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0807c5a
ejercicio 1
pdeleonsoria Jul 22, 2024
4e069e8
terminado
pdeleonsoria Jul 24, 2024
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133 changes: 127 additions & 6 deletions notebook/problems.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -24,12 +24,107 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 9,
"id": "34720ab6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La media de los elementos con distribución normal es: -0.02575701913475372\n",
"La media de los elementos con distribución chi-cuadrado es: 3.1341815977631926\n",
"La mediana de los elementos con distribución normal es: 0.06711738230446523\n",
"La mediana de los elementos con distribución chi-cuadrado es: 2.264034231107231\n",
"La moda de los elementos con distribución normal es: 0.1504587572000871\n",
"La moda de los elementos con distribución chi-cuadrado es: 2.158281311033611\n",
"El rango de los elementos con distribución normal es: 5.282970897189165\n",
"El rango de los elementos con distribución chi-cuadrado es: 9.345939537979136\n",
"La varianza de los elementos con distribución normal es: 1.0575524438121537\n",
"La varianza de los elementos con distribución chi-cuadrado es: 6.242600009195317\n",
"La desviación típica de los elementos con distribución normal es: 1.028373688798072\n",
"La desviación típica de los elementos con distribución chi-cuadrado es: 2.4985195635006177\n",
"La oblicuidad de los elementos con distribución normal es: -0.6331162644708517\n",
"La oblicuidad de los elementos con distribución chi-cuadrado es: 0.9909054595855211\n",
"La curtosis de los elementos con distribución normal es: 0.5208785728358238\n",
"La curtosis de los elementos con distribución chi-cuadrado es: -0.10824014999966147\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8390ef8",
"metadata": {},
"outputs": [],
"source": [
"# TODO"
"# TODO\n",
"import numpy as np \n",
"normal = np.random.normal(size=100)\n",
"chicua= np.random.chisquare(3,100)\n",
"\n",
"import statistics as stats\n",
"\n",
"#Media \n",
"media_normal = stats.mean(normal)\n",
"media_chi = stats.mean(chicua)\n",
"\n",
"print(f\"La media de los elementos con distribución normal es: {media_normal}\")\n",
"print(f\"La media de los elementos con distribución chi-cuadrado es: {media_chi}\")\n",
"\n",
"#Mediana\n",
"mediana_normal = stats.median(normal)\n",
"mediana_chi = stats.median(chicua)\n",
"\n",
"print(f\"La mediana de los elementos con distribución normal es: {mediana_normal}\")\n",
"print(f\"La mediana de los elementos con distribución chi-cuadrado es: {mediana_chi}\")\n",
"\n",
"#Moda \n",
"moda_normal = stats.mode(normal)\n",
"moda_chi = stats.mode(chicua)\n",
"\n",
"print(f\"La moda de los elementos con distribución normal es: {moda_normal}\")\n",
"print(f\"La moda de los elementos con distribución chi-cuadrado es: {moda_chi}\")\n",
"\n",
"#Rango\n",
"rango_normal = max(normal) - min(normal)\n",
"rango_chi = max(chicua) - min(chicua)\n",
"\n",
"print(f\"El rango de los elementos con distribución normal es: {rango_normal}\")\n",
"print(f\"El rango de los elementos con distribución chi-cuadrado es: {rango_chi}\")\n",
"\n",
"#Varianza \n",
"var_normal = stats.variance(normal)\n",
"var_chi = stats.variance(chicua)\n",
"\n",
"print(f\"La varianza de los elementos con distribución normal es: {var_normal}\")\n",
"print(f\"La varianza de los elementos con distribución chi-cuadrado es: {var_chi}\")\n",
"\n",
"#Desviación típica \n",
"dt_normal = stats.stdev(normal)\n",
"dt_chi = stats.stdev(chicua)\n",
"\n",
"print(f\"La desviación típica de los elementos con distribución normal es: {dt_normal}\")\n",
"print(f\"La desviación típica de los elementos con distribución chi-cuadrado es: {dt_chi}\")\n",
"\n",
"#Oblicuidad \n",
"from scipy.stats import skew\n",
"sk_normal = skew(normal)\n",
"sk_chi = skew(chicua)\n",
"\n",
"print(f\"La oblicuidad de los elementos con distribución normal es: {sk_normal}\")\n",
"print(f\"La oblicuidad de los elementos con distribución chi-cuadrado es: {sk_chi}\")\n",
"\n",
"#Curtosis\n",
"from scipy.stats import kurtosis\n",
"kur_normal = kurtosis(normal)\n",
"kur_chi = kurtosis(chicua)\n",
"\n",
"print(f\"La curtosis de los elementos con distribución normal es: {kur_normal}\")\n",
"print(f\"La curtosis de los elementos con distribución chi-cuadrado es: {kur_chi}\")\n",
"\n"
]
},
{
Expand All @@ -48,12 +143,38 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 20,
"id": "d590308e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La desviación estándar es: 2.0\n"
]
}
],
"source": [
"# TODO"
"# TODO\n",
"\n",
"data = [4,2,5,8,6]\n",
"N=len(data)\n",
"import statistics as stats\n",
"\n",
"media = stats.mean(data)\n",
"\n",
"def st_dev(lista):\n",
" suma = 0\n",
" for i in lista:\n",
" suma +=(i-media)**2 \n",
" varianza = suma / N\n",
" desviacion = varianza **0.5\n",
" return desviacion\n",
"\n",
"\n",
"\n",
"print(f\"La desviación estándar es: {st_dev(data)}\")"
]
}
],
Expand All @@ -76,7 +197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.12.4"
}
},
"nbformat": 4,
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20 changes: 10 additions & 10 deletions notebook/solutions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 2,
"id": "34720ab6",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -93,7 +93,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 3,
"id": "75ec280a",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -123,7 +123,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 4,
"id": "d7644384",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -153,7 +153,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 5,
"id": "3e8f1c6f",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -189,7 +189,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 6,
"id": "2f31b2e1",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -219,7 +219,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 7,
"id": "a8b51d2f",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -254,7 +254,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 8,
"id": "2b42a0af",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -287,7 +287,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 9,
"id": "968348dd",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -331,7 +331,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 10,
"id": "d590308e",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -402,7 +402,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.12.4"
}
},
"nbformat": 4,
Expand Down