{"text_format":"3","workbook":"xls","n_sheets":27,"sheets":["Daten_Beispiel0","LDaten_Beispiel0","Daten Beispiel 1","Koeffizienten Beispiel 1","Bestimmtheitsmass Beispiel 1","Anova Beispiel 1","Test Koeffizienten Beispiel 1","Vertrauensintervalle Beispiel 1","Daten Uebung 1","Koeffizienten Ue1","Anova UE1","Tests Koeffizienten UE1","Daten Uebung 2","Anova Tests für Koeff Ue2","Preis_Nachfrage","Loesung_Preis_Nach","PISA_2003","LPisa_2003","Skilifte","LSkilifte","meteoBoegg","LMeteoBoegg","Social_Index","LSocial_Index","Rohdaten_Social_index","VariablenDef_SocialIndex","weit_Datensaezte"],"not_rendered":{"images":17,"external_links":1,"defined_names":3},"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"Daten_Beispiel0","index":0,"n_rows":19,"n_cols":2,"rows_kept":19,"flags":["formulas_cached_only"]}
["Beispiel 0"]
["Einkommen (x_{i})","Ausgaben (y_{i}) für"]
["in 1000 Fr.","Schmuck in"]
["pro Monat","Fr. pro Jahr"]
[6.199,638]
[7.4,750.4]
[5.974,632.5]
[7.714,757.8]
[6.602,688.3]
[5.31,539.7]
[3.823,387.8]
[8.119,905.7]
[5.239,591.8]
[10.61,1058.6]
[9.804,945]
[3.411,350.2]
[6.391,637.7]
[4.205,415.6]
[7.702,716.1]

{"sheet":"LDaten_Beispiel0","index":1,"n_rows":16,"n_cols":1,"rows_kept":16,"flags":["formulas_cached_only"]}
[]
["sch=read.table(\"e:/schmuck.txt\",header=T)"]
[]
["plot(sch$Einkommen,sch$Schmuck)"]
["a=lm(sch$Schmuck~sch$Einkommen)"]
["summary(a)"]
["abline(a)"]
["confint(a)  #Vertrauensintervall fuer Koeffizienten)"]
[]
[]
["#Vertrauensinterval fuer Schätzer auf Gerade:"]
["vi=predict.lm(a,interval = c(\"confidence\"), level = 0.95,type=\"response\") #erzeugt Matrix (nicht Dataframe)"]
["vi=cbind(vi,Einkommen=sch$Einkommen)   #unabhaengige Variale zu Matrix hinzufuegen"]
["vi=vi[order(vi[,4]), ]  #nach unabhaengiger Variable ordnen"]
["lines(vi[,4],vi[,2],col=\"red\",lty=2) #zeichnen"]
["lines(vi[,4],vi[,3],col=\"red\",lty=2)"]

{"sheet":"Daten Beispiel 1","index":2,"n_rows":8,"n_cols":3,"rows_kept":8,"flags":["formulas_cached_only"]}
["Punkte","xi","yi"]
["<1, 4>",1,4]
["<2, 3>",2,3]
["<2.5 4>",2.5,4]
["<3, 4.9>",3,4.9]
["<7, 7.5>",7,7.5]
["<5, 5>",5,5]
["<6, 6>",6,6]

{"sheet":"Koeffizienten Beispiel 1","index":3,"n_rows":14,"n_cols":5,"rows_kept":14,"flags":["formulas_cached_only"]}
["Berechnung der Koeffizienten"]
["Punkte","xi","yi","xi*(yi-y)","xi(xi-x)"]
["<1, 4>",1,4,-0.914285714,-2.785714286]
["<2, 3>",2,3,-3.828571429,-3.571428571]
["<2.5 4>",2.5,4,-2.285714286,-3.214285714]
["<3, 4.9>",3,4.9,-0.042857143,-2.357142857]
["<7, 7.5>",7,7.5,18.1,22.5]
["<5, 5>",5,5,0.428571429,6.071428571]
["<6, 6>",6,6,6.514285714,13.28571429]
["Summen",null,null,17.97142857,29.92857143]
["Mittelwerte",3.785714286,4.914285714]
[]
[null,"Koeffizienten",null,"a =",0.6004773268925786]
[{"$nulls":3},"b =",2.641050119163673]

{"sheet":"Bestimmtheitsmass Beispiel 1","index":4,"n_rows":19,"n_cols":6,"rows_kept":19,"flags":["formulas_cached_only"]}
["Berechnung des Bestimheitsmasses"]
["Punkte","xi","yi","f(xi)","(f(xi)-y)^2","(yi-y)^2"]
["<1, 4>",1,4,3.2415274460562515,2.798120223930029,0.8359183673469377]
["<2, 3>",2,3,3.8420047729488305,1.1497864171543124,3.664489795918365]
["<2.5 4>",2.5,4,4.14224343639512,0.5960492788504972,0.8359183673469377]
["<3, 4.9>",3,4.9,4.442482099841409,0.22259865060271017,0.0002040816326530344]
["<7, 7.5>",7,7.5,6.844391407411724,3.7253079866374357,6.685918367346942]
["<5, 5>",5,5,5.643436753626566,0.5316612381718451,0.007346938775510304]
["<6, 6>",6,6,6.243914080519145,1.7679115922925845,1.1787755102040829]
["Mittelwerte",null,4.914285714285714]
["Summen",{"$nulls":3},10.791435387639414,13.20857142857143]
["Koeffizienten der Regressionsgleichung"]
["a =",0.6004773268925786,null,"Bestimmheitsmass:",null,0.8170024628323155]
["b =",2.641050119163673]
[]
[]
[null,null,"Mit Excelbefehl: ",null,null,0.8170024630402001]
[null,null,"Pearsonkorrelation:",null,null,0.9038818855581742]
[null,null,"Quadrat der Pearsonkorrelation",null,null,0.8170024630402003]

{"sheet":"Anova Beispiel 1","index":5,"n_rows":17,"n_cols":6,"rows_kept":17,"flags":["formulas_cached_only"]}
["ANOVA"]
["<xi, yi>","xi","yi","f(xi)","(yi-f(xi)^2","(f(xi)-y)^2"]
["<1, 4>",1,4,3.2415274460562515,0.5752806150859524,2.7981202229741693]
["<2, 3>",2,3,3.8420047729488305,0.7089720376686115,1.1497864165415819]
["<2.5 4>",2.5,4,4.14224343639512,0.02023319519749247,0.5960492784093311]
["<3, 4.9>",3,4.9,4.442482099841409,0.2093226289655268,0.22259865033310866]
["<7, 7.5>",7,7.5,6.844391407411724,0.4298226266755803,3.725307987740351]
["<5, 5>",5,5,5.643436753626566,0.4140108559174941,0.5316612385885019]
["<6, 6>",6,6,6.243914080519145,0.059494078675500106,1.7679115930523706]
["Summen",null,13.208571428571446,null,2.417136038186158,10.791435387639414]
["Mittelwerte",3.785714286,4.914285714]
[]
[null,null,"F-Wert:","((SQE)/(SQR/n-2))",22.322772109544587]
[{"$nulls":3},"P-Wert:",0.00522071209683328,"<0.5"]
[]
["Koeffizienten","a =",0.6004773268925786]
[null,"b =",2.641050119163673]

{"sheet":"Test Koeffizienten Beispiel 1","index":6,"n_rows":20,"n_cols":8,"rows_kept":20,"flags":["formulas_cached_only"]}
["Berechung der T-Teststatistik für die Koeffizienten"]
[null,"xi","yi","f(xi)","yi-f(xi)","(xi-x)^2"]
["<1, 4>",1,4,3.2415274460562515,0.7584725539437485,7.76020408322449]
["<2, 3>",2,3,3.8420047729488305,-0.8420047729488305,3.1887755112244904]
["<2.5 4>",2.5,4,4.14224343639512,-0.14224343639511972,1.6530612252244903]
["<3, 4.9>",3,4.9,4.442482099841409,0.4575179001585914,0.61734693922449]
["<7, 7.5>",7,7.5,6.844391407411724,0.6556085925882762,10.331632651224488]
["<5, 5>",5,5,5.643436753626566,-0.6434367536265659,1.4744897952244895]
["<6, 6>",6,6,6.243914080519145,-0.24391408051914532,4.903061223224489]
["Summen",null,13.208571428571446,null,null,29.928571428571423]
["Mittelwerte",3.785714286,4.914285714]
[{"$nulls":3},"Varianz ei",0.48342720763723157]
["quad.Mittelwert",14.33163265522449]
[{"$nulls":3},"Für b:",null,null,"Für a:"]
["Koeffizienten","a =",0.6004773268925786,"Var(B)",0.3005555790074935,null,"Var(A)",0.01615269906186454]
[null,"b =",2.641050119163673,"Stand.fehler(B)",0.5482294948354143,null,"Stand.fehler(A)",0.12709326914461105]
[{"$nulls":3},"t-Wert:",4.81741705625771,null,"t-Wert:",4.724698097185108]
[{"$nulls":3},"p-Wert:",0.0024046720127249843,null,"p-Wert:",0.00261035604841664]
[]
[{"$nulls":4},"(jeweils rechtsseitige Tests)"]

{"sheet":"Vertrauensintervalle Beispiel 1","index":7,"n_rows":31,"n_cols":5,"rows_kept":31,"flags":["formulas_cached_only"]}
["Vertrauensintervalle"]
[]
["Koeffizienten","a =",0.6004773268925786]
[null,"b =",2.641050119163673]
["t-15(0.975)=",2.570581835636315]
[]
[]
["Für b:",null,null,"Für a:"]
["Var(B)",0.3005555790074935,null,"Var(A)",0.01615269906186454]
["Stand.fehler(B)",0.5482294948354143,null,"Stand.fehler(A)",0.12709326914461105]
["Untere Grenze",1.231781337979684,null,null,0.2737736777978041]
["Obere Grenze",4.050318900347662,null,null,0.927180975987353]
[]
[]
[]
["Vertrauensintervalle für Schätzer auf Gerade:"]
[]
["dbei1=read.table(\"e:/b1.txt\",header=T)"]
["plot(dbei1$yi~dbei1$xi)"]
["a1=lm(yi~xi,data=dbei1)"]
["ppplotNorm(a1$residuals)"]
[]
[]
["vi1=predict.lm(a1,interval = c(\"confidence\"), level = 0.95,type=\"response\") #erzeugt Matrix (nicht Dataframe)"]
["vi1=cbind(vi1,x=dbei1$xi)   #unabhaengige Variale zu Matrix hinzufuegen"]
["vi1=vi1[order(vi1[,4]), ]  #nach unabhaengiger Variable ordnen"]
[]
["plot(dbei1$yi~dbei1$xi)"]
["abline(a1)"]
["lines(vi1[,4],vi1[,2],col=\"red\",lty=2) #zeichnen"]
["lines(vi1[,4],vi1[,3],col=\"red\",lty=2)"]

{"sheet":"Daten Uebung 1","index":8,"n_rows":11,"n_cols":3,"rows_kept":11,"flags":["formulas_cached_only"]}
["Objekt","Alter","Prämien"]
[1,60,350]
[2,30,540]
[3,20,570]
[4,35,490]
[5,45,450]
[6,44,450]
[7,28,550]
[8,64,360]
[9,18,600]
[10,54,400]

{"sheet":"Koeffizienten Ue1","index":9,"n_rows":19,"n_cols":5,"rows_kept":19,"flags":["formulas_cached_only"]}
["Berechnung der Koeffizienten"]
["Objekt","Alter","Prämien","xi(yi-y)","xi(xi-x)"]
[1,60,350,-7560,1212.0000000000002]
[2,30,540,1920,-293.9999999999999]
[3,20,570,1880,-395.99999999999994]
[4,35,490,490,-167.9999999999999]
[5,45,450,-1170,234.0000000000001]
[6,44,450,-1144,184.80000000000013]
[7,28,550,2072,-330.3999999999999]
[8,64,360,-7424,1548.8000000000002]
[9,18,600,2232,-392.4]
[10,54,400,-4104,766.8000000000002]
["Mittelwerte",39.8,476]
["Standardabweichungen",16.212478047616425,88.46845012017938]
["Summen",null,null,-12808,2365.6000000000013]
[]
[]
["Die Steigung ist somit: ",null,"a = ",-5.414271220831921]
["Der Ordinatenabschnitt: ",null,"b = ",691.4879945891105]

