regression

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diff --git a/lectures/lectures/causal_graphical_models.html b/lectures/lectures/causal_graphical_models.html
diff --git a/lectures/lectures/causal_graphical_models.ipynb b/lectures/lectures/causal_graphical_models.ipynb
diff --git a/lectures/lectures/causal_graphical_models.pdf b/lectures/lectures/causal_graphical_models.pdf
diff --git a/lectures/lectures/data/fig/flowchart.png b/lectures/lectures/data/fig/flowchart.png
diff --git a/lectures/lectures/data/fig/polynomial_regression.png b/lectures/lectures/data/fig/polynomial_regression.png
diff --git a/lectures/lectures/dynamics.ipynb b/lectures/lectures/dynamics.ipynb
@@ -31,7 +31,7 @@
         "    -   [Dynamics and Stability in One\n",
         "        Dimension](https://intro.quantecon.org/scalar_dynam.html)"
       ],
-      "id": "d2174d20-5fa3-4c13-b60a-bba0a25e38fa"
+      "id": "548020bd-5f97-4fc2-9577-fe39f1bfe7b0"
     },
     {
       "cell_type": "code",
@@ -77,7 +77,7 @@
         "    -   If there is an $(\\lambda, x)$ pair with $\\lambda = 1$ it is a\n",
         "        fixed point"
       ],
-      "id": "77f01c2e-9eb8-46f5-9686-2bf56def69dc"
+      "id": "1d6aba78-711f-4017-9920-ba08030e931e"
     },
     {
       "cell_type": "code",
@@ -118,7 +118,7 @@
         "\n",
         "-   Consider $f(x) = \\sqrt{x}$ and $f(x) = x^2$ for $x \\geq 0$"
       ],
-      "id": "52186f7f-bb9f-46c6-a310-e7cb7b1ff008"
+      "id": "c1215c45-66b5-4b2d-bf9c-706c717bc53f"
     },
     {
       "cell_type": "code",
@@ -163,7 +163,7 @@
         "See [QuantEcon Scalar\n",
         "dynamics](https://intro.quantecon.org/scalar_dynam.html) for base code"
       ],
-      "id": "366f67fc-e87d-480d-aba4-1bd5c52ec2dc"
+      "id": "29b22096-dade-418b-ae40-bbca38c4b265"
     },
     {
       "cell_type": "code",
@@ -244,7 +244,7 @@
       "source": [
         "## Evaluating the $\\sqrt{x}$ near $x=0.05 > 0$"
       ],
-      "id": "4fa89b35-5a4a-4be5-9d07-5a2fc5228c79"
+      "id": "a9f62c10-6fdb-4e92-9974-5e54d09c33f6"
     },
     {
       "cell_type": "code",
@@ -270,7 +270,7 @@
       "source": [
         "## Evaluating the $\\sqrt{x}$ near $x=1.1 > 1$"
       ],
-      "id": "4442fb15-8577-4dcf-a46b-77d7fbbc951e"
+      "id": "5d9de567-80f7-449a-bbfe-5c0beacf8874"
     },
     {
       "cell_type": "code",
@@ -296,7 +296,7 @@
       "source": [
         "## Evaluating the $x^2$ for $x = 0.6 < 1$"
       ],
-      "id": "f705bde5-ce2d-4c0a-8a11-16cfa2f4d01b"
+      "id": "009ce980-cef4-41f8-9351-d968c65b46bc"
     },
     {
       "cell_type": "code",
@@ -322,7 +322,7 @@
       "source": [
         "## Evaluating the $x^2$ for $x = 1.01 > 1$"
       ],
-      "id": "94e6852d-65d2-4588-b197-1c5914a2bfca"
+      "id": "d33be7ac-10c4-4179-9114-8e6f9b9b1676"
     },
     {
       "cell_type": "code",
@@ -426,7 +426,7 @@
         "\n",
         "## Plot Against 45 degree line Reminder"
       ],
-      "id": "98e97a60-d386-495f-a49e-799647cc2823"
+      "id": "8ea00fa0-314d-47f5-9b38-a47fb3ccdf26"
     },
     {
       "cell_type": "code",
@@ -512,7 +512,7 @@
         "\n",
         "## Implementing the Solow-Swan Model"
       ],
-      "id": "32f06caa-ebac-4cc2-beac-417efda30aa4"
