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[lab-confidence-intervals] Miguel Moraes #223

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148 changes: 142 additions & 6 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -32,11 +32,118 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
"heights"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"173.46666666666667"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean = sum(heights) / len(heights)\n",
"mean"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7.356888576280704"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statistic\n",
"\n",
"std = statistics.stdev(heights)\n",
"std"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.stats as st"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(heights)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(171.0323076132764, 175.90102572005694)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.norm.interval(0.8, loc=mean, scale = std/np.sqrt(15))"
]
},
{
Expand All @@ -51,11 +158,40 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 27,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80% Confidence Interval: (0.20248138545542083, 0.3118043288302934)\n",
"90% Confidence Interval: (0.18698561776452813, 0.3273000965211861)\n"
]
}
],
"source": [
"# your code here"
"from scipy.stats import norm\n",
"import math\n",
"import scipy.stats as stats\n",
"import numpy as np\n",
"\n",
"n = 105\n",
"shops_with_losses = 27\n",
"\n",
"p_hat = shops_with_losses / n\n",
"\n",
"z_80 = norm.ppf(0.9)\n",
"z_90 = norm.ppf(0.95)\n",
"\n",
"margin_error_80 = z_80 * math.sqrt((p_hat * (1 - p_hat)) / n)\n",
"margin_error_90 = z_90 * math.sqrt((p_hat * (1 - p_hat)) / n)\n",
"\n",
"confidence_interval_80 = (p_hat - margin_error_80, p_hat + margin_error_80)\n",
"confidence_interval_90 = (p_hat - margin_error_90, p_hat + margin_error_90)\n",
"\n",
"print(f\"80% Confidence Interval: {confidence_interval_80}\")\n",
"print(f\"90% Confidence Interval: {confidence_interval_90}\")\n"
]
},
{
Expand Down Expand Up @@ -145,7 +281,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.11.4"
}
},
"nbformat": 4,
Expand Down