diff --git a/lecture_1.ipynb b/lecture_1.ipynb
index c5b8e8246c39bd92cc02789ed4f805cbe40e5bfe..04fd9924c902c333e66e429e694e6c51ee44aeb8 100644
--- a/lecture_1.ipynb
+++ b/lecture_1.ipynb
@@ -19,6 +19,9 @@
     "# \n",
     "\n",
     "<br>\n",
+    "\n",
+    "Material: [https://gitlab.rrz.uni-hamburg.de/BAN1966/statlecture](https://gitlab.rrz.uni-hamburg.de/BAN1966/statlecture)\n",
+    "\n",
     "<br>\n",
     "\n",
     " Hartmut Stadie\n",
diff --git a/lecture_2.ipynb b/lecture_2.ipynb
index 49f9dc518f47173798e69d0eccfd153c43793e50..82c5a98b027006a73d27b1294b7012a0960bc97f 100644
--- a/lecture_2.ipynb
+++ b/lecture_2.ipynb
@@ -18,8 +18,10 @@
     "## Probability density functions and confidence intervals\n",
     "\n",
     "<br>\n",
-    "<br>\n",
     "\n",
+    "Material: [https://gitlab.rrz.uni-hamburg.de/BAN1966/statlecture](https://gitlab.rrz.uni-hamburg.de/BAN1966/statlecture)\n",
+    "\n",
+    "<br>\n",
     " Hartmut Stadie\n",
     "\n",
     "hartmut.stadie@uni-hamburg.de"
@@ -711,7 +713,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c595233d",
+   "id": "e2ec2ce6",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -786,7 +788,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "06885d9b",
+   "id": "819c3f7b",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -866,7 +868,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d187a99",
+   "id": "7394d804",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1469,7 +1471,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c2d1227f",
+   "id": "e8dfbb1f",
    "metadata": {
     "cell_style": "split",
     "jupyter": {
@@ -1541,7 +1543,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18f65b9d",
+   "id": "2be2516a",
    "metadata": {
     "cell_style": "split"
    },
@@ -1557,7 +1559,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b82090eb",
+   "id": "a39e2f6f",
    "metadata": {
     "cell_style": "split"
    },
@@ -1607,7 +1609,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f09138b6",
+   "id": "fb443d99",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1629,7 +1631,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e609ea07",
+   "id": "8f17b01a",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1661,7 +1663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f258f33",
+   "id": "72272998",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1690,7 +1692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9378e3ba",
+   "id": "2f72dcc4",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1702,7 +1704,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59be1788",
+   "id": "ac6f96a5",
    "metadata": {
     "cell_style": "split"
    },
@@ -1712,7 +1714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d172ac9",
+   "id": "ec6272b7",
    "metadata": {
     "cell_style": "split"
    },
@@ -1723,7 +1725,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a096afa1",
+   "id": "15bf969e",
    "metadata": {
     "cell_style": "split"
    },
@@ -1739,7 +1741,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "648b37eb",
+   "id": "b674636f",
    "metadata": {
     "cell_style": "split"
    },
@@ -1771,7 +1773,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "770f4472",
+   "id": "82ea4c91",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1841,7 +1843,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f703d4ed",
+   "id": "335523f9",
    "metadata": {
     "slideshow": {
      "slide_type": ""
diff --git a/lecture_3.ipynb b/lecture_3.ipynb
index eda3ad98bf76ec57f9bed4ee0bda8857c31e5664..1a39c33b8ecec028014e5e2b66460a05e98b936b 100644
--- a/lecture_3.ipynb
+++ b/lecture_3.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "612072eb",
+   "id": "45ae2161",
    "metadata": {},
    "source": [
     "# Lecture 3\n",
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc6b6159",
+   "id": "fd1987a9",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "3f4ad5a3",
+   "id": "1407d2c6",
    "metadata": {
     "cell_style": "center"
    },
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "89de9c74",
+   "id": "1c34c282",
    "metadata": {
     "cell_style": "center",
     "slideshow": {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b83fd0c",
+   "id": "cc967bb7",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -198,7 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b61361d1",
+   "id": "584b2acc",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb43d9dd",
+   "id": "d221c922",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -245,49 +245,39 @@
     "This is `np.var(xs, ddof=1)` in *numpy*"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7d6b2ac",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
-   "id": "7c7c6ecd-1611-4787-8985-da0881e90d9f",
+   "id": "b16daf6f",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
-    },
-    "tags": []
+    }
    },
    "source": [
-    "# Methode der kleinsten Quadrate\n",
-    "\n",
-    "## Herleitung\n",
-    "\n",
-    "### Methode der kleinsten Quadrate \n",
-    "\n",
-    "$y(x) = mx +  a$: Finde $\\hat m$ und $\\hat a$!"
+    "## General methods for parameter estimation"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "73e6e46a-4e41-4e66-857b-2946946498d1",
+   "id": "7c7c6ecd-1611-4787-8985-da0881e90d9f",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     },
     "tags": []
    },
    "source": [
-    "<img src=\"./figures/11/line.png\" style=\"width:90.0%\" alt=\"image\" />"
+    "## The method of least squares\n",
+    "\n",
+    "\n",
+    "### Line fitting with least squares\n",
+    "\n",
+    "$y(x) = mx +  a$: Determine $\\hat m$ und $\\hat a$!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 52,
    "id": "55bf0cad-8a50-4831-8a37-daf9bcb39b71",
    "metadata": {
     "slideshow": {
@@ -298,7 +288,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -335,16 +325,16 @@
    "id": "64e67452-e6bd-442c-a174-e12bdb18dba0",
    "metadata": {
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "slide"
     },
     "tags": []
    },
    "source": [
-    "### Methode der kleinsten Quadrate \n",
+    "### Least squares\n",
     "\n",
     "$$\\chi^2 = \\sum_i \\left(\\frac{y_i - \\hat y(x)}{\\sigma_i}\\right)^2$$\n",
-    "quantifiziert die Übereinstimmung von Modell zu Daten  \n",
-    "$\\rightarrow$ $\\hat m$ und $\\hat a$ sollten $\\chi^2$ minimieren.\n",
+    "quantifies the agreement between data and model \n",
+    "$\\rightarrow$ $\\hat m$ und $\\hat a$ should $\\chi^2$ minimize.\n",
     "<img src=\"./figures/11/line.png\" style=\"width:80.0%\"\n",
     "alt=\"image\" />"
    ]
@@ -689,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a73415de",
+   "id": "70701265",
    "metadata": {},
    "outputs": [],
    "source": []