{"sheet":"Anova UE1","index":10,"n_rows":19,"n_cols":7,"rows_kept":19,"flags":["formulas_cached_only"]}
["Anova:"]
["Objekt","Alter","Prämien","f(xi)","(f(xi)-y)^2","(yi-f(xi)^2"]
[1,60,350,366.6317213391952,11961.420377227445,276.61415470464175]
[2,30,540,529.0598579641528,2815.348527176069,119.68670776451097]
[3,20,570,583.2025701724721,11492.391051583798,174.30785915904906]
[4,35,490,501.9885018599932,675.4022289268711,143.72417684706093]
[5,45,450,447.84578965167407,792.659560337783,4.6406222248345355]
[6,44,450,453.26006087250596,517.1048315221344,10.627996892444322]
[7,28,550,539.8884004058167,4081.727706413959,102.24444635308795]
[8,64,360,344.97463645586754,17167.645891872075,225.76154963334477]
[9,18,600,594.031112614136,13931.343544930845,35.62761662512681]
[10,54,400,399.11734866418675,5910.942076424227,0.779073380612915]
["Mittelwerte",39.8,476]
["Standardabweichungen",16.212478047616425,88.46845012017938]
["Summen",{"$nulls":3},69345.9857964152,1094.014203584714]
["Df",{"$nulls":3},1,8]
[null,"Koeffizienten der Regressionsgeraden"]
[null,"a=",-5.414271220831921,null,"F-Wert:",507.0938608964446]
[null,"b=",691.4879945891105,null,"p-Wert:",1.6009896963709025e-08,"<0.05"]

{"sheet":"Tests Koeffizienten UE1","index":11,"n_rows":26,"n_cols":7,"rows_kept":26,"flags":["formulas_cached_only"]}
["T-Teststatistik für die Koeffizienten, Vertrauensintervalle"]
[]
["Objekt","Alter (xi)","Prämien (yi)","f(xi)","yi-f(xi)","(xi-xquer)^2"]
[1,60,350,366.6317213391952,-16.631721339195224,408.04000000000013]
[2,30,540,529.0598579641528,10.940142035847202,96.03999999999995]
[3,20,570,583.2025701724721,-13.202570172472065,392.0399999999999]
[4,35,490,501.9885018599932,-11.988501859993221,23.039999999999974]
[5,45,450,447.84578965167407,2.1542103483259325,27.04000000000003]
[6,44,450,453.26006087250596,-3.26006087250596,17.640000000000025]
[7,28,550,539.8884004058167,10.111599594183303,139.23999999999992]
[8,64,360,344.97463645586754,15.02536354413246,585.6400000000001]
[9,18,600,594.031112614136,5.968887385864036,475.2399999999999]
[10,54,400,399.11734866418675,0.8826513358132502,201.64000000000007]
["Mittelwerte",39.8,476]
["Standardabweichungen",16.212478047616425,88.46845012017938]
["Summen",{"$nulls":4},2365.6]
["Die Steigung a ist somit: ",null,-5.414271220831921,"Varianz ei",136.75177544808926]
["Der Ordinatenabschnitt: ",null,691.4879945891105,"T-Tests:"]
[{"$nulls":3},"Var(A)",0.0578084948630746,"Var(B)",105.24614574771358]
[{"$nulls":3},"S.Fehler(A)",0.24043397194047808,"S.Fehler(B)",10.258954417859238]
["T-18(0.975)=",2.306004135204167,null,"t-Wert",-22.518744656317867,"t-Wert",67.40335968208785]
[{"$nulls":3},"p-Wert",8.004948481854512e-09,"p-Wert",1.3061773884714967e-12]
[]
[{"$nulls":3},"Vertrauensintervalle"]
[{"$nulls":3},"unter Grenze",-5.968712954370226,null,667.830803278656]
[{"$nulls":3},"obere Grenze",-4.859829487293616,null,715.1451858995649]

{"sheet":"Daten Uebung 2","index":12,"n_rows":13,"n_cols":3,"rows_kept":13,"flags":["formulas_cached_only"]}
["Laden","Verkaufsfläche (in 1000 qm)","Umsatz (in Mio Fr.)"]
[1,0.32,2.92]
[2,0.97,5.29]
[3,1.22,6.83]
[4,1.3,7.05]
[5,1.11,7.01]
[6,1.48,8.37]
[7,0.79,4.32]
[8,0.93,5.75]
[9,1.31,7.69]
[10,0.49,3.13]
[11,0.22,1.51]
[12,0.54,3.17]

{"sheet":"Anova Tests für Koeff Ue2","index":13,"n_rows":49,"n_cols":11,"rows_kept":49,"flags":["formulas_cached_only"]}
["Berechnung der Geradengleichung",{"$nulls":4},"Anova",null,null,"T-Test; Vertrauensintervalle"]
["Laden","Verkaufsfläche (in 1000 qm)","Umsatz (in Mio Fr.)","xi*(yi-y)","xi(xi-x)","f(xi)","(f(xi)-y)^2","(yi - f(xi))^2","yi-f(xi)","(xi-x)^2"]
[1,0.32,2.92,-0.7466666666666666,-0.1824000000000001,2.2797178048019897,8.84238931152274,0.40996128948758287,0.6402821951980102,0.3249000000000003]
[2,0.97,5.29,0.03556666666666706,0.07759999999999974,5.670682881197379,0.1741806451023239,0.14491945603673798,-0.38068288119737925,0.006399999999999958]
[3,1.22,6.83,1.9235333333333338,0.4025999999999997,6.97490021827253,2.9637925393192495,0.020996073255426732,-0.14490021827252964,0.10889999999999983]
[4,1.3,7.05,2.335666666666667,0.5329999999999998,7.392249766136578,4.574963506515758,0.11713490242054232,-0.342249766136578,0.16809999999999983]
[5,1.11,7.01,1.9499000000000004,0.24419999999999986,6.401044589959464,1.317241128586333,0.3708266914176369,0.6089554100405357,0.048399999999999936]
[6,1.48,8.37,4.612666666666666,0.8731999999999996,8.331286248830686,9.473794150018657,0.0014987545295994814,0.03871375116931297,0.3480999999999997]
[7,0.79,4.32,-0.7373333333333328,-0.07900000000000017,4.731646398503272,0.27215725797238455,0.16945275740071417,-0.41164639850327145,0.01000000000000004]
[8,0.93,5.75,0.46190000000000037,0.03719999999999983,5.462008107265356,0.043545161275580786,0.08293933028088288,0.2879918927346443,0.0015999999999999851]
[9,1.31,7.69,3.1920333333333346,0.5501999999999998,7.444418459619584,4.800854030632836,0.06031029297561812,0.24558154038041646,0.17639999999999983]
[10,0.49,3.13,-1.040433333333333,-0.19600000000000012,3.1665855940130916,4.354516127558138,0.0013385056892907708,-0.03658559401309169,0.1600000000000002]
[11,0.22,1.51,-0.8235333333333333,-0.14740000000000006,1.7580308699719298,12.217139310380293,0.061519312459032346,-0.2480308699719298,0.44890000000000035]
[12,0.54,3.17,-1.125,-0.1890000000000001,3.4274290614281218,3.3339264101616997,0.06626972166776372,-0.25742906142812183,0.12250000000000014]
["Mittelwerte",0.8900000000000002,5.253333333333333,{"$nulls":5},"Summe",1.9242]
["Summen",null,null,10.038300000000003,1.9241999999999977,null,52.368499579045995,1.5071670876208283,"Var(A)",0.07832694562004097]
[null,"Koeffizienten","a = ",5.2168693483006,null,"Freiheitsgrade",1,10,"S.Fehler(A)",0.2798695153460644]
[null,null,"b = ",0.6103196133457978,null,"F-Wert:",347.46313138852736,null,"t-Wert",18.640362962896596]
[{"$nulls":5},"p-Wert: ",4.26647406293057e-09,"< 0.05","p-Wert",2.133237031465285e-09]
[{"$nulls":8},"Var(B)",0.07460249935580805]
[{"$nulls":8},"S-Fehler (B)",0.27313458103251603]
[{"$nulls":8},"t-Wert",2.2345014352947885]
[{"$nulls":8},"p-Wert",0.024731722718519422]
[{"$nulls":8},0.15071670876208282,". = Var(ei)"]
[{"$nulls":7},"Vertrauensintervalle","für a",null,"für b"]
[{"$nulls":8},"Unter Grenze",4.593281207671465,0.0017378415262554459]
[{"$nulls":8},"Obere Grenze",5.840457488929735,1.21890138516534]
[{"$nulls":8},"t-110(0.975)=",2.2281388519862744]
[]
[]
[]
["mit Dataframe \"datLadVer\""]
["names(datLadVer)=c(\"Laden\",\"VerkaufFl\",\"Umsatz\")"]
[]
["plot(datLadVer$Umsatz~datLadVer$VerkaufFl)"]
["a2=lm(Umsatz~VerkaufFl,data=datLadVer)"]
["ppplotNorm(a2$residuals)"]
["summary(a2)"]
["abline(a2)"]
["confint(a2)"]
[]
["vi2=predict.lm(a2,interval = c(\"confidence\"), level = 0.95,type=\"response\") #erzeugt Matrix (nicht Dataframe)"]
["vi2=cbind(vi2,x=datLadVer$VerkaufFl)   #unabhaengige Variale zu Matrix hinzufuegen"]
["vi2=vi2[order(vi2[,4]), ]  #nach unabhaengiger Variable ordnen"]
[]
["plot(datLadVer$Umsatz~datLadVer$VerkaufFl)"]
["abline(a2)"]
["lines(vi2[,4],vi2[,2],col=\"red\",lty=2) #zeichnen"]
["lines(vi2[,4],vi2[,3],col=\"red\",lty=2)"]

{"sheet":"Preis_Nachfrage","index":14,"n_rows":51,"n_cols":11,"rows_kept":51,"flags":["formulas_cached_only"]}
["Beispiel bivariate lineare Regression"]
["Preisnachfragefunktion"]
[]
["Eine renommierte Schaumweinkellerei möchte einen hochwertigen Rieslingschaumwein auf den Markt bringen. "]
["Für die Festlegung des gewinnmaximierenden Verkaufspreises soll zunächst eine Preis-Absatz-Funktion ermittelt werden. Dazu wurde "]
["in n = 40 Geschäften mit vergleichbarer Frequenz in sozio-oekonomisch vergleichbaren Quartieren ein Testverkauf durchgeführt. "]
[]
["Schaetzen Sie die Preis-Absatz-Funktion."]
[{"$nulls":6},"Berechnen Sie eine Regressionsgerade"]
[null,40,{"$nulls":4},"Erklärt die Gerade Varianz weg?"]
["Preis","Anzahl verkaufter Flaschen",{"$nulls":4},"Überprüfen Sie die Voraussetzungen des Testes."]
[14.8,58,{"$nulls":4},"Testen Sie, ob die Koeffizienten der Regressionsgerade von 0 verschieden sind."]
[11.1,66,{"$nulls":4},"Liefern Sie Vertrauensintervalle für die Koeffizienten der Regressionsgerade."]
[15.4,57,{"$nulls":4},"Liefern Sie ein Vertrauensband für die geschätzten Werte auf der Regressionsgerade."]
[10.1,73]
[16.2,44,{"$nulls":4},"Wie sieht die Preisnachfragefunktion bei 500 Verkaufsstellen aus?"]
[17,52,{"$nulls":4},"Wie sieht die Erlösfunktion bei 500 Verkaufsstellen aus?"]
[13.8,63]
[16,51]
[18.9,45,{"$nulls":4},"Von der Kostenfunktion sei folgendes gegeben. "]
[12.2,60,{"$nulls":4},"K(100)=",100.03,null,null,"für die Berechnung mit R:"]
[10.7,70,{"$nulls":4},"K(3000)=",370.5973,null,null,"k1=c(100.03,370.5973,33852.93,640107.5)"]
[16.7,48,{"$nulls":4},"K(15000)=",33852.93,null,null,"x1=c(100,3000,15000,40000)"]
[17.2,50,{"$nulls":4},"K(40000)=",640107.5]
[18.9,41]
[14.3,57]
[16.4,53,{"$nulls":4},"Berechnen Sie die Menge mit dem maximalen Gewinn. "]
[15.1,53,{"$nulls":4},"Welcher Preis muss festgelegt werden?"]
[14.5,57]
[17.4,51]
[15.1,55]
[10.4,72]
[16.4,51]
[14.5,57]
[19.8,41]
[16.9,49]
[14,59]
[17.3,49]
[15.3,58]
[16.2,53]
[15.3,57]
[11.1,64]
[19.3,42]
[12.5,60]
[12.2,60]
[11.5,67]
[13.3,63]
[14.3,55]
[11.3,64]
[15.7,53]
[12.7,61]

