diff --git a/henni/GeographicAnalysis-CA.ipynb b/henni/GeographicAnalysis-CA.ipynb new file mode 100644 index 0000000..4741e6a --- /dev/null +++ b/henni/GeographicAnalysis-CA.ipynb @@ -0,0 +1,1521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zusammenhang zwischen jährlichem Transportwert und Nähe zu Mexiko\n", + "//Henriette Sand, 564285\n", + "\n", + "##### Nullhypothese: \n", + "Die Höhe des jährlichen Transportwertes einzelner US-Staaten hängt nicht von der Nähe zu Mexiko ab.\n", + "\n", + "##### Alternativhypothese\n", + "Die Höhe des jährlichen Transportwertes einzelner US-Staaten hängt von der Nähe zu Mexiko ab." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Daten laden\n", + "df <- read.csv(\"trade_values_by_state_and_year_CA.csv\", header=TRUE, row.names=\"state\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Arithmetische Mittel berechnen" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Nach Entfernung zur mexikanischen Grenze gruppieren\n", + "\n", + "1: keine/geringe Entfernung zu Mexiko\n", + "\n", + "2: mittlere Entfernung zu Mexiko\n", + "\n", + "3: große Entfernung zu Mexiko" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# manuell erhobene Gruppierung nach Staat in alphabetischer Reihenfolge\n", + "distance <- c(3, 1, 3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 1, 2, 2, 1, 1, 3, 2, 1, 2, 2, 1, 2, 3, 1, 2, 1, 1, 3, 2, 1, 2, 2, 2, 2, 3, 2, 1, 2, 1, 2, 1, 2)\n", + "avg_value <- cbind(avg_value, distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Nach Mittelwert des Transportwertes gruppieren" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1: hoher jährlicher Transportwert in US-Dollar\n", + "\n", + "2: mittlerer jährlicher Transportwert in US-Dollar\n", + "\n", + "3: niedriger jährlicher Transportwert in US-Dollar" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "average <- avg_value[,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "5357607570.91667" + ], + "text/latex": [ + "5357607570.91667" + ], + "text/markdown": [ + "5357607570.91667" + ], + "text/plain": [ + "[1] 5357607571" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "median(average)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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avg_valuedistancevalue
AL 65063125173 2
AK 11050352951 3
AS 03 3
AZ 36430902993 2
AR 21635456923 3
CA450800805113 1
CO 51978193022 2
CT 51654297792 2
DE 17551229752 3
DC 915732012 3
FL 83574349043 2
GA106021569703 1
HI 2238688493 3
ID 21150905121 3
IL534932919811 1
IN199912205171 1
IA 69726077772 2
KS 39364869282 2
KY113971324652 1
LA 51096875343 2
ME 32779284441 2
MD 36616617972 2
MA109070439122 1
MI722487620551 1
MN152400445041 1
MS 31412729873 2
MO 83826542602 2
MT 47380542051 2
NE 26563134272 3
NV 20799876542 3
NH 65878533041 2
NJ151050496292 1
NM 4149790063 3
NY318990626271 1
NC100879629812 2
ND 58415269491 2
OH340057791271 1
OK 60804997043 2
OR 55173958402 2
PA224436591981 1
RI 9753488612 3
SC 65056265272 2
SD 10329292962 3
TN139287047362 1
TX424008391653 1
UT 33016402682 2
VT 42897731621 2
VA 51560165142 2
WA223870386121 1
WV 29343275372 2
WI115608355301 1
WY 13526639402 3
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " & avg\\_value & distance & value\\\\\n", + "\\hline\n", + "\tAL & 6506312517 & 3 & 2 \\\\\n", + "\tAK & 1105035295 & 1 & 3 \\\\\n", + "\tAS & 0 & 3 & 3 \\\\\n", + "\tAZ & 3643090299 & 3 & 2 \\\\\n", + "\tAR & 2163545692 & 3 & 3 \\\\\n", + "\tCA & 45080080511 & 3 & 1 \\\\\n", + "\tCO & 5197819302 & 2 & 2 \\\\\n", + "\tCT & 5165429779 & 2 & 2 \\\\\n", + "\tDE & 1755122975 & 2 & 3 \\\\\n", + "\tDC & 91573201 & 2 & 3 \\\\\n", + "\tFL & 8357434904 & 3 & 2 \\\\\n", + "\tGA & 10602156970 & 3 & 1 \\\\\n", + "\tHI & 223868849 & 3 & 3 \\\\\n", + "\tID & 2115090512 & 1 & 3 \\\\\n", + "\tIL & 53493291981 & 1 & 1 \\\\\n", + "\tIN & 19991220517 & 1 & 1 \\\\\n", + "\tIA & 6972607777 & 2 & 2 \\\\\n", + "\tKS & 3936486928 & 2 & 2 \\\\\n", + "\tKY & 11397132465 & 2 & 1 \\\\\n", + "\tLA & 5109687534 & 3 & 2 \\\\\n", + "\tME & 3277928444 & 1 & 2 \\\\\n", + "\tMD & 3661661797 & 2 & 2 \\\\\n", + "\tMA & 10907043912 & 2 & 1 \\\\\n", + "\tMI & 72248762055 & 1 & 1 \\\\\n", + "\tMN & 15240044504 & 1 & 1 \\\\\n", + "\tMS & 3141272987 & 3 & 2 \\\\\n", + "\tMO & 8382654260 & 2 & 2 \\\\\n", + "\tMT & 4738054205 & 1 & 2 \\\\\n", + "\tNE & 2656313427 & 2 & 3 \\\\\n", + "\tNV & 2079987654 & 2 & 3 \\\\\n", + "\tNH & 6587853304 & 1 & 2 \\\\\n", + "\tNJ & 15105049629 & 2 & 1 \\\\\n", + "\tNM & 414979006 & 3 & 3 \\\\\n", + "\tNY & 31899062627 & 1 & 1 \\\\\n", + "\tNC & 10087962981 & 2 & 2 \\\\\n", + "\tND & 5841526949 & 1 & 2 \\\\\n", + "\tOH & 34005779127 & 1 & 1 \\\\\n", + "\tOK & 6080499704 & 3 & 2 \\\\\n", + "\tOR & 5517395840 & 2 & 2 \\\\\n", + "\tPA & 22443659198 & 1 & 1 \\\\\n", + "\tRI & 975348861 & 2 & 3 \\\\\n", + "\tSC & 6505626527 & 2 & 2 \\\\\n", + "\tSD & 1032929296 & 2 & 3 \\\\\n", + "\tTN & 13928704736 & 2 & 1 \\\\\n", + "\tTX & 42400839165 & 3 & 1 \\\\\n", + "\tUT & 3301640268 & 2 & 2 \\\\\n", + "\tVT & 4289773162 & 1 & 2 \\\\\n", + "\tVA & 5156016514 & 2 & 2 \\\\\n", + "\tWA & 22387038612 & 1 & 1 \\\\\n", + "\tWV & 2934327537 & 2 & 2 \\\\\n", + "\tWI & 11560835530 & 1 & 1 \\\\\n", + "\tWY & 1352663940 & 2 & 3 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | avg_value | distance | value |\n", + "|---|---|---|---|\n", + "| AL | 6506312517 | 3 | 2 |\n", + "| AK | 1105035295 | 1 | 3 |\n", + "| AS | 0 | 3 | 3 |\n", + "| AZ | 3643090299 | 3 | 2 |\n", + "| AR | 2163545692 | 3 | 3 |\n", + "| CA | 45080080511 | 3 | 1 |\n", + "| CO | 5197819302 | 2 | 2 |\n", + "| CT | 5165429779 | 2 | 2 |\n", + "| DE | 1755122975 | 2 | 3 |\n", + "| DC | 91573201 | 2 | 3 |\n", + "| FL | 8357434904 | 3 | 2 |\n", + "| GA | 10602156970 | 3 | 1 |\n", + "| HI | 223868849 | 3 | 3 |\n", + "| ID | 2115090512 | 1 | 3 |\n", + "| IL | 53493291981 | 1 | 1 |\n", + "| IN | 19991220517 | 1 | 1 |\n", + "| IA | 6972607777 | 2 | 2 |\n", + "| KS | 3936486928 | 2 | 2 |\n", + "| KY | 11397132465 | 2 | 1 |\n", + "| LA | 5109687534 | 3 | 2 |\n", + "| ME | 3277928444 | 1 | 2 |\n", + "| MD | 3661661797 | 2 | 2 |\n", + "| MA | 10907043912 | 2 | 1 |\n", + "| MI | 72248762055 | 1 | 1 |\n", + "| MN | 15240044504 | 1 | 1 |\n", + "| MS | 3141272987 | 3 | 2 |\n", + "| MO | 8382654260 | 2 | 2 |\n", + "| MT | 4738054205 | 1 | 2 |\n", + "| NE | 2656313427 | 2 | 3 |\n", + "| NV | 2079987654 | 2 | 3 |\n", + "| NH | 6587853304 | 1 | 2 |\n", + "| NJ | 15105049629 | 2 | 1 |\n", + "| NM | 414979006 | 3 | 3 |\n", + "| NY | 31899062627 | 1 | 1 |\n", + "| NC | 10087962981 | 2 | 2 |\n", + "| ND | 5841526949 | 1 | 2 |\n", + "| OH | 34005779127 | 1 | 1 |\n", + "| OK | 6080499704 | 3 | 2 |\n", + "| OR | 5517395840 | 2 | 2 |\n", + "| PA | 22443659198 | 1 | 1 |\n", + "| RI | 975348861 | 2 | 3 |\n", + "| SC | 6505626527 | 2 | 2 |\n", + "| SD | 1032929296 | 2 | 3 |\n", + "| TN | 13928704736 | 2 | 1 |\n", + "| TX | 42400839165 | 3 | 1 |\n", + "| UT | 3301640268 | 2 | 2 |\n", + "| VT | 4289773162 | 1 | 2 |\n", + "| VA | 5156016514 | 2 | 2 |\n", + "| WA | 22387038612 | 1 | 1 |\n", + "| WV | 2934327537 | 2 | 2 |\n", + "| WI | 11560835530 | 1 | 1 |\n", + "| WY | 1352663940 | 2 | 3 |\n", + "\n" + ], + "text/plain": [ + " avg_value distance value\n", + "AL 6506312517 3 2 \n", + "AK 1105035295 1 3 \n", + "AS 0 3 3 \n", + "AZ 3643090299 3 2 \n", + "AR 2163545692 3 3 \n", + "CA 45080080511 3 1 \n", + "CO 5197819302 2 2 \n", + "CT 5165429779 2 2 \n", + "DE 1755122975 2 3 \n", + "DC 91573201 2 3 \n", + "FL 8357434904 3 2 \n", + "GA 10602156970 3 1 \n", + "HI 223868849 3 3 \n", + "ID 2115090512 1 3 \n", + "IL 53493291981 1 1 \n", + "IN 19991220517 1 1 \n", + "IA 6972607777 2 2 \n", + "KS 3936486928 2 2 \n", + "KY 11397132465 2 1 \n", + "LA 5109687534 3 2 \n", + "ME 3277928444 1 2 \n", + "MD 3661661797 2 2 \n", + "MA 10907043912 2 1 \n", + "MI 72248762055 1 1 \n", + "MN 15240044504 1 1 \n", + "MS 3141272987 3 2 \n", + "MO 8382654260 2 2 \n", + "MT 4738054205 1 2 \n", + "NE 2656313427 2 3 \n", + "NV 2079987654 2 3 \n", + "NH 6587853304 1 2 \n", + "NJ 15105049629 2 1 \n", + "NM 414979006 3 3 \n", + "NY 31899062627 1 1 \n", + "NC 10087962981 2 2 \n", + "ND 5841526949 1 2 \n", + "OH 34005779127 1 1 \n", + "OK 6080499704 3 2 \n", + "OR 5517395840 2 2 \n", + "PA 22443659198 1 1 \n", + "RI 975348861 2 3 \n", + "SC 6505626527 2 2 \n", + "SD 1032929296 2 3 \n", + "TN 13928704736 2 1 \n", + "TX 42400839165 3 1 \n", + "UT 3301640268 2 2 \n", + "VT 4289773162 1 2 \n", + "VA 5156016514 2 2 \n", + "WA 22387038612 1 1 \n", + "WV 2934327537 2 2 \n", + "WI 11560835530 1 1 \n", + "WY 1352663940 2 3 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Gruppierung nach Staat in alphabetischer Reihenfolge\n", + "value <- c(2, 3, 3, 2, 3, 1, 2, 2, 3, 3, 2, 1, 3, 3, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 3, 3, 2, 1, 3, 1, 2, 2, 1, 2, 2, 1, 3, 2, 3, 1, 1, 2, 2, 2, 1, 2, 1, 3)\n", + "avg_value <- cbind(avg_value, value)\n", + "avg_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 1: kleine Distanz zu Mexiko und hoher Transportwert" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "16" + ], + "text/latex": [ + "16" + ], + "text/markdown": [ + "16" + ], + "text/plain": [ + "[1] 16" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "16" + ], + "text/latex": [ + "16" + ], + "text/markdown": [ + "16" + ], + "text/plain": [ + "[1] 16" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dis1 <- subset(avg_value, distance == 1)\n", + "val1 <- subset(avg_value, value == 1)\n", + "# Kontrolle: Vergleich der Größen der Stichprobe\n", + "nrow(dis1)\n", + "nrow(val1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "19451559751.2708" + ], + "text/latex": [ + "19451559751.2708" + ], + "text/markdown": [ + "19451559751.2708" + ], + "text/plain": [ + "[1] 19451559751" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "27043168846.1354" + ], + "text/latex": [ + "27043168846.1354" + ], + "text/markdown": [ + "27043168846.1354" + ], + "text/plain": [ + "[1] 27043168846" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Kontrolle: arithmetische Mittel der Stichproben\n", + "mean(dis1[,1])\n", + "mean(val1[,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tTwo Sample t-test\n", + "\n", + "data: dis1[, 1] and val1[, 1]\n", + "t = -1.1191, df = 30, p-value = 0.272\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -21445979461 6262761271\n", + "sample estimates:\n", + " mean of x mean of y \n", + "19451559751 27043168846 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# |T| ermitteln\n", + "t.test(dis1[,1], val1[,1], var.equal = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "2.04227245630124" + ], + "text/latex": [ + "2.04227245630124" + ], + "text/markdown": [ + "2.04227245630124" + ], + "text/plain": [ + "[1] 2.042272" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# T* ermitteln\n", + "qt(p = 0.05/2, df = 30, lower.tail = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Ergebnis\n", + "\n", + "|T| = -1.1191\n", + "\n", + "T* = 2.04227245630124\n", + "\n", + "|T| < T* Nullhypothese annehmen, Alternativhypothese verwerfen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 2: mittlere Distanz zu Mexiko und mittlerer Transportwert" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "23" + ], + "text/latex": [ + "23" + ], + "text/markdown": [ + "23" + ], + "text/plain": [ + "[1] 23" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "23" + ], + "text/latex": [ + "23" + ], + "text/markdown": [ + "23" + ], + "text/plain": [ + "[1] 23" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dis2 <- subset(avg_value, distance == 2)\n", + "val2 <- subset(avg_value, value == 2)\n", + "# Kontrolle: Vergleich der Größen der Stichproben\n", + "nrow(dis2)\n", + "nrow(val2)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "5569630417.61594" + ], + "text/latex": [ + "5569630417.61594" + ], + "text/markdown": [ + "5569630417.61594" + ], + "text/plain": [ + "[1] 5569630418" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "5408394065.99275" + ], + "text/latex": [ + "5408394065.99275" + ], + "text/markdown": [ + "5408394065.99275" + ], + "text/plain": [ + "[1] 5408394066" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Kontrolle: arithmetische Mittel der Stichproben\n", + "mean(dis2[,1])\n", + "mean(val2[,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tTwo Sample t-test\n", + "\n", + "data: dis2[, 1] and val2[, 1]\n", + "t = 0.16644, df = 44, p-value = 0.8686\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -1791123654 2113596357\n", + "sample estimates:\n", + " mean of x mean of y \n", + "5569630418 5408394066 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# |T| ermitteln\n", + "t.test(dis2[,1], val2[,1], var.equal = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "2.01536757444376" + ], + "text/latex": [ + "2.01536757444376" + ], + "text/markdown": [ + "2.01536757444376" + ], + "text/plain": [ + "[1] 2.015368" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# T* ermitteln\n", + "qt(p = 0.05/2, df = 44, lower.tail = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Ergebnis\n", + "\n", + "|T| = 0,16644\n", + "\n", + "T* = 2.01536757444376\n", + "\n", + "|T| < T* Nullhypothese annehmen, Alternativhypothese verwerfen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 3: große Distanz zu Mexico und niedriger Transportwert" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "13" + ], + "text/latex": [ + "13" + ], + "text/markdown": [ + "13" + ], + "text/plain": [ + "[1] 13" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "13" + ], + "text/latex": [ + "13" + ], + "text/markdown": [ + "13" + ], + "text/plain": [ + "[1] 13" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dis3 <- subset(avg_value, distance == 3)\n", + "val3 <- subset(avg_value, value == 3)\n", + "# Kontrolle: Vergleich der Größen der