Popisna statistika

K popisnej statistike nadcitame tieto baliky programov( v Mathematica 5. ano, v Mathematica 6. nepotrebujem

<< Statistics`DescriptiveStatistics`

<< Statistics`DataManipulation`

<<"StatisticalPlots`"

Pocas 50 tyzdnov sa merali pocty chybne vyrobenych panelov, vysledky boli nasledovne, urobte prvotnu statisticku analyz

panely={14, 13, 15, 12, 14, 9, 15, 8, 14, 10, 16, 12, 13, 17, 18, 13, 13, 10, 14, 16, 11, 14, 12, 9, 15, 11, 14, 15, 11, 15, 10, 16, 10, 12, 13, 11, 13, 11, 9, 13, 8, 12, 16, 12, 17, 12, 13, 11, 13, 12}

Najprv urobime tabulku pocetnosti merani a grafy pocetnosti-

Min[panely]

Max[panely]

pocetnost = BinCounts[panely, {8,19}]

Prikaz  je vo forme (zlava uzavrety, sprava otvoreny interval
pocetnost

?BinCounts

tp=Tally[Sort[panely]]

hodnoty=tp[[All,1]]

pocetnost=tp[[All,2]]

tp//MatrixForm

<<"BarCharts`"

<<"Histograms`"

BarChart[pocetnost,BarLabels->hodnoty]

?Histogram

Histogram[panely]

urobme teraz tabulku a grafy triednych pocetnosti, rpvky stotoznime so stredom intervalu

triedy= BinCounts[panely, {7.5,19.5,3}]

p=Table[i,{i,7.5,19.5,3}]

Histogram[triedy,FrequencyData->True,HistogramRange->{7.5,19.5},Ticks->{{9,12,15,18},triedy}]

Histogram[panely,HistogramCategories->p]

StemLeafPlot[panely]

StemLeafPlot[panely, ColumnLabels->{desiatky, jednotky}]

StemLeafPlot[panely,IncludeStemCounts->True]

StemLeafPlot[panely,StemExponent->0]

?BoxWhiskerPlot

Zvisle ciary su postupne - lavy koniec- minimum, 0.25 kvantil da sa menit prikazom, 0.5 kvantil = median, 0.75 kvantil - da sa menit prikazom, pravy koniec maximum

BoxWhiskerPlot[panely]

BoxWhiskerPlot[panely,BoxOrientation->Horizontal,BoxLabels->"panely"]

BoxWhiskerPlot[panely,BoxOrientation->Horizontal,BoxQuantile->0.2]

vybocujuce=Flatten[{panely,25}]

BoxWhiskerPlot[panely,BoxOutliers->All,BoxOrientation->Horizontal]

BoxWhiskerPlot[vybocujuce,BoxOutliers->All,BoxOutlierMarkers->{■}]

ParetoPlot[panely]

Charakteristiky polohy

Mean[panely]

%//N

Median[panely]

Prikaz Mode na najdenie Modusu bol nahradeny Commonest

Commonest[panely]

HarmonicMean[panely]//N

Quartiles[panely]//N

Quantile[panely,0.1]

ZeroMean[data_]:=data-Mean[data]

ZeroMean[panely]//N

LocationReport[data_]:={Mean->Mean[data],HarmonicMean->HarmonicMean[data],Median->Median[data],Mode->Commonest[data]}

LocationReport[panely]//N

Charakteristiky rozptylu

Variance[panely]//N

VarianceMLE[data_]:=Variance[data](Length[data]-1)/Length[data]

VarianceMLE[panely]//N

StandardDeviation[panely]//N

StandardDeviationMLE[data_]:=StandardDeviation[data]Sqrt[(Length[data]-1)/Length[data]]

StandardDeviationMLE[panely]//N

InterquartileRange[panely]

QuartileDeviation[panely]

Skewness[panely]//N

Skewness[NormalDistribution[]]

Kurtosis[panely]//N

Kurtosis[NormalDistribution[]]

%-3

Uloha
Simulujte tri mnoziny dat z normalneho rozdelenia(rozne stredne hodnoty a disperzie), urobte graficku a statisticku analyzu, kazda mnozina nech obsahuje 100 dat

normal1=RandomReal[NormalDistribution[],100]

normal2=RandomReal[NormalDistribution[2,1],100]

BoxWhiskerPlot[normal1,normal2]