{"sheet":"Loesung_Preis_Nach","index":15,"n_rows":73,"n_cols":17,"rows_kept":73,"flags":["formulas_cached_only"]}
["Preisnachfragefunktion Lösung",{"$nulls":15},"(modell für Resultat von lm; lev = Breite VI; lty = Art der Kurven (durchgezogen, etc)."]
["nach=read.table(\"e:/nach.txt\",header=T)",{"$nulls":14},"VI-Band mit"]
["head(nach)",{"$nulls":14},"VI_band_Regression=function(modell,lev=0.95,lty=1){"]
["plot(nach$Preis,nach$Flaschen)",{"$nulls":14}," vorh=predict.lm(modell,interval=c(\"confidence\"),level=lev,type=\"response\")"]
["m=lm(Flaschen~Preis,data=nach)",{"$nulls":14}," vorh=cbind(vorh,x=modell$model[[2]])"]
["abline(m)",{"$nulls":14}," vorh=vorh[order(vorh[,4]),]"]
["summary(m)",{"$nulls":14}," plot(modell$model[[2]],modell$model[[1]])"]
["library(varia)",{"$nulls":14}," abline(modell$coefficients[1],modell$coefficients[2])"]
["ppplotNorm(m$residuals)",{"$nulls":14}," lines(vorh[,4],vorh[,2],col=\"red\",lty=lty)"]
["confint(m)",{"$nulls":14}," lines(vorh[,4],vorh[,3],col=\"red\",lty=lty)"]
[{"$nulls":15},"  vorh"]
[{"$nulls":15},"}"]
[{"$nulls":15},"  "]
[{"$nulls":15},"  "]
[{"$nulls":15},"VI_band_Regression(m)"]
[]
[]
[{"$nulls":15},"Man erhält:"]
{"$rows_empty":21}
["Schlussfolgerungen:"]
[]
["Das Modell erklärt singifikant Varianz weg (p-Wert fast 0) (letzte Zeile!)"]
["Beide Parameter sind signifikant von 0 verschieden. "]
["Das geschätzte Modell ist:"]
["x(p)= -2.9661x+99.8585. "]
["(vermutlich könnte man mit -3 und 100 arbeiten. "]
[]
[]
["Damit haben wie eine Nachfrage-Preisfunktion pro Verkaufsfläche. "]
["In 500 Verkaufsfächen wird für einen spezifischen Preis 500 mal mehr nach"]
["gefragt."]
["Also erhält man"]
["xa(p)=500*(-2.9661*p+99.8585)",null,null,"=49929-1483.1*p"]
[]
["Die Preisnachfragefunktion ist dann:"]
["p(x)=33. 665-6.7426*10^(-4)* x"]
[]
["Die Erlösfunktion?"]
["E(x)= 33.665*x-6.7426*10^(-4)*x^2 "]
[]
["Kostenfunktion: Resultat mit R:"]
["x1=c(100,3000,15000,40000)"]
["k1=c(100.03,370.5973,33852.93,640107.5)"]
["mat1=cbind(1,x1,x1^2,x1^3)"]
["solve(mat1)%*%k1"]
["K(x)="]
[{"v":100,"d":"1.00E+02"},"+"]
[{"v":0.0002000428,"d":"2.00E-04"},"*x+"]
[{"v":-3.141959e-10,"d":"-3.14E-10"},"*x^2+"]
[{"v":1e-08,"d":"1.00E-08"},"*x^3"]
[]
["G(x)=E(x)-K(x)"]
["etc."]

{"sheet":"PISA_2003","index":16,"n_rows":28,"n_cols":3,"rows_kept":28,"flags":["formulas_cached_only"]}
["Pisa 2003"]
["Zusammenhang zwischen Ausgaben pro Schüler pro Land und mittlere Textverständniskompetenz"]
[]
[]
["Land","Ausgaben","Pisamittel"]
["Australia",44623,528]
["Austria",71387,507]
["Belgium",46338,507]
["Czech_Republic",21384,492]
["Denmark",65794,497]
["Finland",45363,546]
["France",50481,505]
["Germany",41978,484]
["Greece",27356,474]
["Hungary",20277,480]
["Ireland",31015,527]
["Italy",60824,487]
["Japan",53255,522]
["Korea",30844,525]
["Mexico",11239,422]
["Norway",61677,505]
["Poland",16154,479]
["Portugal",36521,470]
["Spain",36699,493]
["Sweden",53386,516]
["Switzerland",64266,494]
["United_Kingdom",42793,523]
["United_States",67313,504]

{"sheet":"LPisa_2003","index":17,"n_rows":28,"n_cols":1,"rows_kept":28,"flags":["formulas_cached_only"]}
["Pisa 2003 Lösungsvorschlag"]
[]
[]
[]
[]
["Lösungsvorschlag:"]
["pisa=read.table(\"e:/Pisa_Ausgaben_Leistung.txt\",header=T)"]
["plot(pisa$Ausgaben,pisa$Pisamittel)"]
[]
["m1=lm(Pisamittel~Ausgaben,data=pisa)"]
["summary(m1)"]
["abline(m1)"]
[]
["pisa$ausgaben2=pisa$Ausgaben^2"]
["pisa"]
[]
["m2=lm(Pisamittel~Ausgaben+ausgaben2,data=pisa)"]
["summary(m2)"]
[]
["curve(m2$coefficients[1]+m2$coefficients[2]*x+m2$coefficients[3]*x^2,add=T,col=\"red\")"]
[]
["anova(m1,m2)"]
["library(varia)"]
["par(mfrow=c(1,2))"]
["ppplotNorm(m1$residuals)"]
["ppplotNorm(m2$residuals)"]
["sum(m1$residuals^2)"]
["sum(m2$residuals^2)"]

{"sheet":"Skilifte","index":18,"n_rows":63,"n_cols":6,"rows_kept":63,"flags":["formulas_cached_only"]}
["Preise und Anzahl Pisten-Kilometer"]
["Ort","Pistlang","PreisTagPassFr","euro","PreisEuroproKm","cluster"]
["Nax",35,46,38.3,1.1,1]
["Graechen",42,54,45,1.07,1]
["Leukerbad",50,52,43.3,0.87,1]
["Anzere",52,52,43.3,0.83,1]
["Lauchernalp",55,53,44.2,0.8,1]
["Toggenburg",60,57,47.5,0.79,1]
["Soerenberg",52,49,40.8,0.79,1]
["Disentis",60,52,43.3,0.72,1]
["Belalp",60,52,43.3,0.72,1]
["Flumserberge",65,59,49.2,0.76,1]
["Haslital",60,46,38.3,0.64,1]
["Arolla",52,26.5,22.1,0.42,2]
["Leysin",40,23.5,19.6,0.49,2]
["Engelberg",82,62,51.7,0.63,1]
["Motta_Naluns",80,55,45.8,0.57,1]
["Chandolin",75,35,29.2,0.39,2]
["Davos_Klosters",112,66,55,0.49,2]
["Aletsch",104,55,45.8,0.44,2]
["Gotthard_Oberalp",125,57,47.5,0.38,2]
["Crans_Montana",140,63,52.5,0.38,2]
["Saas_Fee",150,69,57.5,0.38,2]
["Lenzerheide",155,69,57.5,0.37,2]
["Villars_Gryon",125,52,43.3,0.35,2]
["Grindelwald_Wengen",160,62,51.7,0.32,2]
["Jungfrau_Region",213,71,59.2,0.28,2]
["Arosa",225,69,57.5,0.26,2]
["Laax",235,71,59.2,0.25,2]
["Adelboden_Lenk",210,61,50.8,0.24,2]
["Gstaad",220,62,51.7,0.23,2]
["Silvretta",238,60,50,0.21,2]
["Zermatt_Cervinia",361,86,71.7,0.2,2]
["St_Moritz",350,73,60.8,0.17,2]
["Verbier",412,69,57.5,0.14,2]
["Portes_du_Soleil",650,57,47.5,0.07,2]
{"$rows_empty":24}
[{"$nulls":4},"install.packages(\"calibrate\")"]
[{"$nulls":4},"library(calibrate)"]
[{"$nulls":4},"textxy(skil$lnPistlang,skil$PreisTagPassFr,skil$Ort)"]

{"sheet":"LSkilifte","index":19,"n_rows":45,"n_cols":1,"rows_kept":45,"flags":["formulas_cached_only"]}
["Lösungsvorschlag:"]
["skil=read.table(\"F:/dateien/Mathematik/111Stat/Statistik_Unterlagen/Skilift_Beispiel/skil.txt\",header=T)"]
[]
[]
[]
["#'data.frame':   34 obs. of  4 variables:"]
["# $ Pistlang      : int  35 42 50 52 55 60 52 60 60 65 ..."]
["# $ PreisTagPassFr: num  46 54 52 52 53 57 49 52 52 59 ..."]
["# $ euro          : num  38.3 45 43.3 43.3 44.2 47.5 40.8 43.3 43.3 49.2 ..."]
["# $ PreisEuroproKm: num  1.1 1.07 0.87 0.83 0.8 0.79 0.79 0.72 0.72 0.76 ..."]
["# $ cluster       : int  1 1 1 1 1 1 1 1 1 1 ..."]
[]
[]
["plot(skil$Pistlang~skil$PreisTagPassFr)"]
[]
["#Deutlich nicht auf Gerade. Ein Polynom?"]
[]
["#Es ist sicher besser, wenn man ein paar Ausreisser wegnimmt. Um diese zu finden: "]
["#install.packages(\"calibrate\")"]
["library(calibrate)"]
["textxy(skil$Pistlang,skil$PreisTagPassFr,skil$Ort)"]
[]
["#Ausreisser wegnehmen:Chandolin, Leysin, Zermatt, Porte du Soleil, Arolla"]
["skil1=skil[c(-16,-34,-13,-12,-31),]  #Zeilen auf dataframe herausgesucht!"]
["plot(skil1$Pistlang,skil1$PreisTagPassFr)"]
[]
["#Sieht viel besser aus, aber immer noch gekrümmt."]
["#Zudem scheint die Varianz nicht überall gleich zu sein. Also logarithmieren, und zwar die x-Achse"]
["#nach oben gekrümmt würde man die y-Achse logarithmieren: "]
["#also: "]
["skil1$lnPistlang=log(skil1$Pistlang)"]
[]
["plot(skil1$lnPistlang,skil1$PreisTagPassFr)"]
["#sieht nun recht gut aus. "]
[]
["mln1=lm(PreisTagPassFr~lnPistlang,data=skil1)"]
["summary(mln1)"]
["shapiro.test(mln1$residuals)  #TEst auf Nomralverteilung: Residuen können als Normalverteilt betrachtet werden. p > 0.05)"]
["abline(mln1)"]
[]
["#Wie berechnet man mittleren Preis in Abhängigkeit von Pistenlänge?"]
["#Man möchte Wissen, wieviel der Preis für  32 km ist. "]
["#Man rechnet p(32)=log(32)*9.717+13.712"]
["#man erhält 47.3885 Franken. "]
["#Kann man brauchen für Preispolitik!!!!!"]

