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"hide_input": true,
"slideshow": {
"slide_type": "slide"
},
"tags": [
"hide_input"
]
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" \n",
" >\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" "
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""
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"source": [
"### BEGIN hide_toggle\n",
"### Update 30/10-'20\n",
"def hide_toggle_code(off=0):\n",
" \"\"\"This function generates HTML code to toggle the display of an input\n",
" cell.\n",
" \n",
" The output of the cell will still be displayed. This can be used\n",
" to hide (from immediate view) some code to generate data or the\n",
" like. It can also be used to hide other notebook explicit\n",
" implementations - e.g., C++ processing, or the like.\n",
" \n",
" Note, calling this function alone will not enable toggling.\n",
" Instead, we must wrap the generated code in an\n",
" `IPython.display.HTML` object and return that as the cell value.\n",
" This will let IPython evaluate the HTML code and pass it on to the\n",
" browser.\n",
" \n",
" If all one wants is to toggle a cell one can use the function\n",
" `hide_toggle` below. However, we can also combine the code\n",
" generated here with other HTML code - for example _style_\n",
" declarations and pass that along embedded in an HTML object.\n",
" \n",
" Parameters\n",
" ----------\n",
" off : int \n",
" Offset of cell to hide relative to the cell calling this function \n",
" \n",
" Returns\n",
" -------\n",
" code : str \n",
" HTML code to enable toggling of the cell\n",
"\n",
" \"\"\"\n",
" from random import randint \n",
" from IPython.display import HTML \n",
" \n",
" jp_cell = 'document.getElementsByClassName(\"jp-Cell jp-mod-selected\")[0]'\n",
" jq_cell = '$(\"div.cell.code_cell.rendered.selected\")'\n",
" toggle_text = 'Please close'\n",
" cell_id = str(randint(1,2**64))\n",
" func_name = f'code_toggle_{cell_id}'\n",
" \n",
" scr1 = f'''\n",
" \n",
" '''\n",
" but = f'''\n",
" \n",
" >\n",
" \n",
" '''\n",
" scr2 = f'''\n",
" \n",
" '''\n",
" return scr1+but+scr2 \n",
"\n",
"def hide_toggle(off=0,cnt=None):\n",
" \"\"\"This will wrap the HTML code returned from the above function\n",
" in an `IPython.display.HTML` object so that the notebook will \n",
" evaluate the HTML code. \n",
" \n",
" This function is what we will use most of the time. However, \n",
" the function `hide_toggle_code` can be combined with other code \n",
" and then be put into an HTML object to let the notebook evaluate\n",
" all the code. \n",
"\n",
" Parameters \n",
" ----------\n",
" off : int \n",
" Cell offset relative to calling cell which we should toggle \n",
" cnt : int or None \n",
" If not None, set the execution count to this number \n",
" (currently broken)\n",
" \n",
" Returns\n",
" -------\n",
" object : IPython.display.HTML \n",
" HTML object wrapping code to toggle cell \n",
" \"\"\"\n",
" from IPython.display import HTML\n",
" if cnt is not None:\n",
" get_ipython().execution_count = cnt\n",
" return HTML(hide_toggle_code(off))\n",
"### END hide_toggle\n",
"\n",
"\n",
"### BEGIN setup_matplotlib\n",
"### Update 30/10-'20\n",
"def _setup_matplotlib():\n",
" \"\"\"Set-up Matplotlib parameters. \n",
" \n",
" We specify that we want both PDF and PNG images, and \n",
" that the default image size should be 8 by 8 inches \n",
" \n",
" We also disable warnings about too many open figures \n",
" \"\"\"\n",
" %matplotlib inline \n",
" from matplotlib import rcParams \n",
" \n",
" rcParams['figure.max_open_warning'] = 0\n",
" rcParams['font.serif'] = ['Palatino'] + rcParams['font.serif']\n",
" rcParams['font.family'] = ['serif']\n",
" rcParams['mathtext.fontset'] = 'dejavuserif'\n",
" rcParams['axes.formatter.use_mathtext'] = True\n",
"\n",
" f = None\n",
" try:\n",
" # IPython >= 7.23 depcrates set_matplotlib_formats\n",
" from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
" f = set_matplotlib_formats\n",
" \n",
" except Exception as e:\n",
" try:\n",
" from IPython.display import set_matplotlib_formats\n",
" f = set_matplotlib_formats\n",
" except Exception as e:\n",
" pass \n",
"\n",
" if f is not None:\n",