Stichproben\n", + "nrow(dis3)\n", + "nrow(val3)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "10286443702.8718" + ], + "text/latex": [ + "10286443702.8718" + ], + "text/markdown": [ + "10286443702.8718" + ], + "text/plain": [ + "[1] 10286443703" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1228189131.29487" + ], + "text/latex": [ + "1228189131.29487" + ], + "text/markdown": [ + "1228189131.29487" + ], + "text/plain": [ + "[1] 1228189131" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Kontrolle: arithmetische Mittel der Stichproben\n", + "mean(dis3[,1])\n", + "mean(val3[,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tTwo Sample t-test\n", + "\n", + "data: dis3[, 1] and val3[, 1]\n", + "t = 2.146, df = 24, p-value = 0.0422\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " 346394907 17770114236\n", + "sample estimates:\n", + " mean of x mean of y \n", + "10286443703 1228189131 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# |T| ermitteln\n", + "t.test(dis3[,1], val3[,1], var.equal = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1.71088207990943" + ], + "text/latex": [ + "1.71088207990943" + ], + "text/markdown": [ + "1.71088207990943" + ], + "text/plain": [ + "[1] 1.710882" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# T* ermitteln\n", + "qt(p = 0.05, df = 24, lower.tail = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ergebnis\n", + "\n", + "|T| = 2.146\n", + "\n", + "T* = 1.71088207990943\n", + "\n", + "|T| > T* Nullhypothese verwerfen, Alternativhypothese annehmen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/henni/GeographicAnalysis-MX.ipynb b/henni/GeographicAnalysis-MX.ipynb new file mode 100644 index 0000000..ef5f631 --- /dev/null +++ b/henni/GeographicAnalysis-MX.ipynb @@ -0,0 +1,1521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zusammenhang zwischen jährlichem Transportwert und Nähe zu Mexiko\n", + "//Henriette Sand, 564285\n", + "\n", + "##### Nullhypothese: \n", + "Die Höhe des jährlichen Transportwertes einzelner US-Staaten hängt nicht von der Nähe zu Mexiko ab.\n", + "\n", + "##### Alternativhypothese\n", + "Die Höhe des jährlichen Transportwertes einzelner US-Staaten hängt von der Nähe zu Mexiko ab." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Daten laden\n", + "df <- read.csv(\"trade_values_by_state_and_year_MX.csv\", header=TRUE, row.names=\"state\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Arithmetische Mittel berechnen" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Nach Entfernung zur mexikanischen Grenze gruppieren\n", + "\n", + "1: keine/geringe Entfernung zu Mexiko\n", + "\n", + "2: mittlere Entfernung zu Mexiko\n", + "\n", + "3: große Entfernung zu Mexiko" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# manuell erhobene Gruppierung nach Staat in alphabetischer Reihenfolge\n", + "distance <- c(2, 3, 3, 1, 2, 1, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 3, 2, 2, 3, 2, 2, 3, 3, 1, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 1, 2, 3, 3, 3, 3, 3, 3)\n", + "avg_value <- cbind(avg_value, distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Nach Mittelwert des Transportwertes gruppieren" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1: hoher jährlicher Transportwert in US-Dollar\n", + "\n", + "2: mittlerer jährlicher Transportwertin US-Dollar\n", + "\n", + "3: niedriger jährlicher Transportwert in US-Dollar" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "average <- avg_value[,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "3423362655.33333" + ], + "text/latex": [ + "3423362655.33333" + ], + "text/markdown": [ + "3423362655.33333" + ], + "text/plain": [ + "[1] 3423362655" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "median(average)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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avg_valuedistancevalue
AL 57211735882 2
AK 422471433 3
AS 03 3
AZ 157944613211 1
AR 14717336592 3
CA 697083636271 1
CO 26977265842 3
CT 34031865603 3
DE 4850175423 3
DC 208502163 3
FL 85555692042 2
GA 96658265052 2
HI 303581013 3
ID 3941187633 3
IL 215065833353 1
IN 95608202633 2
IA 37297439473 3
KS 26800629382 3
KY 78617743362 2
LA 92490753231 2
ME 1186215343 3
MD 27544174713 3
MA 59820066973 2
MI 593130454303 1
MN 42727295303 3
MS 34435387512 3
MO 55534357772 3
MT 1503125483 3
NE 16799777452 3
NV 9857883652 3
NH 9309744843 3
NJ 71251814553 2
NM 20261422521 3
NY 64649458293 2
NC 83716276223 2
ND 4075688073 3
OH 143911406463 2
OK 15292565982 3
OR 11129735923 3
PA 83096560643 2
RI 17156418013 3
SC 56866947213 3
SD 4242863883 3
TN 117122624802 2
TX1906546000951 1
UT 40744738012 3