{"sheet":"meteoBoegg","index":20,"n_rows":102,"n_cols":12,"rows_kept":102,"flags":["formulas_cached_only"]}
["Zusatzübung Regression",{"$nulls":6},"Jahr","mittlere Sommertemperatur (Grad Celsius)","Anzahl Sommertage","Anzahl Hitzetage","Brennzeit Bögg in Minuten"]
["Meteo-Schweiz publiziert die folgende Abbildung: ",{"$nulls":6},2005,17.7,41,8,17.7]
[{"$nulls":7},2004,17.6,30,0,11.7]
[{"$nulls":7},2003,21.7,73,27,5.7]
[{"$nulls":7},2002,18.4,33,7,13]
[{"$nulls":7},2001,17.7,35,2,26.4]
[{"$nulls":7},2000,17.6,30,3,16.8]
[{"$nulls":7},1999,17.2,25,2,23.9]
[{"$nulls":7},1998,18.1,33,13,10.2]
[{"$nulls":7},1997,17.3,33,0,7.5]
[{"$nulls":7},1996,17,25,1,8]
[{"$nulls":7},1995,17.4,34,5,6]
[{"$nulls":7},1994,19.1,51,11,22]
[{"$nulls":7},1993,17,29,2,12]
[{"$nulls":7},1992,18.4,35,14,10.2]
[{"$nulls":7},1991,18,43,6,12]
[{"$nulls":7},1990,17.3,37,7,10.5]
[{"$nulls":7},1989,16.9,23,1,24]
[{"$nulls":7},1988,16.9,23,1,40]
[{"$nulls":7},1987,16.3,25,2,17]
[{"$nulls":7},1986,16.7,38,1,14]
[{"$nulls":7},1985,16.8,30,4,24]
[{"$nulls":7},1984,16.6,23,6,23]
[{"$nulls":7},1983,19,44,12,24.3]
["(aus:  Arbeitsbericht MeteoSchweiz Nr. 213, Saisonale Vorhersage in Tradition und Moderne:",{"$nulls":6},1982,17.4,30,1,13]
["Vergleich der „Sommerprognose“ des Zürcher Bööggs mit einem dynamischen Klimamodell",{"$nulls":6},1981,16.4,24,0,14.2]
["Daniela Schmuki und Andreas P. Weigel",null,null,", Bundesamt für Meteorologie und Klimatologie, ",{"$nulls":3},1980,15.5,18,1,17]
["Eidg. Departement des Inneren)",{"$nulls":6},1979,16.2,20,1,19]
[{"$nulls":7},1978,15.3,18,0,14]
[{"$nulls":7},1977,15.8,18,1,27]
[{"$nulls":7},1976,17.4,36,8,12]
[{"$nulls":7},1975,16.3,27,1,22]
[{"$nulls":7},1974,16.3,21,3,5.1]
[{"$nulls":7},1973,17.1,31,0,26]
[{"$nulls":7},1972,15.5,22,1,8]
["Ist die Berechnung einer Regressionsgereade bei diesen Daten gerechtfertigt?",{"$nulls":5},"1 Punkt",1971,16.7,27,5,5]
["Überprüfung graphischer Voraussetzungen (Testvoraussetzungen kommen später)",{"$nulls":6},1970,16.8,45,6,40]
[{"$nulls":7},1969,15.9,41,6,14]
[{"$nulls":7},1968,15.8,25,7,8]
[{"$nulls":7},1967,16.7,44,12,10]
["Wie würden Sie die Daten bearbeiten, um eventuell doch noch eine",{"$nulls":6},1966,15.6,25,3,10]
["Regressionsgerade berechnen zu können (Weglassen eines oder mehrerer Daten.",{"$nulls":6},1965,15.6,29,5,20]
["gilt auch als \"Bearbeitung\")"]
[]
[]
[]
[]
[]
["Berechnen Sie die Regressionsgerade für den neuen Datensatz (mit R oder SAS)",{"$nulls":5},"1 Punkt"]
[]
[]
[]
[]
[]
[]
["Trägt die Regressionsgerade zur Präzision der Schätzung der mittleren Temperatur des"]
["Sommers bei? (Geben Sie die entsprechenden Testresultate an):",{"$nulls":5},"1 Punkt"]
{"$rows_empty":7}
["Sind die Regressionskoeffizienten signifikant von 0 verschieden?",{"$nulls":5},"1 Punkt"]
{"$rows_empty":8}
["Überprüfen Sie die Voraussetzungen der durchgeführten Tests (3)",{"$nulls":5},"2 Punkte"]
{"$rows_empty":18}
["Ziehen Sie die Schlussfolgerungen aus den Ergebnissen: ",{"$nulls":5},"1 Punkt"]
["(Tests und Überprüfung der Voraussetzungen)"]
[]
[]
[]
[]
["Analysieren Sie den Zusammenhang der Anzahl Hitzetage und der mittleren Sommertemperatur"]
["(Graphische Voraussetzungen; Regressionsgerade; Tests; Schlussfolgerungen; unabhängige "]
["Variable: Anzahl Hitzetage)"]
["(Berechnen Sie alles, selbst wenn die Voraussetzungen nicht gegeben sind)",{"$nulls":5},"3 Punkte"]

{"sheet":"LMeteoBoegg","index":21,"n_rows":224,"n_cols":12,"rows_kept":224,"flags":["formulas_cached_only"]}
["Zusatzübung Regression",{"$nulls":3},"ergibt drei Zehntel zur letzten Prüfung",null,null,"Jahr","mittlere Sommertemperatur (Grad Celsius)","Anzahl Sommertage","Anzahl Hitzetage","Brennzeit Bögg in Minuten"]
["Meteo-Schweiz publiziert die folgende Abbildung: ",{"$nulls":3},"Max Punkte 11",null,null,2005,17.7,41,8,17.7]
[{"$nulls":7},2004,17.6,30,0,11.7]
[{"$nulls":7},2003,21.7,73,27,5.7]
[{"$nulls":7},2002,18.4,33,7,13]
[{"$nulls":7},2001,17.7,35,2,26.4]
[{"$nulls":7},2000,17.6,30,3,16.8]
[{"$nulls":7},1999,17.2,25,2,23.9]
[{"$nulls":7},1998,18.1,33,13,10.2]
[{"$nulls":7},1997,17.3,33,0,7.5]
[{"$nulls":7},1996,17,25,1,8]
[{"$nulls":7},1995,17.4,34,5,6]
[{"$nulls":7},1994,19.1,51,11,22]
[{"$nulls":7},1993,17,29,2,12]
[{"$nulls":7},1992,18.4,35,14,10.2]
[{"$nulls":7},1991,18,43,6,12]
[{"$nulls":7},1990,17.3,37,7,10.5]
[{"$nulls":7},1989,16.9,23,1,24]
[{"$nulls":7},1988,16.9,23,1,40]
[{"$nulls":7},1987,16.3,25,2,17]
[{"$nulls":7},1986,16.7,38,1,14]
[{"$nulls":7},1985,16.8,30,4,24]
[{"$nulls":7},1984,16.6,23,6,23]
[{"$nulls":7},1983,19,44,12,24.3]
["(aus:  Arbeitsbericht MeteoSchweiz Nr. 213, Saisonale Vorhersage in Tradition und Moderne:",{"$nulls":6},1982,17.4,30,1,13]
["Vergleich der „Sommerprognose“ des Zürcher Bööggs mit einem dynamischen Klimamodell",{"$nulls":6},1981,16.4,24,0,14.2]
["Daniela Schmuki und Andreas P. Weigel",null,null,", Bundesamt für Meteorologie und Klimatologie, ",{"$nulls":3},1980,15.5,18,1,17]
["Eidg. Departement des Inneren)",{"$nulls":6},1979,16.2,20,1,19]
[{"$nulls":7},1978,15.3,18,0,14]
[{"$nulls":7},1977,15.8,18,1,27]
[{"$nulls":7},1976,17.4,36,8,12]
[{"$nulls":7},1975,16.3,27,1,22]
[{"$nulls":7},1974,16.3,21,3,5.1]
[{"$nulls":7},1973,17.1,31,0,26]
[{"$nulls":7},1972,15.5,22,1,8]
["Ist die Berechnung einer Regressionsgereade bei diesen Daten gerechtfertigt?",{"$nulls":5},"1 Punkt",1971,16.7,27,5,5]
["der rote Ausreisser beeinflusst die Steigung der Geraden vermutlich stark. Er ist zu entfernen",{"$nulls":6},1970,16.8,45,6,40]
["Sonst liegen die Daten ungefähr auf Geraden. Punktsymmetrie ist in etwa gegeben. ",{"$nulls":6},1969,15.9,41,6,14]
["beide Variablen sind metrisch skaliert. ",{"$nulls":6},1968,15.8,25,7,8]
[{"$nulls":7},1967,16.7,44,12,10]
["Wie würden Sie die Daten bearbeiten, um eventuell doch noch eine",{"$nulls":6},1966,15.6,25,3,10]
["Regressionsgerade berechnen zu können (Weglassen eines oder mehrerer Daten.",{"$nulls":6},1965,15.6,29,5,20]
["gilt auch als \"Bearbeitung\")",{"$nulls":5},"1 Punkt"]
["Wir lassen den Sommer 2003 weg. "]
["(mit r: finden wir diesen druch \"which(boegg$Sommertemperatur=max(boegg$Sommertemperatur)"]
["(gibt im Dataframe die Stelle an, wo die maximale Sommertemperatur vorkommt)."]
["Neuer Datensatz durch: b1=boegg[-3,]",{"$nulls":3},"(denn 2003 steht in der 3. Zeile!!!)"]
[]
["Berechnen Sie die Regressionsgerade für den neuen Datensatz (mit R oder SAS)",{"$nulls":5},"1 Punkt"]
["Coefficients:"]
["   (Intercept)  Brennzeit_Bögg  "]
["     16.838692        0.005698 "]
[" boeglm=lm(Sommertemperatur~Brennzeit_Boegg,data=b1)"]
[]
[]
["Trägt die Regressionsgerade zur Präzision der Schätzung der mittleren Temperatur des"]
["Sommers bei? (Geben Sie die entsprechenden Testresultate an):",{"$nulls":5},"1 Punkt"]
["> anova(boeglm)"]
["Analysis of Variance Table"]
["Response: Sommertemperatur"]
["               Df Sum Sq Mean Sq F value Pr(>F)"]
["Brennzeit_Boegg  1  0.090   0.090   0.097 0.7572"]
["Residuals      38 35.078   0.923   ",{"$nulls":3},"trägt nicht signifikant bei!!!"]
[]
["Sind die Regressionskoeffizienten signifikant von 0 verschieden?",{"$nulls":5},"1 Punkt"]
["Coefficients:"]
["                Estimate Std. Error t value Pr(>|t|)    "]
["(Intercept)    16.838692   0.337384  49.910   <2e-16 ***"]
["Brennzeit_Boegg  0.005698   0.018299   0.311    0.757    "]
["Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 "]
["Ordinatenabschnitt ja, Steigung nein. Trägt also nichts zur Schätzung der Sommertemperataur "]
["bei (Ordinatenabschnitt  ungefähr = mittlere Sommertemperatur über alle Jahre!!!!)"]
[]
["Überprüfen Sie die Voraussetzungen der durchgeführten Tests (3)",{"$nulls":5},"2 Punkte"]
["Normalverteilung der Residuen:  ppplotNorm(resid(boeglm))",{"$nulls":4},"Varianzgleichheit:"]
{"$rows_empty":11}
["ist gegeben."]
[]
["da es sich um Zeitreihen handelt, ",null,null,"Scheint graphisch gegeben zu sein (trotz zwei"]
["ist i.i.d. u.U. nicht gegeben.",null,null,"Punkten oben in der Mitte (die wohl Ausreisser sind)"]
["(für Zeitreihen gibt es eigene Verfahren)",null,null,"Regressionsgerade durch abline(boeglm))"]
[]
["Ziehen Sie die Schlussfolgerungen aus den Ergebnissen: ",{"$nulls":5},"1 Punkt"]
["(Tests und Überprüfung der Voraussetzungen)"]
["Voraussetzungen sind erfüllt (bis eventuell auf i.i.d.) "]
["Man kann die Sommertemperatur nicht mit Hilfe der Brenndauer des Böggs voraussagen."]
[]
[]
["Analysieren Sie den Zusammenhang der Anzahl Hitzetage und der mittleren Sommertemperatur"]
["(Graphische Voraussetzungen; Regressionsgerade; Tests; Schlussfolgerungen)",{"$nulls":5},"3 Punkte"]
[]
[{"$nulls":3},"a) beide metrisch skaliert o.k."]
[{"$nulls":3},"b) ungefähr auf Gerade, o.k."]
[{"$nulls":3},"c) Punktsymmetrie, nein"]
{"$rows_empty":8}
[" b2=lm(b1$Hitzetage~b1$Sommertemperatur)"]
["> b2"]
[]
["Call:"]
["lm(formula = b1$Hitzetage ~ b1$Sommertemperatur)"]
[]
["Coefficients:"]
["        (Intercept)  b1$Sommertemperatur  "]
["            -34.152                2.266 "]
[]
["> anova(b2)"]
["Analysis of Variance Table"]
[]
["Response: b1$Hitzetage"]
["                    Df Sum Sq Mean Sq F value    Pr(>F)    "]
["b1$Sommertemperatur  1 180.66  180.66  15.520 0.0003377 ***"]
["Residuals           38 442.32   11.64                      "]
["---"]
["Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 "]
[]
["Würde bei Geltung der Voraussetzungen signifikant zur Präzision der Schätzung beitragen."]
[]
["> summary(b2)"]
["Call:"]
["lm(formula = b1$Hitzetage ~ b1$Sommertemperatur)"]
[]
["Residuals:"]
["    Min      1Q  Median      3Q     Max "]
["-5.7379 -2.7612 -0.4048  2.0454  8.3020 "]
[]
["Coefficients:"]
["                    Estimate Std. Error t value Pr(>|t|)    "]
["(Intercept)         -34.1524     9.7564  -3.501 0.001203 ** "]
["b1$Sommertemperatur   2.2665     0.5753   3.940 0.000338 ***"]
["---"]
["Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 "]
[]
["Residual standard error: 3.412 on 38 degrees of freedom"]
["Multiple R-squared:  0.29,      Adjusted R-squared: 0.2713 "]
["F-statistic: 15.52 on 1 and 38 DF,  p-value: 0.0003377 "]
[]
["Koeffizienten sind signifikant von 0 verschieden."]
["ppplotNorm(resid(b2))",null,"Analyse der Testvoraussetzungen:"]
[]
[]
[]
[]
[]
[]
[null,null,"Es fällt auf, dass die Punkte rechts nicht um die Regressions-"]
[null,null,"gerade liegen. Vermutlich müsste man ein Polynom höheren "]
[null,null,"Grades anpassen (z.B. f(x) = b + ax1 + bx^2."]
[null,null,"Varianzgleichheit: kaum haltbar (s. Graphik)"]
["Normalverteilung der Residuen ist haltbar, i.i.d (s. oben Zeitreihen)"]
[]
[]
["Eine Regressionkurve zweiten Grades rechnet man, indem man eine"]
["das Quadrat der Variable Sommertemperatur hinzufügt. (z.B. b1$x2=b1$Sommertemperatur^2"]
["dann gibt man"]
["b3=lm(Hitzetage~Sommertemperatur+x2,data=b1)"]
["ein. "]
["Man erhlät mit summary(b3):"]
["Coefficients:"]
["                 Estimate Std. Error t value Pr(>|t|)  "]
["(Intercept)      290.9222   135.7914   2.142   0.0388 *"]
["Sommertemperatur -35.9367    15.9310  -2.256   0.0301 *"]
["x2                 1.1190     0.4663   2.399   0.0216 *"]
["---"]
["Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 "]
[]
["Residual standard error: 3.216 on 37 degrees of freedom"]
["Multiple R-squared: 0.3856,     Adjusted R-squared: 0.3524 "]
["F-statistic: 11.61 on 2 and 37 DF,  p-value: 0.0001220 "]
[]
["Alle Koeffizienten weichen signifikant von 0 ab. "]
["Die Kurve erklärt signifikant viel Varianz weg.",{"$nulls":3},"mit:"]
[{"$nulls":4}," plot(b1$Sommertemperatur,b1$Hitzetage)"]
[{"$nulls":4},"curve(290.922-35.947*x+1.119*x^2,add=T)"]
{"$rows_empty":17}
["Die Varianzgleichheit ist nun besser gewahrt. "]
[]
["Die Residuen scheinen allerdings von der Normalverteilungsabnahme abzuweichen:"]
{"$rows_empty":11}
["Damiit sind die p-Werte mit Vorsicht aufzunehmen, da die Verteilung der"]
["jeweiligen Teststatistiken nur unter der Voraussetzung der Normalverteilung"]
["der Residuen gegeben ist."]