" set_matplotlib_formats('png','pdf')\n",
" \n",
"_setup_matplotlib()\n",
"### END setup_matplotlib\n",
"_setup_matplotlib()\n",
"\n",
"### BEGIN css_styling\n",
"### Update 30/10-'20\n",
"def css_styling_code():\n",
" \"\"\"This function returns HTML code to customize the CSS \n",
" of the notebook \n",
" \n",
" - The text font to be Palatino (serif)\n",
" - Headers are oblique (italic)\n",
" - Extra spacing below H1 headers \n",
" - Extra spacing spacing above H1 headers \n",
" - Headers have larger fonts, and is set in normal weight\n",
" - Remove padding around code cells \n",
" - Code uses the fint \"Source Code Pro\" (or monospace)\n",
" - Code background is changed to light yellow \n",
" - Output background is set to lavender\n",
" \n",
" The function combines these CSS declarations with the HTML \n",
" code from `hide_toggle_code` above so what we automatically \n",
" hide this code from the user. \n",
" \"\"\"\n",
" styles = '''\n",
" \n",
" \n",
" '''\n",
" return styles\n",
"\n",
"def css_styling():\n",
" from IPython.display import HTML \n",
" \n",
" return HTML(hide_toggle_code()+css_styling_code())\n",
"### END css_styling\n",
"css_styling()"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "-"
},
"tags": [
"title"
]
},
"source": [
"### Christian Holm Christensen \n",
"\n",
"# Statistik\n",
"## Introduktion med Python \n",
"## Version 0.1 - September 2019 (Dansk)\n",
"\n",
"> Vi kigger på statistik og data behandling. \n",
">\n",
"> This document is available in many formats at https://cholmcc.gitlab.io/nbi-python\n",
"\n",
"### Niels Bohr Institutet "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.680232Z",
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"shell.execute_reply": "2021-11-14T19:40:05.682438Z"
},
"slideshow": {
"slide_type": "skip"
},
"tags": [
"hide_input",
"hide_output",
"nolatex"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_454209/406809543.py:5: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
" set_matplotlib_formats('png', 'pdf')\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['figure.figsize'] = [10, 10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Data og statistik\n",
"\n",
"- Vi måler $x$\n",
"- Hver gang vi gentager målingen får vi et lidt anderledes resultat\n",
"- Hvad er vores måleresultat? "
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Svaret er bestemt via _statistik_"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Citat:\n",
"\n",
"> Statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty; \n",
"> and it thereby provides the navigation essential for controlling the course of scientific and societal advances.\n",
"> This field will become ever more critical as academia, businesses, and governments rely increasingly on data\n",
">-driven decisions, expanding the demand for statistics expertise.\n",
"\n",
"[Science, Vol. 336, Issue 6077, pp. 12](https://doi.org/10.1126/science.1218685)"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Tankeeksperiment\n",
"\n",
"Tre menesker ved en moterveejsbro tæller røde biler \n",
"\n",
"- Én er farveblind og tæller alle grønne og røde biler \n",
"- Én har festet natten før og sover halvdelen af tiden, og resten af tiden tæller kun halvdelen af alle røde biler \n",
"- Den sidste har sovet dårligt (pga. ovenover) og tæller kun halvdelen af alle røde biler "
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Antag $N=100$ røde biler passerer, så har vi at "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.690685Z",
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},
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"N=100\n",
"NN=np.array([2*N, 1/2*1/2*N, 1/2*N])"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Hvad er svaret?"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Præcision \n",
"\n",
"Usikkerhed på tælletal $n$: $\\delta_n=\\sqrt{n}$, så "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.699003Z",
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"shell.execute_reply": "2021-11-14T19:40:05.702864Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obs 1: 200.0 +/- 14.142135623730951\n",
"Obs 2: 25.0 +/- 5.0\n",
"Obs 3: 50.0 +/- 7.0710678118654755\n"
]
}
],
