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Update after exporting PDFs
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‎notebooks/5.0-Visualization-and-modeling.ipynb‎

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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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"source": [
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"## Linear fits\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"source": [
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"### Explicit formulas for uniform uncertainty\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"outputs": [],
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"source": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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"source": [
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"### Exercise 2\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"outputs": [],
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"source": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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"source": [
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"### Linear fits with `polyfit`\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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"outputs": [],
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"source": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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"source": [
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"### Exercise 3\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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"outputs": [],
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"source": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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"source": [
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"## Residual analysis\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs": [],
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"source": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"source": [
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"### Exercise 4\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"outputs": [],
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"source": [
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,

‎notebooks/6.0-Fitting-complex-functions.ipynb‎

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"\n",
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"Now we consider the general case of the goodness-of-fit statistic,\n",
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"\n",
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"$$\\chi^2(m,c;\\{x_k\\},\\{y_k\\},\\{\\alpha_k\\}) = \\sum_{k=1}^N\\frac{[y_k - (mx_k + c)]^2}{\\alpha_k^2}, \\label{eq:lsq}\\tag{1}$$\n",
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"$$\n",
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"\\chi^2(m,c;\\{x_k\\},\\{y_k\\},\\{\\alpha_k\\}) = \\sum_{k=1}^N\\frac{[y_k - (mx_k + c)]^2}{\\alpha_k^2}, \\label{eq:lsq}\\tag{1}\n",
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"$$\n",
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"\n",
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"where the errors $\\{\\alpha_k\\}$ are non-uniform. Differentiating with respect to the fit parameters $\\{m, c\\}$ to find the extremum, we obtain\n",
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"\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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"nbformat": 4,

‎notebooks/7.0-The-error-matrix.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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"nbformat": 4,

‎notebooks/8.0-Hypothesis-testing.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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"nbformat": 4,

‎notebooks/8.1-The-t-distribution.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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}
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‎notebooks/A.0-Reading-goals.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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}
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"nbformat": 4,

‎notebooks/A.1-Example-Fit.ipynb‎

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"# Show residuals\n",
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"# Setting use_line_collection=True avoids a warning in Matplotlib 3.1\n",
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"plt.stem(frequency, res, use_line_collection=True)\n",
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"plt.stem(frequency, res)\n",
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"plt.xlabel(\"Frequency (Hz)\")\n",
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"plt.ylabel(\"Residual (mV)\")\n",
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"plt.title(\"Fit residuals\")\n",
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"outputs": [],
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"# Show normalized residuals\n",
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"# Setting use_line_collection=True avoids a warning in Matplotlib 3.1\n",
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"plt.stem(frequency, normres, use_line_collection=True)\n",
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"plt.stem(frequency, normres)\n",
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"plt.xlabel(\"Frequency (Hz)\")\n",
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"plt.ylabel(\"Normalized Residual\")\n",
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"plt.title(\"Normalized residuals\")\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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"nbformat": 4,

‎notebooks/A.2-Minimum-Deviation-Fit.ipynb‎

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"This example is based on [A.3-Template-Fit.ipynb](A.3-Template-Fit.ipynb)."
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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‎notebooks/A.3-Template-Fit.ipynb‎

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"cell_type": "markdown",
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},
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"source": [
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"##### About this notebook\n",
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"© J. Steven Dodge, 2019. The notebook text is licensed under CC BY 4.0. See more at [Creative Commons](https://creativecommons.org/licenses/by/4.0/). The notebook code is open source under the [MIT License](https://opensource.org/licenses/MIT)."
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"display_name": "Python 3 (ipykernel)",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"nbformat": 4,
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"nbformat_minor": 4
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}

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