{"sheet":"Social_Index","index":22,"n_rows":130,"n_cols":7,"rows_kept":130,"flags":["formulas_cached_only"]}
[null,"Country Code",null,"Social Progress Index 2014","GDP (Gross Domestic Product) 2012",null,"Versuchen Sie, den Zusammenhang zwischen "]
["Angola","AGO",null,39.93,7345.681974525645,null,"GDP und dem Social Progress Index zu beschreiben."]
["Albania","ALB",null,69.13,9243.366780872211,null,"Quellen der Daten:"]
["United Arab Emirates","ARE",null,72.92,57044.5776548759,null,"http://www.socialprogressimperative.org/data/spi#data_table/countries/spi/dim1,dim2,dim3"]
["Argentina","ARG",null,70.59,"NA",null,"http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD"]
["Armenia","ARM",null,65.03,7290.639071332968]
["Australia","AUS",null,86.1,42278.29673173857]
["Austria","AUT",null,85.11,43138.86602298981]
["Azerbaijan","AZE",null,62.44,15888.217965793956]
["Burundi","BDI",null,37.33,736.8432416620566]
["Belgium","BEL",null,82.63,39497.540008737466]
["Benin","BEN",null,49.11,1686.5466351519583]
["Burkina Faso","BFA",null,47.33,1527.8559085991408]
["Bangladesh","BGD",null,52.04,2363.8332404006387]
["Bulgaria","BGR",null,70.24,15738.340143420475]
["Bosnia and Herzegovina","BIH",null,64.99,9183.519291442984]
["Belarus","BLR",null,65.2,16868.46657352841]
["Bolivia","BOL",null,62.9,5650.430750211654]
["Brazil","BRA",null,69.97,14300.581135839097]
["Botswana","BWA",null,65.6,14443.389629547995]
["Central African Republic","CAF",null,34.17,963.9618815131499]
["Canada","CAN",null,86.95,40588.04493428159]
["Switzerland","CHE",null,88.19,51292.59314567861]
["Chile","CHL",null,76.3,21099.21378840352]
["China","CHN",null,58.67,10771.18924346894]
["Cameroon","CMR",null,45.51,2550.9253014880196]
["Congo, Republic of","COG",null,47.99,5631.301518423544]
["Colombia","COL",null,67.24,11687.245366380163]
["Costa Rica","CRI",null,77.75,13091.09386130088]
["Cuba","CUB",null,61.07,"NA"]
["Czech Republic","CZE",null,80.41,26733.405259571027]
["Germany","DEU",null,84.61,41966.41504338603]
["Denmark","DNK",null,86.55,41524.33942230911]
["Dominican Republic","DOM",null,63.03,11015.727797169251]
["Algeria","DZA",null,59.13,12778.547731477422]
["Ecuador","ECU",null,68.15,9900.075212206879]
["Egypt","EGY",null,59.97,10685.046008678966]
["Spain","ESP",null,80.77,31198.413286081697]
["Estonia","EST",null,81.28,24194.65281653033]
["Finland","FIN",null,86.91,38103.753109388075]
["France","FRA",null,81.11,36073.62839980803]
["United Kingdom","GBR",null,84.56,34693.91066573828]
["Georgia","GEO",null,63.94,6690.880228415904]
["Ghana","GHA",null,55.96,3638.4727393388985]
["Guinea","GIN",null,37.41,1216.0498273910935]
["Greece","GRC",null,73.43,25391.354890385966]
["Guatemala","GTM",null,61.37,6989.518390455089]
["Guyana","GUY",null,60.06,6053.658710718089]
["Honduras","HND",null,61.28,4422.54195587013]
["Croatia","HRV",null,73.31,19946.214936103344]
["Hungary","HUN",null,73.87,22146.127669554386]
["Indonesia","IDN",null,58.98,8856.2012437266]
["India","IND",null,50.24,5050.10745293848]
["Ireland","IRL",null,84.05,42918.608245685245]
["Iran","IRN",null,56.65,15461.25482123368]
["Iraq","IRQ",null,44.84,14527.309490992322]
["Iceland","ISL",null,88.07,38553.292055027865]
["Israel","ISR",null,71.4,30599.770727650102]
["Italy","ITA",null,76.93,33668.15842844796]
["Jamaica","JAM",null,70.39,8421.419418160496]
["Jordan","JOR",null,61.92,11340.243873881489]
["Japan","JPN",null,84.21,35006.22227389452]
["Kazakhstan","KAZ",null,59.47,21505.651217332554]
["Kenya","KEN",null,50.2,2108.9279546888783]
["Kyrgyzstan","KGZ",null,57.08,2846.528682271742]
["Cambodia","KHM",null,51.89,2789.0781434968903]
["Korea, Republic of","KOR",null,77.18,29494.910204547257]
["Laos","LAO",null,52.41,4388.170192065211]
["Lebanon","LBN",null,60.05,16509.006941758777]
["Liberia","LBR",null,44.02,782.3872542507102]
["Sri Lanka","LKA",null,59.71,8862.155994392082]
["Lesotho","LSO",null,48.94,2367.966231113271]
["Lithuania","LTU",null,73.76,23553.892151336066]
["Latvia","LVA",null,73.91,21229.204239562947]
["Morocco","MAR",null,58.01,6877.97114676275]
["Moldova","MDA",null,60.12,4146.325006466185]
["Madagascar","MDG",null,44.28,1377.8075164061756]
["Mexico","MEX",null,66.41,16143.789735475975]
["Macedonia","MKD",null,68.33,11707.980834238631]
["Mali","MLI",null,46.85,1606.9306885152196]
["Mongolia","MNG",null,58.97,8288.362985162566]
["Mozambique","MOZ",null,45.23,971.2128074879256]
["Mauritania","MRT",null,43.11,2938.148093282832]
["Mauritius","MUS",null,73.68,16193.810455222929]
["Malawi","MWI",null,48.79,738.9554007676796]
["Malaysia","MYS",null,70,21897.320805389314]
["Namibia","NAM",null,61.19,9135.850388255481]
["Niger","NER",null,40.1,883.9819456515075]
["Nigeria","NGA",null,42.65,5439.620585863992]
["Nicaragua","NIC",null,62.33,4254.093223542782]
["Netherlands","NLD",null,87.37,42452.580651511504]
["Norway","NOR",null,87.12,62858.00894627328]
["Nepal","NPL",null,51.58,2130.6283926214755]
["New Zealand","NZL",null,88.24,32360.232192429776]
["Pakistan","PAK",null,42.4,4360.347169621992]
["Panama","PAN",null,72.58,16655.206679571278]
["Peru","PER",null,66.29,11602.584393071302]
["Philippines","PHL",null,65.86,6004.778670717849]
["Poland","POL",null,77.44,22142.64840844068]
["Portugal","PRT",null,80.49,25096.318187580662]
["Paraguay","PRY",null,62.65,7215.4844179554675]
["Romania","ROU",null,67.72,17233.790009597582]
["Russia","RUS",null,60.79,23183.98716750331]
["Rwanda","RWA",null,49.46,1378.6337311737414]
["Saudi Arabia","SAU",null,64.38,50790.68237893644]
["Sudan","SDN",null,38.45,3370.09230636828]
["Senegal","SEN",null,53.52,2173.5232544207192]
["El Salvador","SLV",null,64.7,7445.296434751931]
["Serbia","SRB",null,70.61,11586.784989075504]
["Slovakia","SVK",null,78.93,25537.243881163533]
["Slovenia","SVN",null,81.65,27394.442750583632]
["Sweden","SWE",null,87.08,41839.67738184087]
["Swaziland","SWZ",null,48.87,5912.3303446621285]
["Chad","TCD",null,32.6,2003.3960144419846]
["Togo","TGO",null,42.8,1285.836469958722]
["Thailand","THA",null,65.14,13586.083422205022]
["Tajikistan","TJK",null,56.05,2320.327120295904]
["Trinidad and Tobago","TTO",null,69.88,29085.822080004546]
["Tunisia","TUN",null,62.96,10611.902744403335]
["Turkey","TUR",null,64.62,18167.453918187824]
["Tanzania","TZA",null,46.06,1654.48594846601]
["Uganda","UGA",null,47.75,1334.139169831908]
["Ukraine","UKR",null,64.91,8332.323198973603]
["Uruguay","URY",null,77.51,17965.51024085285]
["United States","USA",null,82.77,50859.394208178506]
["Uzbekistan","UZB",null,57.34,4704.529253157376]
["Venezuela","VEN",null,63.78,17642.39700577904]
["Yemen","YEM",null,40.23,3996.3936742372657]
["South Africa","ZAF",null,62.96,11988.7926714564]
["Zambia","ZMB",null,49.88,2990.0538103930244]

{"sheet":"LSocial_Index","index":23,"n_rows":34,"n_cols":7,"rows_kept":34,"flags":["formulas_cached_only"]}
["gdp=read.table(\"c:/var/gdp_SI.txt\",header=T)"]
["head(gdp)  #SPI  GDP",{"$nulls":5},"Zusammenhang gemäss NZZ, 31.Mai 2014, S. 29 (Geld macht doch glücklich)"]
["plot(gdp$GDP,gdp$SPI)"]
[]
["gdp$lnGDP=log(gdp$GDP)"]
["head(gdp)"]
[]
["plot(gdp$lnGDP,gdp$SPI)"]
["m1=lm(SPI~lnGDP,data=gdp)"]
["abline(m1)"]
["summary(m1)"]
["library(varia)"]
["ppplotNorm(m1$residuals)"]
[]
["Die Residuen sind nicht wirklich normalverteilt. "]
["Sicherheitshalber für die Tests "]
["Bootstrap-Methode anwenden."]
[]
[]
["Man erhält eine zur beiliegenden Graphik ähnliche Darstellung mit: "]
[]
[]
["mspi=lm(gdp$SPI~gdp$lnGDP)"]
["plot(gdp$SPI~gdp$GDP)"]
["f=function(x) mspi$coefficients[1]+mspi$coefficients[2]*log(x)"]
["curve(f,add=T,col=\"red\")"]
[]
[]
[]
[]
[{"$nulls":6},"Problem: Schaut man den Social Progress Index (s. nächste Seiten) an, sieht man, dass dort alles mögliche verrechnet wird. "]
[{"$nulls":6},"(metrische Skalen in verschiedenen Einheiten, Ordinalskalen, etc). Es stellt sich dabei das "]
[{"$nulls":6},"Problem der Gewichtung der verschiedenen Aspekte, die in den Index einfliessen. Diese Problem"]
[{"$nulls":6},"stellen sich bei allen Indices dieser Art (z.B. Innovationsindex, Wettbewerbsfähigkeitsindex, Demokratieindex, etc. etc.)"]