"source": [
"dNN = np.sqrt(NN)\n",
"for i,(n,dn) in enumerate(zip(NN,dNN)):\n",
" print('Obs {}: {} +/- {}'.format(i+1,n,dn))"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Relativt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.709240Z",
"iopub.status.busy": "2021-11-14T19:40:05.708331Z",
"iopub.status.idle": "2021-11-14T19:40:05.712149Z",
"shell.execute_reply": "2021-11-14T19:40:05.713088Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obs 1 relativ usikkerhed: 7.07%\n",
"Obs 2 relativ usikkerhed: 20.00%\n",
"Obs 3 relativ usikkerhed: 14.14%\n"
]
}
],
"source": [
"for i,(n,dn) in enumerate(zip(NN,dNN)):\n",
" print('Obs {} relativ usikkerhed: {:6.2f}%'.format(i+1,100*dn/n))"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Observatør 1 har den bedst bestemte værdi - eller mest _præcise_ (engl. _precise_) værdi"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Nøjagtighed\n",
"\n",
"Lad os sammenligne med den \"sande\" værdi"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.728975Z",
"iopub.status.busy": "2021-11-14T19:40:05.726979Z",
"iopub.status.idle": "2021-11-14T19:40:05.731355Z",
"shell.execute_reply": "2021-11-14T19:40:05.727968Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obs 1 måling 200.0 +/- 14.1 er cirka 7 usikkerheder fra N=200.0\n",
"Obs 2 måling 25.0 +/- 5.0 er cirka 15 usikkerheder fra N=25.0\n",
"Obs 3 måling 50.0 +/- 7.1 er cirka 7 usikkerheder fra N=50.0\n"
]
}
],
"source": [
"for i,(n,dn) in enumerate(zip(NN,dNN)):\n",
" print('Obs {} måling {:5.1f} +/- {:4.1f} er cirka {:2.0f} usikkerheder fra N={}'\n",
" .format(i+1, n, dn, np.abs(N-n)/dn, n))"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Observatørene 1 og 3 er altså _lige_ tætte på den \"sande\" værdi, mens Observatør 2 er langt fra.\n",
"\n",
"De er mere _nøjagtige_ (engl. _accurate_) end Observatør 2 "
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Præcision og Nøjagtighed \n",
"\n",
"(engl. _precision and accuracy_) \n",
"\n",
"- _Præcision_ er hvor godt vi kender vores resultat og kan beregnes statistisk \n",
"- _Nøjagtighed_ er hvor tæt vi er på den \"sande\" resultat og kan oftes kun anslås "
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"_Statistik_ handler om præcision, mens det er en kunstform at være nøjagtig. \n",
"\n",
"_Eller_: Statistik fortæller os ikke hvor tæt vi er på den \"sande\" værdi"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Middelværdi\n",
"\n",
"Anne har lavet 10 målinger "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
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"iopub.status.busy": "2021-11-14T19:40:05.738515Z",
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"shell.execute_reply": "2021-11-14T19:40:05.741776Z"
}
},
"outputs": [],
"source": [
"data = np.array([2, 5, 2, 5, 1, 1, 3, 7, 2, 7])"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Hvad er hendes result?"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Gennemsnittet (eller middelværdi)\n",
"\n",
"$$\\overline{x} = \\frac1N\\sum_{i=1}^N x_i\\quad.$$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.751340Z",
"iopub.status.busy": "2021-11-14T19:40:05.749575Z",
"iopub.status.idle": "2021-11-14T19:40:05.754698Z",
"shell.execute_reply": "2021-11-14T19:40:05.753793Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.5\n"
]
}
],
"source": [
"resultat = data.mean()\n",
"print(resultat)"
]
},
{
"cell_type": "markdown",
"metadata": {
"lang": "da",
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Middelværdi som funktion af data størrelse\n",
"\n",
"Lad os varierer data størrelsen (engl. _sample size_). \n",
"\n",
"- 1000 prøver \n",
"- af tilfældig størrelse mellem 10 og 9999 ($N\\sim U(10,9999)$)\n",
"- af tilfældige heltal mellem 0 og 9 ($X\\sim U(0,9)$)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-14T19:40:05.764796Z",
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"iopub.status.idle": "2021-11-14T19:40:08.209774Z",
"shell.execute_reply": "2021-11-14T19:40:08.210597Z"
}
},
"outputs": [
{
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\n",
"image/png": 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\n",
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