+      "id": "dc266f9a-b0ee-4a05-9a21-88029aea1083"
     },
     {
       "cell_type": "code",
@@ -552,7 +552,7 @@
       "source": [
         "## Plotting $k_t$ vs. $k_{t+1}$ verifies our $k^*$"
       ],
-      "id": "488314dd-f408-45bc-8e24-3dc9f328bb92"
+      "id": "8d2719c8-fe0b-4fe6-9bad-21a57ea64087"
     },
     {
       "cell_type": "code",
@@ -604,7 +604,7 @@
         "\n",
         "## Simulation"
       ],
-      "id": "b55d44ff-165b-4945-9498-9839ec135cfc"
+      "id": "b91ff75e-1f8e-4fac-bf56-30e959ef2a5e"
     },
     {
       "cell_type": "code",
@@ -642,7 +642,7 @@
       "source": [
         "## Capital Transition from $k_0 < k^*$ and $k_0 > k^*$"
       ],
-      "id": "1513f5d8-e410-4ccd-90e4-cbae9cfdd995"
+      "id": "edb0cfae-12ce-478c-a2ef-54d65a9529a6"
     },
     {
       "cell_type": "code",
@@ -681,7 +681,7 @@
       "source": [
         "## Trajectories Using the 45 degree Line"
       ],
-      "id": "6e954e23-76d5-4e84-8099-d1c2cc1e29d9"
+      "id": "39214517-4e51-4093-b534-7e9dcedbc462"
     },
     {
       "cell_type": "code",
@@ -796,7 +796,7 @@
         "\n",
         "-   Consider $A, B, C, D$ as a set of web pages with links given below"
       ],
-      "id": "2b2bbb26-23fe-420a-ac7a-572eebd81e56"
+      "id": "3b94920a-cef4-4a72-9b1c-c47684b76ebc"
     },
     {
       "cell_type": "code",
@@ -814,7 +814,7 @@
         }
       ],
       "source": [],
-      "id": "d8107b32-db7f-496a-b307-32992f550979"
+      "id": "54d342b2-9af3-44e8-866d-c8bda72540fa"
     },
     {
       "cell_type": "markdown",
@@ -891,7 +891,7 @@
         "    actually compute the eigenvector of a huge matrix with a\n",
         "    decomposition."
       ],
-      "id": "c3e7d652-2f4e-4efe-bf87-6dafaae8090c"
+      "id": "20e1de5f-8d85-4e05-801e-2badedfdb703"
     }
   ],
   "nbformat": 4,

diff --git a/lectures/lectures/dynamics.pdf b/lectures/lectures/dynamics.pdf
diff --git a/lectures/lectures/eigenvalue_applications.ipynb b/lectures/lectures/eigenvalue_applications.ipynb
@@ -31,7 +31,7 @@
         "    -   [A First Course in Quantitative Economics with\n",
         "        Python](https://intro.quantecon.org/)"
       ],
-      "id": "ba49fd8b-8e8f-4843-bf9d-256f86f8dee3"
+      "id": "8bb7c23f-dc25-4c2d-ac69-f01e853f3264"
     },
     {
       "cell_type": "code",
@@ -69,7 +69,7 @@
         "\\end{bmatrix}\n",
         "$$"
       ],
-      "id": "b1b756b3-cd59-49c4-942b-99cf9534a391"
+      "id": "ce62f0d0-b6a2-4ee2-bfe6-c027ccccb505"
     },
     {
       "cell_type": "code",
@@ -102,7 +102,7 @@
         "\n",
         "Iterate $x_{t+1} = A x_t$ from $x_0$ for $t=100$"
       ],
-      "id": "8bf49357-d5d8-48f8-8695-13bbc3e4ef63"
+      "id": "d5c07f6e-6591-437e-9207-30739bb1bb34"
     },
     {
       "cell_type": "code",
@@ -142,7 +142,7 @@
         "\n",
         "## Iterating with $\\rho(A) < 1$"
       ],
-      "id": "1841e75f-3d84-43d6-83ba-3fae3ed3191f"
+      "id": "560b199d-567a-4acf-ac87-93f64b6841e5"
     },
     {
       "cell_type": "code",
@@ -181,7 +181,7 @@
         "-   Leave previous eigenvectors in $Q$, change $\\Lambda$ to force\n",
         "    $\\rho(A)$ directly"
       ],
-      "id": "ce0aed03-6ce0-4732-af39-aa7f94604b96"
+      "id": "86cad15a-7bc6-4a4d-bbb2-3f3ae6b67897"