{"sheet":"Rohdaten_Social_index","index":24,"n_rows":133,"n_cols":76,"rows_kept":133,"flags":["formulas_cached_only"]}
[null,"Country Code",null,"Social Progress Index",null,"Basic Human Needs","Foundations of Wellbeing","Opportunity",null,"Nutrition and Basic Medical Care","Water and Sanitation","Shelter","Personal Safety","Access to Basic Knowledge","Access to Information and Communications","Health and Wellness","Ecosystem Sustainability","Personal Rights","Personal Freedom and Choice","Tolerance and Inclusion","Access to Advanced Education",null,"Undernourishment (% of pop.)","Depth of food deficit (calories/undernourished person)","Maternal mortality rate (deaths/100,000 live births)","Stillbirth rate (deaths/1,000 live births)","Child mortality rate (deaths/1,000 live births)","Deaths from infectious diseases (deaths/100,000)","Access to piped water (% of pop.)","Rural vs. urban access to improved water source (absolute difference between % of pop.)","Access to improved sanitation facilities (% of pop.)","Availability of affordable housing (% satisfied)","Access to electricity (% of pop.)","Quality of electricity supply (1=low; 7=high)","Indoor air pollution attributable deaths (deaths/100,000)","Homicide rate (1= <2/100,000; 5= >20/100,000","Level of violent crime (1=low; 5=high)","Perceived criminality (1=low; 5=high)","Political terror (1=low; 5=high)","Traffic deaths (deaths/100,000)","Adult literacy rate (% of pop. aged 15+)","Primary school enrollment (% of children)","Lower secondary school enrollment (% of children)","Upper secondary school enrollment (% of children)","Gender parity in secondary enrollment (girls/boys)","Mobile telephone subscriptions (subscriptions/100 people)","Internet users (% of pop.)","Press Freedom Index (0=most free; 100=least free)","Life expectancy (years)","Non-communicable disease deaths between the ages of 30 and 70 (probability of dying)","Obesity rate (% of pop.)","Outdoor air pollution attributable deaths (deaths/100,000)","Suicide rate (deaths/100,000)","Greenhouse gas emissions (CO2 equivalents per GDP)","Water withdrawals as a percent of resources","Biodiversity and habitat (0=no protection; 100=high protection)","Political rights (1=full rights; 7=no rights)","Freedom of speech (0=low; 2=high)","Freedom of assembly/association (0=low; 2=high)","Freedom of movement (0=low; 4=high)","Private property rights (0=none; 100=full)","Freedom over life choices (% satisfied)","Freedom of religion (1=low; 4=high)","Modern slavery, human trafficking and child marriage (1=low; 100=high)","Satisfied demand for contraception (% of women)","Corruption (0=high; 100=low)","Women treated with respect (0=low; 100=high)","Tolerance for immigrants (0=low; 100=high)","Tolerance for homosexuals (0=low; 100=high)","Discrimination and violence against minorities (0=low; 10=high)","Religious tolerance (1=low; 4=high)","Community safety net (0=low; 100=high)","Years of tertiary schooling","Women's average years in school","Inequality in the attainment of education (0=low; 1=high)","Number of globally ranked universities (0=none; 5= >50)"]
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["Uruguay","URY",null,77.51,null,85.79,72.18,74.56,null,94.81,97.95,78.39,72.01,95.05,{"v":74.9,"d":"74.90"},71.98,46.81,{"v":92.8,"d":"92.80"},85.19,78.33,41.91,null,"<5.0",32,29,9,{"v":7.2,"d":"7"},55,{"v":99.3709186,"d":"99.4"},{"v":2.3834760000000017,"d":"2.4"},{"v":98.8805056,"d":"98.9"},40,99,{"v":5.62038267861272,"d":"5.6"},{"v":3.85676,"d":"3.9"},2,2,3,1,21.5,{"v":98.07271,"d":"98.1"},{"v":99.53355,"d":"99.5"},{"v":110.4361,"d":"110.4"},{"v":70.62888,"d":"70.6"},{"v":1.13729,"d":"1.1"},{"v":147.13068510653,"d":"147.1"},{"v":55.1146,"d":"55.1"},{"v":15.92,"d":"15.9"},{"v":76.76426829268293,"d":"76.8"},20,23.6,19,{"v":13.6894,"d":"13.7"},{"v":817.948146825203,"d":"817.9"},{"v":0.863199369,"d":"0.9"},{"v":24.62,"d":"24.6"},1,2,2,4,70,85,4,{"v":9.511661809045226,"d":"9.5"},91.5,73,47,82,76,2.8,4,85,0.3,11.6,{"v":0.10800000000000001,"d":"0.11"},0]
["Uzbekistan","UZB",null,57.34,null,75.88,53.28,42.87,null,92.01,73.93,73.81,63.76,94.29,42.11,68.36,8.34,11.35,61.69,48.37,50.07,null,6.1,41,28,6,{"v":39.6,"d":"40"},104,{"v":47.3783375,"d":"47.4"},{"v":17.5607732,"d":"17.6"},{"v":100,"d":"100.0"},71,100,null,{"v":47.5422,"d":"47.5"},2,3,3,3,11.3,{"v":99.43299,"d":"99.4"},{"v":88.54244,"d":"88.5"},{"v":94.52605,"d":"94.5"},{"v":129.3383,"d":"129.3"},{"v":0.97842,"d":"1.0"},{"v":71.0339144617982,"d":"71.0"},{"v":36.5213,"d":"36.5"},{"v":60.39,"d":"60.4"},{"v":67.98039024390245,"d":"68.0"},32,17.3,12,{"v":9.933,"d":"9.9"},{"v":2796.9566297842,"d":"2797.0"},{"v":4.316671945,"d":"4.3"},{"v":12.87,"d":"12.9"},7,0,1,0,15,88,1,{"v":15.669884673366836,"d":"15.7"},87.7,17,96,51,5,7.5,3,92,null,12.3,{"v":0.013999999999999999,"d":"0.01"},0]
["Venezuela","VEN",null,63.78,null,65.47,75.02,50.86,null,92.89,{"v":81.2,"d":"81.20"},60.26,27.55,91.16,65.89,74.44,68.58,36.49,66.89,61.45,38.62,null,"<5.0",16,92,11,{"v":15.3,"d":"15"},71,{"v":86.7802031,"d":"86.8"},{"v":19.065468600000003,"d":"19.1"},{"v":90.8733156,"d":"90.9"},50,100,{"v":1.78094054193548,"d":"1.8"},{"v":1.65921,"d":"1.7"},5,4,5,3,37.2,{"v":95.51199,"d":"95.5"},{"v":92.27759,"d":"92.3"},{"v":91.8574,"d":"91.9"},{"v":75.54412,"d":"75.5"},{"v":1.09376,"d":"1.1"},{"v":101.881639465779,"d":"101.9"},{"v":44.0456,"d":"44.0"},{"v":34.44,"d":"34.4"},{"v":74.33097560975611,"d":"74.3"},16,30.8,11,{"v":8.32688,"d":"8.3"},{"v":829.551888168614,"d":"829.6"},{"v":2.299918259,"d":"2.3"},{"v":92.92,"d":"92.9"},5,0,1,4,5,78,3,{"v":9.343821984924624,"d":"9.3"},85.5,20,59,67,51,6.4,3,93,0.39,10.7,{"v":0.18100000000000002,"d":"0.18"},0]
["Yemen","YEM",null,40.23,null,{"v":48.5,"d":"48.50"},47.88,24.31,null,70.27,50.76,28.93,44.04,58.01,31.67,72.37,29.46,15.79,54.82,21.89,4.72,null,32.4,215,200,23,60,232,{"v":40.1872549,"d":"40.2"},{"v":25.4445834,"d":"25.4"},{"v":53.0057283,"d":"53.0"},30,45,{"v":1.454153372,"d":"1.5"},{"v":67.4799,"d":"67.5"},2,3.5,4,4.5,23.7,{"v":65.26195,"d":"65.3"},{"v":86.27468,"d":"86.3"},{"v":56.60537,"d":"56.6"},{"v":36.94565,"d":"36.9"},{"v":0.65423,"d":"0.7"},{"v":58.2750362866912,"d":"58.3"},{"v":17.4465,"d":"17.4"},{"v":69.22,"d":"69.2"},{"v":62.71848780487806,"d":"62.7"},27,16.7,7,{"v":2.70545,"d":"2.7"},{"v":507.264529471813,"d":"507.3"},{"v":4.674046102,"d":"4.7"},{"v":23.59,"d":"23.6"},6,0,0,1,30,67,2,{"v":7.277719346733669,"d":"7.3"},53.4,18,68,25,null,{"v":9,"d":"9.0"},1,68,0.1,1.8,{"v":0.498,"d":"0.50"},0]
["Zambia","ZMB",null,49.88,null,38.57,63.67,47.41,null,49.33,33.23,23.05,48.67,79.52,47.45,64.63,63.09,{"v":53.8,"d":"53.80"},62.94,49.18,23.73,null,47.4,345,440,26,{"v":88.5,"d":"89"},961,{"v":15.016632,"d":"15.0"},{"v":35.9196613,"d":"35.9"},{"v":42.0764842,"d":"42.1"},41,19,{"v":3.10312336424581,"d":"3.1"},{"v":103.411,"d":"103.4"},5,3,3,3,23.8,{"v":61.42829,"d":"61.4"},{"v":93.71627,"d":"93.7"},{"v":150.469,"d":"150.5"},{"v":63.8223,"d":"63.8"},{"v":0.87467,"d":"0.9"},{"v":74.7751472298703,"d":"74.8"},{"v":13.4682,"d":"13.5"},{"v":27.93,"d":"27.9"},{"v":55.83314634146342,"d":"55.8"},34,4.2,15,{"v":14.6945,"d":"14.7"},{"v":3734.91497776469,"d":"3734.9"},{"v":0.07768468,"d":"0.1"},{"v":100,"d":"100.0"},3,1,1,3,30,78,3,{"v":19.827660804020102,"d":"19.8"},63.1,38,61,68,5,{"v":6,"d":"6.0"},3,78,0.05,6.7,{"v":0.23800000000000002,"d":"0.24"},0]