VT 1912806963 3
VA 20745470723 3
WA 34872521033 3
WV 4239739283 3
WI 57121873463 2
WY 788882163 3
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " & avg\\_value & distance & value\\\\\n", + "\\hline\n", + "\tAL & 5721173588 & 2 & 2 \\\\\n", + "\tAK & 42247143 & 3 & 3 \\\\\n", + "\tAS & 0 & 3 & 3 \\\\\n", + "\tAZ & 15794461321 & 1 & 1 \\\\\n", + "\tAR & 1471733659 & 2 & 3 \\\\\n", + "\tCA & 69708363627 & 1 & 1 \\\\\n", + "\tCO & 2697726584 & 2 & 3 \\\\\n", + "\tCT & 3403186560 & 3 & 3 \\\\\n", + "\tDE & 485017542 & 3 & 3 \\\\\n", + "\tDC & 20850216 & 3 & 3 \\\\\n", + "\tFL & 8555569204 & 2 & 2 \\\\\n", + "\tGA & 9665826505 & 2 & 2 \\\\\n", + "\tHI & 30358101 & 3 & 3 \\\\\n", + "\tID & 394118763 & 3 & 3 \\\\\n", + "\tIL & 21506583335 & 3 & 1 \\\\\n", + "\tIN & 9560820263 & 3 & 2 \\\\\n", + "\tIA & 3729743947 & 3 & 3 \\\\\n", + "\tKS & 2680062938 & 2 & 3 \\\\\n", + "\tKY & 7861774336 & 2 & 2 \\\\\n", + "\tLA & 9249075323 & 1 & 2 \\\\\n", + "\tME & 118621534 & 3 & 3 \\\\\n", + "\tMD & 2754417471 & 3 & 3 \\\\\n", + "\tMA & 5982006697 & 3 & 2 \\\\\n", + "\tMI & 59313045430 & 3 & 1 \\\\\n", + "\tMN & 4272729530 & 3 & 3 \\\\\n", + "\tMS & 3443538751 & 2 & 3 \\\\\n", + "\tMO & 5553435777 & 2 & 3 \\\\\n", + "\tMT & 150312548 & 3 & 3 \\\\\n", + "\tNE & 1679977745 & 2 & 3 \\\\\n", + "\tNV & 985788365 & 2 & 3 \\\\\n", + "\tNH & 930974484 & 3 & 3 \\\\\n", + "\tNJ & 7125181455 & 3 & 2 \\\\\n", + "\tNM & 2026142252 & 1 & 3 \\\\\n", + "\tNY & 6464945829 & 3 & 2 \\\\\n", + "\tNC & 8371627622 & 3 & 2 \\\\\n", + "\tND & 407568807 & 3 & 3 \\\\\n", + "\tOH & 14391140646 & 3 & 2 \\\\\n", + "\tOK & 1529256598 & 2 & 3 \\\\\n", + "\tOR & 1112973592 & 3 & 3 \\\\\n", + "\tPA & 8309656064 & 3 & 2 \\\\\n", + "\tRI & 1715641801 & 3 & 3 \\\\\n", + "\tSC & 5686694721 & 3 & 3 \\\\\n", + "\tSD & 424286388 & 3 & 3 \\\\\n", + "\tTN & 11712262480 & 2 & 2 \\\\\n", + "\tTX & 190654600095 & 1 & 1 \\\\\n", + "\tUT & 4074473801 & 2 & 3 \\\\\n", + "\tVT & 191280696 & 3 & 3 \\\\\n", + "\tVA & 2074547072 & 3 & 3 \\\\\n", + "\tWA & 3487252103 & 3 & 3 \\\\\n", + "\tWV & 423973928 & 3 & 3 \\\\\n", + "\tWI & 5712187346 & 3 & 2 \\\\\n", + "\tWY & 78888216 & 3 & 3 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | avg_value | distance | value |\n", + "|---|---|---|---|\n", + "| AL | 5721173588 | 2 | 2 |\n", + "| AK | 42247143 | 3 | 3 |\n", + "| AS | 0 | 3 | 3 |\n", + "| AZ | 15794461321 | 1 | 1 |\n", + "| AR | 1471733659 | 2 | 3 |\n", + "| CA | 69708363627 | 1 | 1 |\n", + "| CO | 2697726584 | 2 | 3 |\n", + "| CT | 3403186560 | 3 | 3 |\n", + "| DE | 485017542 | 3 | 3 |\n", + "| DC | 20850216 | 3 | 3 |\n", + "| FL | 8555569204 | 2 | 2 |\n", + "| GA | 9665826505 | 2 | 2 |\n", + "| HI | 30358101 | 3 | 3 |\n", + "| ID | 394118763 | 3 | 3 |\n", + "| IL | 21506583335 | 3 | 1 |\n", + "| IN | 9560820263 | 3 | 2 |\n", + "| IA | 3729743947 | 3 | 3 |\n", + "| KS | 2680062938 | 2 | 3 |\n", + "| KY | 7861774336 | 2 | 2 |\n", + "| LA | 9249075323 | 1 | 2 |\n", + "| ME | 118621534 | 3 | 3 |\n", + "| MD | 2754417471 | 3 | 3 |\n", + "| MA | 5982006697 | 3 | 2 |\n", + "| MI | 59313045430 | 3 | 1 |\n", + "| MN | 4272729530 | 3 | 3 |\n", + "| MS | 3443538751 | 2 | 3 |\n", + "| MO | 5553435777 | 2 | 3 |\n", + "| MT | 150312548 | 3 | 3 |\n", + "| NE | 1679977745 | 2 | 3 |\n", + "| NV | 985788365 | 2 | 3 |\n", + "| NH | 930974484 | 3 | 3 |\n", + "| NJ | 7125181455 | 3 | 2 |\n", + "| NM | 2026142252 | 1 | 3 |\n", + "| NY | 6464945829 | 3 | 2 |\n", + "| NC | 8371627622 | 3 | 2 |\n", + "| ND | 407568807 | 3 | 3 |\n", + "| OH | 14391140646 | 3 | 2 |\n", + "| OK | 1529256598 | 2 | 3 |\n", + "| OR | 1112973592 | 3 | 3 |\n", + "| PA | 8309656064 | 3 | 2 |\n", + "| RI | 1715641801 | 3 | 3 |\n", + "| SC | 5686694721 | 3 | 3 |\n", + "| SD | 424286388 | 3 | 3 |\n", + "| TN | 11712262480 | 2 | 2 |\n", + "| TX | 190654600095 | 1 | 1 |\n", + "| UT | 4074473801 | 2 | 3 |\n", + "| VT | 191280696 | 3 | 3 |\n", + "| VA | 2074547072 | 3 | 3 |\n", + "| WA | 3487252103 | 3 | 3 |\n", + "| WV | 423973928 | 3 | 3 |\n", + "| WI | 5712187346 | 3 | 2 |\n", + "| WY | 78888216 | 3 | 3 |\n", + "\n" + ], + "text/plain": [ + " avg_value distance value\n", + "AL 5721173588 2 2 \n", + "AK 42247143 3 3 \n", + "AS 0 3 3 \n", + "AZ 15794461321 1 1 \n", + "AR 1471733659 2 3 \n", + "CA 69708363627 1 1 \n", + "CO 2697726584 2 3 \n", + "CT 3403186560 3 3 \n", + "DE 485017542 3 3 \n", + "DC 20850216 3 3 \n", + "FL 8555569204 2 2 \n", + "GA 9665826505 2 2 \n", + "HI 30358101 3 3 \n", + "ID 394118763 3 3 \n", + "IL 21506583335 3 1 \n", + "IN 9560820263 3 2 \n", + "IA 3729743947 3 3 \n", + "KS 2680062938 2 3 \n", + "KY 7861774336 2 2 \n", + "LA 