     },
     {
       "cell_type": "code",
@@ -240,7 +240,7 @@
         "\n",
         "Simulate by iterating $X_{t+1} = A X_t$ from $X_0$ until $T=100$"
       ],
-      "id": "cbaf2397-94dc-4d16-a8c5-8101f15f4f8b"
+      "id": "5bbc55a4-b08d-448c-b386-bc2aa7d51bbd"
     },
     {
       "cell_type": "code",
@@ -276,7 +276,7 @@
       "source": [
         "## Dynamics of Unemployment"
       ],
-      "id": "5c35ba84-aae1-482f-92e9-3c9978bb8c81"
+      "id": "e202d6e2-8a26-410c-abaa-d68edcbbba60"
     },
     {
       "cell_type": "code",
@@ -334,7 +334,7 @@
         "\n",
         "## Using the First Eigenvector for the Steady State"
       ],
-      "id": "48017896-375e-434b-85a4-6162af7351e0"
+      "id": "9637cd3b-b616-40fd-a5aa-9b7c818a35b6"
     },
     {
       "cell_type": "code",
@@ -392,7 +392,7 @@
         "\n",
         "## Simulating with Different Eigenvalues"
       ],
-      "id": "aaed0c0b-b28b-4088-b847-00f5b857c041"
+      "id": "029a14b1-2bc8-4200-beec-915223906b4e"
     },
     {
       "cell_type": "code",
@@ -428,7 +428,7 @@
       "source": [
         "## Convergence Dynamics of Unemployment"
       ],
-      "id": "26415618-b729-45a5-8f4d-bd10256edc74"
+      "id": "509f3bc0-901b-4142-b29f-4b652dee5ccf"
     },
     {
       "cell_type": "code",
@@ -494,7 +494,7 @@
         "\n",
         "Here is an example with $1 < \\rho(A) < 1/\\beta$. Try with different $A$"
       ],
-      "id": "9c12e38a-f963-4a14-ab12-9062e391ccbf"
+      "id": "0de0c2fa-bae2-4f3f-b4c3-31ba482a78b6"
     },
     {
       "cell_type": "code",
@@ -535,7 +535,7 @@
         "    have very different scales - so there will be times when you need to\n",
         "    rescale your problems"
       ],
-      "id": "671a618b-be9b-4193-b382-1076dc20db6d"
+      "id": "4dca6b8d-16eb-4818-8f5e-5d9944fa83ff"
     },
     {
       "cell_type": "code",
@@ -583,7 +583,7 @@
         "    inv](https://www.mathworks.com/help/matlab/ref/inv.html#bu6sfy8-1)\n",
         "    for example, where `inv` is a bad idea due to poor conditioning"
       ],
-      "id": "a6854bc1-a1c2-443b-922d-abb8d97be145"
+      "id": "dac27a15-3a15-40c7-bd09-890ab34a74af"
     },
     {
       "cell_type": "code",
@@ -650,7 +650,7 @@
         "-   Let’s look at the numerical error here from the interpolation using\n",
         "    the inf-norm, i.e., $||x||_{\\infty} = \\max_n |x_n|$"
       ],
-      "id": "2042e90a-be2e-4737-9753-fee073762605"
+      "id": "48068fc0-f94a-4f9b-bb68-c05ea8f52160"
     },
     {
       "cell_type": "code",
@@ -685,7 +685,7 @@
       "source": [
         "## Things Getting Poorly Conditioned Quickly"
       ],
-      "id": "be3cef27-5905-4846-b91c-e798ce291cd9"
+      "id": "d7649e0a-b981-4c14-934b-a0a95f7dd2a7"
     },
     {
       "cell_type": "code",
@@ -720,7 +720,7 @@
       "source": [
         "## Matrix Inverses Fail Completely for $N = 20$"
       ],
-      "id": "b61d0948-d401-49e3-a1f8-c7ecdeac1875"
+      "id": "0f30e023-1b1b-4dae-bb8a-1ce3a0e41179"
     },
     {
       "cell_type": "code",
@@ -773,7 +773,7 @@
         "    -   Prefer polynomials like Chebyshev, which are designed to be as\n",
         "        orthogonal as possible"
       ],
-      "id": "41bc5e0d-f530-4d54-9a67-669cdf9cc25f"