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["All data in the 2014 Social Progress Index is the most recent available as of February 6, 2014."]
["Dimension","Component","Indicator name","Definition","Source","Link","Instructions for finding data (if applicable)","Additional notes"]
["Basic Human Needs","Nutrition and Basic Medical Care","Undernourishment (% of pop.)","The percentage of the population whose food intake is insufficient to meet dietary energy requirements continuously. Data showing as 5% signifies a prevalence of undernourishment at or below 5%.","Food and Agriculture Organization of the United Nations","http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world-development-indicators ","(select all countries and prevalence of undernourishment) \nDimension One: Country. Select all countries. \nDimension Two: Series. On left panel, choose topic, then Health, then Mortality. Indicator is titled “Prevalence of Undernourishment.” \nDimension Three: Time. Select all years.\n","Iraq:\nFood and Agriculture Organization of the United Nations.\nhttp://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.UvOf0flkRxA\nDownload data. Prevalence of undernourishment is on sheet V_7.1\n"]
["Basic Human Needs","Nutrition and Basic Medical Care","Depth of food deficit (calories/undernourished person)","The number of calories needed to lift the undernourished from their status, everything else being constant. The average intensity of food deprivation of the undernourished, estimated as the difference between the average dietary energy requirement and the average dietary energy consumption of the undernourished population (food-deprived), is multiplied by the number of undernourished to provide an estimate of the total food deficit in the country, which is then normalized by the total population.","Food and Agriculture Organization of the United Nations","http://data.worldbank.org/indicator/SN.ITK.DFCT "]
["Basic Human Needs","Nutrition and Basic Medical Care","Maternal mortality rate (deaths/100,000 live births)","The annual number of female deaths from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, per 100,000 live births.","World Health Organization","http://apps.who.int/gho/data/node.main.15?lang=en "]
["Basic Human Needs","Nutrition and Basic Medical Care","Stillbirth rate (deaths/1,000 live births)","Third trimester fetal deaths (> 1000 grams or > 28 weeks), per 1,000 live births.","World Health Organization","http://apps.who.int/gho/data/node.main.ChildMort-2?lang=en"]
["Basic Human Needs","Nutrition and Basic Medical Care","Child mortality rate (deaths/1,000 live births)","The probability of a child born in a specific year dying before reaching the age of five per 1,000 live births.  ","UN Inter-agency Group for Child Mortality Estimation","http://data.worldbank.org/indicator/SH.DYN.MORT"]
["Basic Human Needs","Nutrition and Basic Medical Care","Deaths from infectious diseases (deaths/100,000)","Age-standardized mortality rate from deaths caused by tuberculosis, sexually transmitted diseases, HIV/AIDS, diarrhea, pertussis, polio,  measles, tetanus, meningitis, hepatitis B, hepatitis C, malaria, trypanosomiasis, Chagas disease, schistosomiasis, leishmaniasis, lymphatic filariasis, onchocerciasis, leprosy, dengue, Japanese encephaltitis, trachoma, intestinal infections, and other infectious diseases per 100,000 people.","World Health Organization","http://apps.who.int/gho/data/node.main.18?lang=en","Age-standardized mortality rate by cause (per 100 000 population) - Communicable"]
["Basic Human Needs","Water and Sanitation","Access to piped water (% of pop.)","The percentage of the population with a water service pipe connected with in-house plumbing to one or more taps or a piped water connection to a tap placed in the yard or plot outside the house. ","WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation ","http://www.wssinfo.org/data-estimates/table/"]
["Basic Human Needs","Water and Sanitation","Rural vs. urban access to improved water source (absolute difference between % of pop.)","The absolute value of the  difference between rural and urban access to improved drinking water, which is defined as the percentage of the population with piped water into dwelling, piped water to yard/plot, public tap or standpipe, tubewell or borehole, protected dug well, protected spring, or rainwater.","Social Progress Imperative calculation using WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation data","http://www.wssinfo.org/data-estimates/table/"]
["Basic Human Needs","Water and Sanitation","Access to improved sanitation facilities (% of pop.)","The percentage of the population with improved sanitation, including flush toilets, piped sewer systems, septic tanks, flush/pour flush to pit latrine, ventilated improved pit latrines (VIP), pit latrine with slab, and composting toilets.","WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation ","http://www.wssinfo.org/data-estimates/table/"]
["Basic Human Needs","Shelter","Availability of affordable housing (% satisfied)","The percentage of respondents answering satisfied to the question, “In your city or area where you live, are you satisfied or dissatisfied with the availability of good, affordable housing?”","Gallup World Poll"]
["Basic Human Needs","Shelter","Access to electricity (% of pop.)","The percentage of the population with access to electricity.","United Nations Sustainable Energy for All Project","http://www.se4all.org/wp-content/uploads/2013/09/11-gtf_data_annex.pdf"]
["Basic Human Needs","Shelter","Quality of electricity supply (1=low; 7=high)","Average response to the question: “In your country, how would you assess the reliability of the electricity supply (lack of interruptions and lack of voltage fluctuations)? “[1 = not reliable at all; 7 = extremely reliable]","World Economic Forum Global Competitiveness Report","http://www.weforum.org/issues/competitiveness-0/gci2012-data-platform/","Select “Data Download” tab. Then select series 2.07, Quality of electricity and Edition 2013-2014. "]
["Basic Human Needs","Shelter","Indoor air pollution attributable deaths (deaths/100,000)","Age standardized deaths caused from indoor air pollution, including indoor air pollution-derived cases of influenza, pneumococcal pneumonia, H influenzae type B pneumonia, respiratory syncytial virus pneumonia, other lower respiratory infections, trachea, bronchus, and lung cancers, ischemic heart disease, ischemic stroke, hemorrhagic and other non-ischemic stroke, chronic obstructive pulmonary disease, and cataracts per 100,000 people. In the SPI model, data is scaled from 3 (<30 deaths per 100,000 people) to 1 (>100 deaths per 100,000 people).","Institute for Global Health Metrics and Evaluation","https://cloud.ihme.washington.edu/public.php?service=files&t=5a680ed82f7be84d10b32052099fb617&download&path=/2010/IHME_GBD_2010_RESULTS_2010_HOUSEHOLD_AIR_POLLUTION_FROM_SOLID_FUELS_Y2013M11D20.zip","Filter data to “Age standardized” in Column F, “both sexes” in column G, and “Death” in column H.  Data is found in the column “Rt-mean” to signify the average rate of deaths per country.","Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Sweden, Switzerland, United Kingdom, United States: \nWorld Health Organization\nhttp://apps.who.int/gho/data/view.main.34100?lang=en \n"]
["Basic Human Needs","Personal Safety","Homicide rate (1= <2/100,000; 5= >20/100,000)","Number of homicides, defined as death deliberately inflicted on a person by another person, per 100,000 people. Scored on a 1-5 scale:\n\n 1 = 0 – 1.99\n 2 = 2 – 5.99\n 3 = 6 – 9.99\n 4 = 10 – 19.99\n 5 = > 20\n","Institute for Economics and Peace Global Peace Index","http://www.visionofhumanity.org/gpi-data/ "]
["Basic Human Needs","Personal Safety","Level of violent crime (1=low; 5=high)","Evaluation based on the question: “Is violent crime likely to pose a significant problem for government and/or business over the next two years?” Measured on a scale of 1 (strongly no) to 5 (strongly yes).","Institute for Economics and Peace Global Peace Index","http://www.visionofhumanity.org/gpi-data/ "]
["Basic Human Needs","Personal Safety","Perceived criminality (1=low; 5=high)","An assessment of the level of domestic security and the degree to which other citizens can be trusted. Measured on a scale of 1 (majority of other citizens can be trusted) to 5 (very high level of distrust).","Institute for Economics and Peace Global Peace Index","http://www.visionofhumanity.org/gpi-data/ "]
["Basic Human Needs","Personal Safety","Political terror (1=low; 5=high)","The level of political violence and terror that a country experiences based on a 5-level “terror scale”:\n\n1 = Countries under a secure rule of law, people are not imprisoned for their views, and torture is rare or exceptional. Political murders are extremely rare.\n2 = There is a limited amount of imprisonment for nonviolent political activity. However, few persons are affected; torture and beatings are exceptional. Political murder is rare.\n3 = There is extensive political imprisonment or a recent history of such imprisonment. Execution or other political murders and brutality may be common. Unlimited detention, with or without a trial, for political views is accepted.\n4 = Civil and political rights violations have expanded to large numbers of the population. Murders, disappearances, and torture are a common part of life. In spite of its generality, on this level terror affects those who interest themselves in politics or ideas.\n5 = Terror has expanded to the whole population. The leaders of these societies place no limits on the means or thoroughness with which they pursue personal or ideological goals.\n","Institute for Economics and Peace Global Peace Index","http://www.visionofhumanity.org/gpi-data/ "]
["Basic Human Needs","Personal Safety","Traffic deaths (deaths/100,000)","Mortality due to road traffic injury, per 100,000 people, age adjusted.","World Health Organization","http://apps.who.int/gho/data/node.main.A997?lang=en",null,"Combination of time series from WHO Violence and Injury Prevention (VIP); data from WHO Global Burden of Disease 2002 and 2004; and WHO Global Road Safety Status Report accessed via Gapminder\nhttp://www.gapminder.org/data/\nTraffic mortality, age adjusted, per 100 000\n"]
["Foundations of Wellbeing","Access to Basic Knowledge","Adult literacy rate (% of pop. aged 15+)","The percentage of the population aged 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. Literacy also encompasses numeracy, the ability to make simple arithmetic calculations. ","UN Educational, Scientific, and Cultural Organization Institute for Statistics","http://data.worldbank.org/indicator/SE.ADT.LITR.ZS ",null,"Australia, Austria, Belgium, Canada, Republic of the Congo, Czech Republic, Denmark, Djibouti, Finland, France, Germany, Iceland, Ireland, Israel, Japan, Korea, Rep., Netherlands, New Zealand, Norway, Slovakia, Sweden, Switzerland, United Kingdom, United States:\nCIA World Factbook\nhttps://www.cia.gov/library/publications/the-world-factbook/fields/print_2103.html\n"]
["Foundations of Wellbeing","Access to Basic Knowledge","Primary school enrollment (% of children)","The ratio of the number of children of the official primary school age who are enrolled in primary school to the total population of official primary school age children.","UN Educational, Scientific, and Cultural Organization Institute for Statistics","http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=education-statistics-~-all-indicators","Dimension One: Country. Select all countries. \nDimension Two: Series. On left panel, choose topic, then primary, then enrolment rates. Indicator is titled “Net enrolment rate. Primary. Total.” Can also be found by searching indicator name.\nDimension Three: Time. Select all years\n","\nAlbania, China\nUnited Nations Children’s Fund\nhttp://www.unicef.org/sowc2013/files/Table_1_Stat_Tables_SWCR2013_ENGLISH.pdf\n\nBrazil, Madagascar\nUnited Nations Children’s Fund\nhttp://www.unicef.org/sowc2012/pdfs/SOWC-2012-TABLE-1-BASIC-INDICATORS.pdf \n"]
["Foundations of Wellbeing","Access to Basic Knowledge","Lower secondary school enrollment (% of children)","Total enrollment in lower secondary education, regardless of age, expressed as a percentage of the total population of official lower secondary education age. The gross enrollment ratio can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition. In the SPI model, data are capped at 100.","UN Educational, Scientific, and Cultural Organization Institute for Statistics","http://databank.worldbank.org/Data/Views/VariableSelection/SelectVariables.aspx?source=Education%20Statistics#","Dimension One: Country. Select all countries. \nDimension Two: Series. On left panel, choose topic, then secondary, then enrolment rates. Indicator is titled “Gross enrolment ratio. Lower secondary. All programmes. Total.” Can also be found by searching indicator name.\nDimension Three: Time. Select all years.\n","United Arab Emirates:\nhttp://www.unicef.org/sowc2012/pdfs/SOWC-2012-TABLE-11-ADOLESCENTS.pdf\n"]
["Foundations of Wellbeing","Access to Basic Knowledge","Upper secondary school enrollment (% of children)","Total enrollment in upper secondary education, regardless of age, expressed as a percentage of the total population of official upper secondary education age. In the SPI model, data are capped at 100.","UN Educational, Scientific, and Cultural Organization Institute for Statistics","http://databank.worldbank.org/Data/Views/VariableSelection/SelectVariables.aspx?source=Education%20Statistics#","Dimension One: Country. Select all countries. ","United Arab Emirates:\nhttp://www.unicef.org/sowc2012/pdfs/SOWC-2012-TABLE-11-ADOLESCENTS.pdf\n"]
["Foundations of Wellbeing","Access to Basic Knowledge","Gender parity in secondary enrollment (girls/boys)","The ratio of girls to boys enrolled at the secondary level in public and private schools. In the SPI model, scores are capped at 1.0.","UN Educational, Scientific, and Cultural Organization Institute for Statistics","http://databank.worldbank.org/Data/Views/VariableSelection/SelectVariables.aspx?source=Education%20Statistics#","Dimension One: Country. Select all countries. \nDimension Two: Series. On left panel, choose topic, then secondary, then enrolment rates. Indicator is titled “Gender parity index for gross enrolment ratio. Secondary. All programmes.” Can also be found by searching indicator name.\nDimension Three: Time. Select all years.\n"]
