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[lab-confidence-intervals]Sergio Florido #210

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142 changes: 133 additions & 9 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Libraries"
"# Libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.stats as st"
]
},
{
Expand All @@ -30,13 +33,68 @@
"**Hint**: function `stats.norm.interval` from `scipy` can help you get through this exercise. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"st.norm.interval - we use when we know the std of the population and n~of observation is greater than 30\n",
"st.t.interval - when we dont know the std of the population or nº of observation is less than 30"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"(172.14308590115726, 174.79024743217607)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"# NORM DIST\n",
"\n",
"heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
"alpha = 0.80\n",
"std = 4\n",
"mean = np.mean(heights)\n",
"n = len(heights)\n",
"\n",
"st.norm.interval(0.80, loc = mean, scale = std/np.sqrt(n))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(170.9117270472475, 176.02160628608584)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# doing with T\n",
"\n",
"heights = np.array([167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195])\n",
"s = heights.std(ddof=1)\n",
"mean = heights.mean()\n",
"n = len(heights)\n",
"\n",
"st.t.interval(0.80, n-1, loc = mean, scale = s/np.sqrt(n))"
]
},
{
Expand All @@ -51,11 +109,77 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80%: [0.20248138545542083, 0.3118043288302934]\n",
"90%: [0.18698561776452813, 0.3273000965211861]\n"
]
}
],
"source": [
"# your code here"
"n = 105\n",
"losses = 27\n",
"p = losses / n\n",
"\n",
"z80 = st.norm.ppf(1-(1-0.8)/2)\n",
"moe80 = z80 * np.sqrt((p*(1-p))/n)\n",
"\n",
"\n",
"z90 = st.norm.ppf(1-(1-0.9)/2)\n",
"moe90 = z90 * np.sqrt((p*(1-p))/n)\n",
"\n",
"\n",
"CI80 = [p-moe80, p+moe80]\n",
"CI90 = [p-moe90, p+moe90]\n",
"print(\"80%: \", CI80)\n",
"print(\"90%: \", CI90)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.20248138545542083, 0.3118043288302934)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# PYTHON WAY\n",
"st.norm.interval(0.80, loc = p, scale = np.sqrt((p*(1-p))/n))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.1869856177645281, 0.3273000965211861)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# PYTHON WAY\n",
"st.norm.interval(0.90, loc = p, scale = np.sqrt((p*(1-p))/n))"
]
},
{
Expand Down Expand Up @@ -145,7 +269,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.10.9"
}
},
"nbformat": 4,
Expand Down