+      "id": "249e12c3-a2d0-46b6-992d-e620f8367794"
     },
     {
       "cell_type": "code",

diff --git a/lectures/lectures/eigenvalue_applications.pdf b/lectures/lectures/eigenvalue_applications.pdf
diff --git a/lectures/lectures/index.html b/lectures/lectures/index.html
@@ -137,7 +137,9 @@ <h1>Lectures</h1>
 <li><a href="stochastic_processes.html" target="_blank">Stochastic Processes</a>, <a href="stochastic_processes.ipynb" target="_blank">Jupyter</a>, <a href="stochastic_processes.pdf" target="_blank">PDF</a></li>
 <li><a href="introduction_to_causality.html" target="_blank">Intro to Causality</a>, <a href="introduction_to_causality.ipynb" target="_blank">Jupyter</a>, <a href="introduction_to_causality.pdf" target="_blank">PDF</a></li>
 <li><a href="uncertainty_bias_variance.html" target="_blank">Uncertainty Quantification in Applied Economics</a>, <a href="uncertainty_bias_variance.ipynb" target="_blank">Jupyter</a>, <a href="uncertainty_bias_variance.pdf" target="_blank">PDF</a></li>
-<li><a href="causal_graphical_models.html" target="_blank">Causal Graphical Models</a>, <a href="causal_graphical_models.ipynb" target="_blank">Jupyter</a>, <a href="causal_graphical_models.pdf" target="_blank">PDF</a></li>
+<li><a href="causal_graphical_models.html" target="_blank">Introduction to Causal Graphical Models</a>, <a href="causal_graphical_models.ipynb" target="_blank">Jupyter</a>, <a href="causal_graphical_models.pdf" target="_blank">PDF</a></li>
+<li><a href="../lectures/using_dags.html">Practical Uses of Directed Graphical Models</a>, <a href="using_dags.ipynb" target="_blank">Jupyter</a>, <a href="using_dags.pdf" target="_blank">PDF</a></li>
+<li><a href="../lectures/regression.html">Regression</a>, <a href="regression.ipynb" target="_blank">Jupyter</a>, <a href="regression.pdf" target="_blank">PDF</a></li>
 </ol>
 
 

diff --git a/lectures/lectures/index.ipynb b/lectures/lectures/index.ipynb
@@ -41,12 +41,18 @@
         "    Quantification in Applied Economics</a>,\n",
         "    <a href=\"uncertainty_bias_variance.ipynb\" target=\"_blank\">Jupyter</a>,\n",
         "    <a href=\"uncertainty_bias_variance.pdf\" target=\"_blank\">PDF</a>\n",
-        "9.  <a href=\"causal_graphical_models.html\" target=\"_blank\">Causal Graphical\n",
-        "    Models</a>,\n",
+        "9.  <a href=\"causal_graphical_models.html\" target=\"_blank\">Introduction to\n",
+        "    Causal Graphical Models</a>,\n",
         "    <a href=\"causal_graphical_models.ipynb\" target=\"_blank\">Jupyter</a>,\n",
-        "    <a href=\"causal_graphical_models.pdf\" target=\"_blank\">PDF</a>"
+        "    <a href=\"causal_graphical_models.pdf\" target=\"_blank\">PDF</a>\n",
+        "10. [Practical Uses of Directed Graphical Models](using_dags.qmd),\n",
+        "    <a href=\"using_dags.ipynb\" target=\"_blank\">Jupyter</a>,\n",
+        "    <a href=\"using_dags.pdf\" target=\"_blank\">PDF</a>\n",
+        "11. [Regression](regression.qmd),\n",
+        "    <a href=\"regression.ipynb\" target=\"_blank\">Jupyter</a>,\n",
+        "    <a href=\"regression.pdf\" target=\"_blank\">PDF</a>"
       ],
-      "id": "c8fb9f03-d538-4c5b-8373-e7cf536a23ae"
+      "id": "c7855167-81d7-4423-ad3e-575257df4c67"
     }
   ],
   "nbformat": 4,