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[lab-confidence-intervals] Pedro Faria Blanc #203

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121 changes: 109 additions & 12 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -9,14 +10,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Libraries"
"# Libraries\n",
"\n",
"import numpy as np\n",
"import scipy.stats as st"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -32,14 +37,50 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"172.59744399428203\n",
"174.3358893390513\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"# confidence level of 80%\n",
"confidence_level = 0.80\n",
"\n",
"z_score = st.norm.ppf(confidence_level)\n",
"\n",
"heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
"\n",
"mean = np.mean(heights)\n",
"\n",
"# Assuming that the standard deviation of the sizes in the population is 4\n",
"std = 4\n",
"n = len(heights)\n",
"\n",
"left = mean - z_score * (std / np.sqrt(n))\n",
"right = mean + z_score * (std / np.sqrt(n))\n",
"\n",
"print(left)\n",
"print(right)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -51,14 +92,68 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.32793330928514125\n",
"0.18635240500057298\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"# confidence level of 80%\n",
"confidence_level = 0.80\n",
"\n",
"# In a sample of 105 shops with 27 losses \n",
"shops = 105\n",
"shops_losses = 27\n",
"sample_proportion = shops_losses / shops\n",
"\n",
"z_score = st.norm.ppf(confidence_level)\n",
"\n",
"error_margin = z_score* np.sqrt((sample_proportion * (1 - sample_proportion)) / 27)\n",
"\n",
"positive = sample_proportion + error_margin\n",
"negative = sample_proportion - error_margin\n",
"\n",
"print(positive)\n",
"print(negative)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.07983770947987007, 0.4344480048058441)\n"
]
}
],
"source": [
"# for the 90% confidence level\n",
"confidence_level = 0.90\n",
"\n",
"z_score = st.norm.ppf(confidence_level)\n",
"\n",
"error_margin1 = z_score * np.sqrt((sample_proportion * (1 - sample_proportion)) / 27)\n",
"\n",
"ci = st.norm.interval(0.90, loc = sample_proportion, scale = error_margin1)\n",
"\n",
"print(ci)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -76,14 +171,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -94,14 +190,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
Expand All @@ -121,7 +218,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -145,7 +242,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.11.3"
}
},
"nbformat": 4,
Expand Down