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[lab-confidence-intervals] Elson Luis #227

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100 changes: 88 additions & 12 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": 2,
"metadata": {},
"outputs": [],
"source": [
"# Libraries"
"# Libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.stats as st"
]
},
{
Expand All @@ -32,11 +35,28 @@
},
{
"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",
"heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
"std = 4 # of the population\n",
"mean = sum(heights)/len(heights)\n",
"mean\n",
"n = len(heights)\n",
"st.norm.interval(0.80,loc=mean,scale=std/np.sqrt(n))"
]
},
{
Expand All @@ -51,11 +71,60 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"import scipy.stats as stats\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# CONFIDENCE INTERVALS WITH PROPORTION"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.20248138545542083, 0.3118043288302934)\n",
"(0.18698561776452813, 0.3273000965211861)\n"
]
}
],
"source": [
"total = 105\n",
"losses = 27\n",
"\n",
"sample_proportion = losses / total\n",
"\n",
"confidence_80 = 0.80\n",
"confidence_90 = 0.90\n",
"\n",
"standard_error = np.sqrt((sample_proportion * (1 - sample_proportion)) / total)\n",
"\n",
"z_score_80 = stats.norm.ppf((1 + confidence_80) / 2)\n",
"z_score_90 = stats.norm.ppf((1 + confidence_90) / 2)\n",
"\n",
"margin_of_error_80 = z_score_80 * standard_error\n",
"margin_of_error_90 = z_score_90 * standard_error\n",
"\n",
"confidence_interval_80 = (sample_proportion - margin_of_error_80, sample_proportion + margin_of_error_80)\n",
"confidence_interval_90 = (sample_proportion - margin_of_error_90, sample_proportion + margin_of_error_90)\n",
"\n",
"\n",
"print(confidence_interval_80)\n",
"print(confidence_interval_90)"
]
},
{
Expand All @@ -76,7 +145,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -94,7 +163,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -121,17 +190,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -145,7 +221,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.11.5"
}
},
"nbformat": 4,
Expand Down