["Foundations of Wellbeing","Access to Information and Communications","Mobile telephone subscriptions (subscriptions/100 people)","Subscriptions to a public mobile telephone service using cellular technology, including the number of pre-paid SIM cards active during the past three months, expressed as the number of mobile telephone subscriptions per 100 inhabitants. In the SPI model, scores are capped at 100 mobile telephones per 100 people.","International Telecommunications Union ","http://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2013/Mobile_cellular_2000-2012.xls"]
["Foundations of Wellbeing","Access to Information and Communications","Internet users (% of pop.)","The estimated number of Internet users out of the total population, using the Internet from any device (including mobile phones) in the last 12 months. ","International Telecommunications Union ","http://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2013/Individuals_Internet_2000-2012.xls "]
["Foundations of Wellbeing","Access to Information and Communications","Press Freedom Index (0=most free; 100=least free)","The degree of freedom that journalists, news organizations, and netizens enjoy in each country, and the efforts made by the authorities to respect and ensure respect for this freedom. In the SPI model, scores are grouped into 7 bands, from 1 (least free) to 7 (most free).","Reporters Without Borders","http://en.rsf.org/"]
["Foundations of Wellbeing","Health and Wellness","Life expectancy (years)","The number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.","World Development Indicators","http://data.worldbank.org/indicator/SP.DYN.LE00.IN "]
["Foundations of Wellbeing","Health and Wellness","Non-communicable disease deaths between the ages of 30 and 70 (probability of dying)","The probability of dying between the ages 30 and 70 from cardiovascular disease, cancer, diabetes, or chronic respiratory disease.","World Health Organization","http://apps.who.int/gho/data/node.main.A857?lang=en "]
["Foundations of Wellbeing","Health and Wellness","Obesity rate (% of pop.)","The percentage of the population with a body mass index (BMI) of 30 kg/m2 or higher (age-standardized estimate), both sexes.","World Health Organization","http://apps.who.int/gho/data/node.main.A900?lang=en"]
["Foundations of Wellbeing","Health and Wellness","Outdoor air pollution attributable deaths (deaths/100,000)","The number of deaths resulting from emissions from industrial activity, households, cars and trucks, expressed as the rate per 100,000 people.","World Health Organization","http://apps.who.int/gho/data/view.main.34300?lang=en"]
["Foundations of Wellbeing","Health and Wellness","Suicide rate (deaths/100,000)","Mortality due to self-inflicted injury, per 100,000 people, age adjusted.","Institute for Health Metrics and Evaluation","https://cloud.ihme.washington.edu/public.php?service=files&t=d559026958b38c3f4d12029b36d783da&download&path=/2010/IHME_GBD_2010_RESULTS_2010_SELF-HARM_Y2013M11D15.zip "]
["Foundations of Wellbeing","Ecosystem Sustainability","Greenhouse gas emissions (CO2 equivalents per GDP)","Emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6) expressed in CO2 equivalents using 100 year global warming potentials found in the Intergovernmental Panel on Climate Change Second Assessment Report per GDP-PPP. In the SPI model, data is scaled from 0 to 4:\n\n4: < 100\n3: 100 – 200\n2: 200 – 1000\n1: 1000 – 2000\n0: > 2000","World Resources Institute","http://cait2.wri.org/wri/Country%20GHG%20Emissions?indicator=Total%20GHG%20Emissions%20Excluding%20LUCF%20Per%20GDP&indicator=Total%20GHG%20Emissions%20Including%20LUCF%20Per%20GDP&year=2010&sortDir=asc&chartType=geo "]
["Foundations of Wellbeing","Ecosystem Sustainability","Water withdrawals as a percent of resources","Baseline water stress or the ratio of total annual water withdrawals to total available annual renewable supply, scaled from 0 to 5.\n\n4–5: Extremely high stress (>80%)\n3–4: High stress (40–80%)\n2–3: Medium-high stress (20–40%)\n1–2: Low-medium stress (10–20%)\n0–1: Low stress (<10%)\n","World Resources Institute","http://www.wri.org/sites/default/files/aqueduct_aggr_xlsx_20140109%20%282%29.zip"]
["Foundations of Wellbeing","Ecosystem Sustainability","Biodiversity and habitat (0=no protection; 100=high protection)","The protection of terrestrial and marine areas as well as threatened or endangered species, comprising Critical Habitat Protection, Terrestrial Protected Areas (National Biome Weight), Terrestrial Protected Areas (Global Biome Weight), and Marine Protected Areas, scaled from 0 (no protection) to 100 (high protection).","Yale Center for Environmental Law & Policy and Columbia University Center for International Earth Science Information Network Environmental Performance Index","http://epi.yale.edu/files/biodiversity_and_habitat.xls"]
["Opportunity","Personal Rights","Political rights (1=full rights; 7=no rights)","An evaluation of three subcategories of political rights: electoral process, political pluralism and participation, and functioning of government on a scale from 1 (full political rights) to 7 (no political rights).","Freedom House","http://www.freedomhouse.org/sites/default/files/Country%20Ratings%20and%20Status%2C%201973-2014%20%28FINAL%29.xls"]
["Opportunity","Personal Rights","Freedom of speech (0=low; 2=high)","The extent to which freedoms of speech and press are affected by government censorship, including ownership of media outlets, measured on a scale of 0 (government censorship of the media was complete) to 2 (no government censorship of the media in a given year).","Cingranelli-Richards Human Rights Data Project","http://humanrightsdata.blogspot.com/p/data-documentation.html"]
["Opportunity","Personal Rights","Freedom of assembly/association (0=low; 2=high)","The extent to which freedoms of assembly and association are subject to actual governmental limitations or restrictions (as opposed to strictly legal protections), measured on a scale of 0 (rights severely restricted or denied completely to all citizens) to 2 (rights virtually unrestricted and freely enjoyed by practically all citizens).","Cingranelli-Richards Human Rights Data Project","http://humanrightsdata.blogspot.com/p/data-documentation.html"]
["Opportunity","Personal Rights","Freedom of movement (0=low; 4=high)","The sum of the two following variables:\n\nFreedom of Foreign Movement: Citizens' freedom to leave and return to their country, measured on a scale of 0 (freedom was severely restricted) to 2 (unrestricted freedom of foreign movement). \n\nFreedom of Domestic Movement: Citizens' freedom to travel within their own country, measured on a scale of 0 (freedom was severely restricted) to 2 (unrestricted freedom of domestic movement). ","Cingranelli-Richards Human Rights Data Project","http://humanrightsdata.blogspot.com/p/data-documentation.html"]
["Opportunity","Personal Rights","Private property rights (0=none; 100=full)","The degree to which a country’s laws protect private property rights and the degree to which its government enforces those laws, measured on a scale of 0 (private property is outlawed, all property belongs to the state; people do not have the right to sue others and do not have access to the courts; corruption is endemic) to 100 (private property is guaranteed by the government; the court system enforces contracts efficiently and quickly; the justice system punishes those who unlawfully confiscate private property; there is no corruption or expropriation).","Heritage Foundation","http://www.heritage.org/index/explore"]
["Opportunity","Personal Freedom and Choice","Freedom over life choices (% satisfied)","The percentage of respondents answering satisfied to the question, “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”","Gallup World Poll"]
["Opportunity","Personal Freedom and Choice","Freedom of religion (1=low; 4=high)","A combined measure of 20 types of restrictions, including efforts by governments to ban particular faiths, prohibit conversions,  limit preaching or give preferential treatment to one or more religious groups. In the SPI model, scores range from 1 (low freedom) to 4 (very high freedom).","Pew Research Center Government Restrictions Index","http://www.pewforum.org/2014/01/14/appendix-2-government-restrictions-index/   "]
["Opportunity","Personal Freedom and Choice","Modern slavery, human trafficking and child marriage (1=low; 100=high)","A combined measure of three variables: estimated prevalence of modern slavery in each country (accounting for 95% of the total); a measure of the level of human trafficking to and from each country (accounts for 2.5%); and a measure of the level of child and early marriage in each country (accounts for 2.5%), scaled from 1 (low slavery) to 100 (high slavery).","Walk Free Foundation Global Slavery Index","http://www.globalslaveryindex.org/report/","Access Index Data -> “Examine our data” download. Tab titled “Prevalence”"]
["Opportunity","Personal Freedom and Choice","Satisfied demand for contraception (% of women)","The contraceptive prevalence rate for women aged 15-49 who are married or in a union over the sum of contraceptive prevalence rate and unmet need for contraception.","National University of Singapore, published in The Lancet","http://www.un.org/esa/population/publications/WCU2012/Alkema-etal_2013_Natl-regl&global-rates&trends-family-planning_WEB-APPENDIX.pdf","p.48"]
["Opportunity","Personal Freedom and Choice","Corruption (0=high; 100=low)","The perceived level of public sector corruption based on expert opinion, measured on a scale from 0 (highly corrupt) to 100 (very clean).","Transparency International","http://cpi.transparency.org/cpi2013/results/"]
["Opportunity","Tolerance and Inclusion","Women treated with respect (0=low; 100=high)","The percentage of female respondents answering yes to the question, “Do you believe that women in this country are treated with respect and dignity, or not?”","Gallup World Poll"]
["Opportunity","Tolerance and Inclusion","Tolerance for immigrants (0=low; 100=high)","The percentage of respondents answering yes to the question, “Is the city or area where you live a good place or not a good place to live for immigrants from other countries?”","Gallup World Poll"]
["Opportunity","Tolerance and Inclusion","Tolerance for homosexuals (0=low; 100=high)","The percentage of respondents answering yes to the question, “Is the city or area where you live a good place or not a good place to live for gay or lesbian people?”","Gallup World Poll"]
["Opportunity","Tolerance and Inclusion","Discrimination and violence against minorities (0=low; 10=high)","Discrimination, powerlessness, ethnic violence, communal violence, sectarian violence, and religious violence, measured on a scale on 0 (low pressures) to 10 (very high pressures).","Fund for Peace Failed States Index Group Grievance indicator","http://ffp.statesindex.org/rankings-2013-sortable"]
["Opportunity","Tolerance and Inclusion","Religious tolerance (1=low; 4=high)","A measure of 13 types of religious hostility by private individuals, organizations or groups in society, including religion-related armed conflict or terrorism, mob or sectarian violence, harassment over attire for religious reasons or other religion-related intimation or abuse. In the SPI model, scores range from 1 (low) to 4 (very high). ","Pew Research Center Social Hostilities Index","http://www.pewforum.org/2014/01/14/appendix-3-social-hostilities-index/  "]
["Opportunity","Tolerance and Inclusion","Community safety net (0=low; 100=high)","The percentage of respondents answering yes to the question, “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”","Gallup World Poll"]
["Opportunity","Access to Advanced Education","Years of tertiary schooling","The average years of tertiary education completed among people over age 25.","Barro-Lee Educational Attainment Dataset","http://databank.worldbank.org/Data/Views/VariableSelection/SelectVariables.aspx?source=Education%20Statistics","Dimension One: Country. Select all countries. \nDimension Two: Series. On left panel, choose topic, then attainment. Indicator is titled “Barro-Lee: Average years of tertiary schooling, age 25+, total.” \nDimension Three: Time. Select all years.\n"]
["Opportunity","Access to Advanced Education","Women's average years in school","The average number of years of school attended by women between 25 and 34 years old, including primary, secondary and tertiary education.","Institute for Health Metrics and Evaluation","http://www.gapminder.org/data/ ","Dataset: Mean years in school (women 25 to 34 years)"]
["Opportunity","Access to Advanced Education","Inequality in the attainment of education (0=low; 1=high)","The loss in potential education due to inequality, calculated as the percentage difference between the Human Development Index Education Index, which comprises mean years of schooling and expected years of schooling, and the Inequality-adjusted Education Index.","United Nations Development Programme","https://data.undp.org/dataset/Value-lost-due-to-Education-Inequality/7cr6-t56t "]
["Opportunity","Access to Advanced Education","Number of globally ranked universities (0=none; 5= >50)","The number of universities ranked in the top 400 on any of the three most widely used international university rankings, measured on a scale from 0 (no ranked universities) to 5 (more than 50 ranked universities).","Times Higher Education World University Rankings, QS World University Rankings, and Academic Ranking of World Universities","http://www.timeshighereducation.co.uk/world-university-rankings/2013-14/world-ranking; http://www.topuniversities.com/university-rankings/world-university-rankings/2013#sorting=rank+region=+country=+faculty=+stars=false+search=; http://www.shanghairanking.com/ARWU2013.html\n"]
[]
[null,null,"GDP per capita, PPP (constant 2005 international $)","GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 international dollars.","World Bank World Development Indicators","http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD"]

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