ -464,7 +464,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4089fd99",
+   "id": "98cced9f",
    "metadata": {
     "tags": []
    },
@@ -498,7 +498,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d882aa13",
+   "id": "67110036",
    "metadata": {},
    "source": [
     "How many observations are there in the DataFrame?"
@@ -507,7 +507,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "79f1c1ac",
+   "id": "ae555a6e",
    "metadata": {
     "tags": []
    },
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf1f9f83",
+   "id": "f712182f",
    "metadata": {},
    "source": [
     "**Model selection and missing values**\n",
@@ -564,7 +564,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f08e8c45",
+   "id": "a45b7764",
    "metadata": {
     "tags": []
    },
@@ -681,7 +681,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20ec96cf",
+   "id": "ebae008c",
    "metadata": {},
    "source": [
     "Now we have the dataframe reduced down to only relevant variables, but now we need to deal with the missing values in the age variable.\n",
@@ -694,7 +694,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "e16a4efb",
+   "id": "4de471f8",
    "metadata": {
     "tags": []
    },
@@ -721,7 +721,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "c3e68a23",
+   "id": "7e9bb4ed",
    "metadata": {
     "tags": [
      "remove-output"
@@ -778,7 +778,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f22b5af",
+   "id": "e8ed149f",
    "metadata": {},
    "source": [
     "```{figure} ../../../images/basic_empirics/logit/age_boxplot.png\n",
@@ -798,7 +798,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "309cbab7",
+   "id": "b18c1466",
    "metadata": {
     "tags": []
    },
@@ -850,7 +850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a96d6bce",
+   "id": "b1dbf88a",
    "metadata": {},
    "source": [
     "There are 2 null values in the `Embarked` variable. We can drop those 2 records without loosing too much important information from our dataset, so we will do that."
@@ -859,7 +859,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "7fb8b445",
+   "id": "1d04eea0",
    "metadata": {
     "tags": []
    },
@@ -892,7 +892,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "82ef1bc2",
+   "id": "ab7608bb",
    "metadata": {
     "tags": []
    },
@@ -1014,7 +1014,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7827ce1d",
+   "id": "6f067986",
    "metadata": {},
    "source": [
     "**Converting categorical variables to a dummy indicators**\n",

_sources/python/SciPy.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "952e1687",
+   "id": "d2efbaf7",
    "metadata": {},
    "source": [
     "(Chap_SciPy)=\n",
@@ -157,7 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "cd97bc7b",
+   "id": "94f8fcd4",
    "metadata": {
     "tags": [
      "hide-input",
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68d0d3e5",
+   "id": "e7ddea1b",
    "metadata": {},
    "source": [
     "```{figure} ../images/SciPy/root_examp1.png\n",
@@ -246,7 +246,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "eb817237",
+   "id": "0480de27",
    "metadata": {
     "tags": []
    },
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a7b5544",
+   "id": "e329b9d0",
    "metadata": {},
    "source": [
     "As we saw in {numref}`Figure %s <FigScipyRoot_examp1>`, the solution is $(\\hat{x},\\hat{y})=(0.846, 2.331)$ and the zero functions are solved to $1e-12$ precision. {numref}`ExerScipy-root-lin` has you test the linear algebra solution to a system of linear equations to the SciPy root finder solution.\n",