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[lab-confidence-intervals] Ana Santana #215

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112 changes: 99 additions & 13 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 scipy.stats as st\n",
"import numpy as np\n",
"from math import nan\n"
]
},
{
Expand All @@ -32,11 +35,54 @@
},
{
"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 = np.array([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": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(170.9117270472475, 176.02160628608584)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# USING T-DIST\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 +97,51 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Our CI for porpotion is [0.20248138545542083,0.3118043288302934]\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"sample_size= 105 \n",
"losses= 27\n",
"n=105\n",
"p=losses/sample_size\n",
"#80% confidence\n",
"z_value = st.norm.ppf(1-(1-0.80)/2)\n",
"se = np.sqrt((p*(1-p))/n) #standard error\n",
"margin_of_error = z_value * se\n",
"lower_bound = p- margin_of_error\n",
"upper_bound = p+ margin_of_error\n",
"\n",
"print(f\"Our CI for porpotion is [{lower_bound},{upper_bound}]\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.20248138545542083, 0.3118043288302934)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.norm.interval(0.80, loc=p, scale=np.sqrt ((p*(1-p))/n))"
]
},
{
Expand All @@ -76,11 +162,11 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"# your code here\n"
]
},
{
Expand All @@ -94,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -121,7 +207,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -145,7 +231,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.10.9"
}
},
"nbformat": 4,
Expand Down