9249075323 1 2 \n", + "ME 118621534 3 3 \n", + "MD 2754417471 3 3 \n", + "MA 5982006697 3 2 \n", + "MI 59313045430 3 1 \n", + "MN 4272729530 3 3 \n", + "MS 3443538751 2 3 \n", + "MO 5553435777 2 3 \n", + "MT 150312548 3 3 \n", + "NE 1679977745 2 3 \n", + "NV 985788365 2 3 \n", + "NH 930974484 3 3 \n", + "NJ 7125181455 3 2 \n", + "NM 2026142252 1 3 \n", + "NY 6464945829 3 2 \n", + "NC 8371627622 3 2 \n", + "ND 407568807 3 3 \n", + "OH 14391140646 3 2 \n", + "OK 1529256598 2 3 \n", + "OR 1112973592 3 3 \n", + "PA 8309656064 3 2 \n", + "RI 1715641801 3 3 \n", + "SC 5686694721 3 3 \n", + "SD 424286388 3 3 \n", + "TN 11712262480 2 2 \n", + "TX 190654600095 1 1 \n", + "UT 4074473801 2 3 \n", + "VT 191280696 3 3 \n", + "VA 2074547072 3 3 \n", + "WA 3487252103 3 3 \n", + "WV 423973928 3 3 \n", + "WI 5712187346 3 2 \n", + "WY 78888216 3 3 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Gruppierung nach Staat in alphabetischer Reihenfolge\n", + "value <- c(2, 3, 3, 1, 3, 1, 3, 3, 3, 3, 2, 2, 3, 3, 1, 2, 3, 3, 2, 2, 3, 3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 2, 3)\n", + "avg_value <- cbind(avg_value, value)\n", + "avg_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 1: kleine Distanz zu Mexiko und hoher Transportwert" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "5" + ], + "text/latex": [ + "5" + ], + "text/markdown": [ + "5" + ], + "text/plain": [ + "[1] 5" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "5" + ], + "text/latex": [ + "5" + ], + "text/markdown": [ + "5" + ], + "text/plain": [ + "[1] 5" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dis1 <- subset(avg_value, distance == 1)\n", + "val1 <- subset(avg_value, value == 1)\n", + "# Kontrolle: Vergleich der Größen der Stichproben\n", + "nrow(dis1)\n", + "nrow(val1)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "57486528523.4667" + ], + "text/latex": [ + "57486528523.4667" + ], + "text/markdown": [ + "57486528523.4667" + ], + "text/plain": [ + "[1] 57486528523" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "71395410761.5333" + ], + "text/latex": [ + "71395410761.5333" + ], + "text/markdown": [ + "71395410761.5333" + ], + "text/plain": [ + "[1] 71395410762" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Kontrolle: arithmetische Mittel der Stichproben\n", + "mean(dis1[,1])\n", + "mean(val1[,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tTwo Sample t-test\n", + "\n", + "data: dis1[, 1] and val1[, 1]\n", + "t = -0.2933, df = 8, p-value = 0.7768\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -123263075206 95445310730\n", + "sample estimates:\n", + " mean of x mean of y \n", + "57486528523 71395410762 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# |T| ermitteln\n", + "t.test(dis1[,1], val1[,1], var.equal = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "2.30600413520417" + ], + "text/latex": [ + "2.30600413520417" + ], + "text/markdown": [ + "2.30600413520417" + ], + "text/plain": [ + "[1] 2.306004" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# T* ermitteln\n", + "qt(p = 0.05/2, df = 8, lower.tail = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Ergebnis\n", + "\n", + "|T| = -0.2933\n", + "\n", + "T* = 2.30600413520417\n", + "\n", + "|T| < T* Nullhypothese annehmen, Alternativhypothese verwerfen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 2: mittlere Distanz zu Mexiko und mittlerer Transportwert" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "14" + ], + "text/latex": [ + "14" + ], + "text/markdown": [ + "14" + ], + "text/plain": [ + "[1] 14" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "14" + ], + "text/latex": [ + "14" + ], + "text/markdown": [ + "14" + ], + "text/plain": [ + "[1] 14" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dis2 <- subset(avg_value, distance == 2)\n", + "val2 <- subset(avg_value, value == 2)\n", + "# Kontrolle: Vergleich der Größen der Stichprobe\n", + "nrow(dis2)\n", + "nrow(val2)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "4830900023.63095" + ], + "text/latex": [ + "4830900023.63095" + ], + "text/markdown": [ + "4830900023.63095" + ], + "text/plain": [ + "[1] 4830900024" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "8477374811.11905" + ], + "text/latex": [ + "8477374811.11905" + ], + "text/markdown": [ + "8477374811.11905" + ], + "text/plain": [ + "[1] 8477374811" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Kontrolle: arithmetische Mittel der Stichproben\n", + "mean(dis2[,1])\n", + "mean(val2[,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tTwo Sample t-test\n", + "\n", + "data: dis2[, 1] and val2[, 1]\n", + "t = -3.2433, df = 26, p-value = 0.003236\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -5957553632 -1335395943\n", + "sample estimates:\n", + " mean of x mean of y \n", + "4830900024 8477374811 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# |T| ermitteln\n", + "t.test(dis2[,1], val2[,1], var.equal = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "2.05552943864287" + ], + "text/latex": [ + "2.05552943864287" + ], + "text/markdown": [ + "2.05552943864287" + ], + "text/plain": [ + "[1] 2.055529" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# T* ermitteln\n", + "qt(p = 0.05/2, df = 26, lower.tail = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ergebnis\n", + "\n", + "|T| = -3.2433\n", + "\n", + "T* = 2.05552943864287\n", + "\n", + "|T| < T* Nullhypothese annehmen, Alternativhypothese verwerfen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 3: große Distanz zu Mexico und niedriger Transportwert" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "33" + ], + "text/latex": [ + "33" + ], + "text/markdown": [ + "33" + ], + "text/plain": [ + "[1] 33" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "33" + ], + "text/latex": [ + "33" + ], + "text/markdown": [ + "33" + ], + "text/plain": [ + "[1] 33" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dis3 <- subset(avg_value, distance == 3)\n", + "val3 <- subset(avg_value, value == 3)\n", + "# Kontrolle: Vergleich der Größe der Stichproben\n", + "nrow(dis3)\n", + "nrow(val3)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "5414329692.32323" + ], + "text/latex": [ + "5414329692.32323" + ], + "text/markdown": [ + "5414329692.32323" + ], + "text/plain": [ + "[1] 5414329692" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1759933988.83333" + ], + "text/latex": [ + "1759933988.83333" + ], + "text/markdown": [ + "1759933988.83333" + ], + "text/plain": [ + "[1] 1759933989" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Kontrolle: arithmetische Mittel der Stichproben\n", + "mean(dis3[,1])\n", + "mean(val3[,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tTwo Sample t-test\n", + "\n", + "data: dis3[, 1] and val3[, 1]\n", + "t = 1.9244, df = 64, p-value = 0.05875\n", + "alternative hypothesis: true difference in means is not equal to 0\n", + "95 percent confidence interval:\n", + " -139282537 7448073944\n", + "sample estimates:\n", + " mean of x mean of y \n", + "5414329692 1759933989 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# |T| ermitteln\n", + "t.test(dis3[,1], val3[,1], var.equal = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1.66901302502409" + ], + "text/latex": [ + "1.66901302502409" + ], + "text/markdown": [ + "1.66901302502409" + ], + "text/plain": [ + "[1] 1.669013" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# T* ermitteln\n", + "qt(p = 0.05, df = 64, lower.tail = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Ergebnis\n", + "\n", + "|T| = 1.9244\n", + "\n", + "T* = 1.66901302502409\n", + "\n", + "|T| > T* Nullhypothese verwerfen, Alternativhypothese annehmen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/henni/TotalTradeValueFile-CA.ipynb b/henni/TotalTradeValueFile-CA.ipynb new file mode 100644 index 0000000..9c1b0d6 --- /dev/null +++ b/henni/TotalTradeValueFile-CA.ipynb @@ -0,0 +1,765 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df_ex = pd.read_csv('export_by_state_and_year_CA.csv', low_memory=False)\n", + "df_im = pd.read_csv('import_by_state_and_year_CA.csv', low_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "states = ['AL', 'AK', 'AS', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'DC', 'FL', 'GA', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VT', 'VA','WA', 'WV', 'WI', 'WY']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_total = df_ex.add(df_im, fill_value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "df_total.drop(\"2008\", axis=1, inplace=True)\n", + "df_total.drop(\"2010\", axis=1, inplace=True)\n", + "df_total.drop(\"2011\", axis=1, inplace=True)\n", + "df_total.drop(\"2012\", axis=1, inplace=True)\n", + "df_total[\"state\"] = states\n", + "df_total" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df_total.to_csv(\"./trade_values_by_state_and_year_CA.csv\", sep=',',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/henni/TotalTradeValueFile-MX.ipynb b/henni/TotalTradeValueFile-MX.ipynb new file mode 100644 index 0000000..d37d6e3 --- /dev/null +++ b/henni/TotalTradeValueFile-MX.ipynb @@ -0,0 +1,759 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df_ex = pd.read_csv('export_by_state_and_year_MX.csv', low_memory=False)\n", + "df_im = pd.read_csv('import_by_state_and_year_MX.csv', low_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "states = ['AL', 'AK', 'AS', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'DC', 'FL', 'GA', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VT', 'VA','WA', 'WV', 'WI', 'WY']\n", + "years = ['2013', '2014', '2015', '2016', '2017', '2018']" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "df_total = df_ex.add(df_im, fill_value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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7692838722 7599942975 \n", + "35 ND 382144078 473413408 442591039 373907267 \n", + "36 OH 12633416450 13796737566 14683350184 14416247422 \n", + "37 OK 1668978824 1653124656 1586125818 1352779461 \n", + "38 OR 845790276 948561192 1116162839 1183248655 \n", + "39 PA 6436930061 7254407040 8392128990 7700011638 \n", + "40 RI 1246455945 1141540346 1394406032 1361124260 \n", + "41 SC 4059013255 5741521265 5883704978 5515573930 \n", + "42 SD 439851653 414743734 473777396 371207632 \n", + "43 TN 10010035274 11486487351 11995226940 11787994682 \n", + "44 TX 195584974251 192760747221 177996557103 173711228856 \n", + "45 UT 4016992672 3868602026 4191050032 4050848022 \n", + "46 VT 238920438 258869131 245741941 170640622 \n", + "47 VA 1624847802 1949354973 2148219332 1891030264 \n", + "48 WA 3986432252 3524645644 3158332165 3267412215 \n", + "49 WV 483567020 491454311 403296644 409169475 \n", + "50 WI 4967424404 5384452116 5628800907 5803116123 \n", + "51 WY 70433685 59651347 77938646 135289557 \n", + "\n", + " 2017 2018 \n", + "0 6495073471 6776566285 \n", + "1 41314111 56006882 \n", + "2 0 0 \n", + "3 15489173644 16691413836 \n", + "4 1508046254 1773715025 \n", + "5 73121415043 74792539849 \n", + "6 2889185234 2782046926 \n", + "7 3544000222 3451637057 \n", + "8 509987195 472879495 \n", + "9 41348800 26712268 \n", + "10 9769534939 10873730855 \n", + "11 10481211879 10855016084 \n", + "12 11808878 35560010 \n", + "13 415309017 451854331 \n", + "14 22023254674 23341878466 \n", + "15 9867516125 9937585087 \n", + "16 3477683049 4045649707 \n", + "17 2484844815 2832038290 \n", + "18 7722742315 9042203554 \n", + "19 8601849551 11116471637 \n", + "20 119678818 117775346 \n", + "21 2554978632 2414414106 \n", + "22 6289216595 5912970372 \n", + "23 65501426456 68568809666 \n", + "24 4423317321 4642184343 \n", + "25 2571274910 2754982895 \n", + "26 5720538054 6169431336 \n", + "27 157644521 146269306 \n", + "28 1741763366 2110576132 \n", + "29 1153123087 1325533077 \n", + "30 919896833 816406259 \n", + "31 7477833753 7397720379 \n", + "32 2151874164 2217845165 \n", + "33 6466930913 7109760766 \n", + "34 8292728769 11475800870 \n", + "35 378492147 394864904 \n", + "36 14758758785 16058333469 \n", + "37 1334385920 1580144909 \n", + "38 1253536944 1330541643 \n", + "39 9482374495 10592084158 \n", + "40 2446966724 2703357497 \n", + "41 6302091347 6618263551 \n", + "42 428755225 417382685 \n", + "43 11756065520 13237765114 \n", + "44 187105816538 216768276598 \n", + "45 3987578324 4331771728 \n", + "46 136438119 97073927 \n", + "47 2261685070 2572144990 \n", + "48 3314684110 3672006231 \n", + "49 444718385 311637733 \n", + "50 6135420693 6353909831 \n", + "51 52582027 77434036 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_total.drop(\"2007\", axis=1, inplace=True)\n", + "df_total.drop(\"2008\", axis=1, inplace=True)\n", + "df_total.drop(\"2010\", axis=1, inplace=True)\n", + "df_total.drop(\"2011\", axis=1, inplace=True)\n", + "df_total.drop(\"2012\", axis=1, inplace=True)\n", + "df_total[\"state\"] = states\n", + "df_total" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "df_total.to_csv(\"./trade_values_by_state_and_year_MX.csv\", sep=',',index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/henni/YearValuesByState-MX.ipynb b/henni/YearValuesByState-MX.ipynb index 154710c..985dc84 100644 --- a/henni/YearValuesByState-MX.ipynb +++ b/henni/YearValuesByState-MX.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -19,12 +19,12 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", - "df = pd.read_csv('/Users/Tobias/Documents/Studium/10SoSe_19_AI/Grundlagen_Sozialer_Netze_Schmidt/FrightDataAnalysis/Data/dot2_ytd_2007-2018_without2009_usa-mx.csv', low_memory=False)" + "df = pd.read_csv('dot2_ytd_2007-2018_without2009_usa-mx.csv', low_memory=False)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -925,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -935,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -944,9 +944,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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