(* Content-type: application/vnd.wolfram.mathematica *)

(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)

(* CreatedBy='Mathematica 8.0' *)

(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[       157,          7]
NotebookDataLength[    197502,       3983]
NotebookOptionsPosition[    192728,       3814]
NotebookOutlinePosition[    193151,       3830]
CellTagsIndexPosition[    193108,       3827]
WindowFrame->Normal*)

(* Beginning of Notebook Content *)
Notebook[{

Cell[CellGroupData[{
Cell[TextData[StyleBox["Propaga\[CCedilla]\[ATilde]o de incertezas", \
"Section"]], "Subtitle",
 CellChangeTimes->{{3.586540699121662*^9, 3.58654070313762*^9}}],

Cell[BoxData[
 RowBox[{
  RowBox[{"z", "[", 
   RowBox[{"x_", ",", "y_"}], "]"}], ":=", 
  RowBox[{
   RowBox[{"2", "x"}], "+", 
   RowBox[{"3", 
    RowBox[{"y", "^", "2"}]}]}]}]], "Input",
 CellChangeTimes->{{3.554917963794841*^9, 3.5549179907068405`*^9}}],

Cell[CellGroupData[{

Cell[BoxData[{
 RowBox[{"x0", "=", "4.0"}], "\[IndentingNewLine]", 
 RowBox[{"\[Sigma]x0", "=", "1.0"}], "\[IndentingNewLine]", 
 RowBox[{"y0", "=", "5.0"}], "\[IndentingNewLine]", 
 RowBox[{"\[Sigma]y0", "=", 
  RowBox[{"Round", "[", 
   RowBox[{
    RowBox[{"1", "/", 
     RowBox[{"Sqrt", "[", "2", "]"}]}], ",", "0.001"}], "]"}]}]}], "Input",
 CellChangeTimes->{{3.5865396764431553`*^9, 3.5865397233376703`*^9}, {
  3.5865401641242*^9, 3.586540175875174*^9}}],

Cell[BoxData["4.`"], "Output",
 CellChangeTimes->{3.5865397250096817`*^9, 3.5865401765002236`*^9, 
  3.5865424343748426`*^9}],

Cell[BoxData["1.`"], "Output",
 CellChangeTimes->{3.5865397250096817`*^9, 3.5865401765002236`*^9, 
  3.5865424343904696`*^9}],

Cell[BoxData["5.`"], "Output",
 CellChangeTimes->{3.5865397250096817`*^9, 3.5865401765002236`*^9, 
  3.5865424343904696`*^9}],

Cell[BoxData["0.707`"], "Output",
 CellChangeTimes->{3.5865397250096817`*^9, 3.5865401765002236`*^9, 
  3.5865424343904696`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell["\<\
x e y distribu\[IAcute]dos estatisticamente independentes\
\>", "Subsubsection",
 CellChangeTimes->{{3.586540725717616*^9, 3.5865407504227886`*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"v", "=", 
  RowBox[{"Table", "[", 
   RowBox[{
    RowBox[{"z", "[", 
     RowBox[{
      RowBox[{"RandomVariate", "[", 
       RowBox[{"NormalDistribution", "[", 
        RowBox[{"x0", ",", "\[Sigma]x0"}], "]"}], "]"}], ",", 
      RowBox[{"RandomVariate", "[", 
       RowBox[{"NormalDistribution", "[", 
        RowBox[{"y0", ",", "\[Sigma]y0"}], "]"}], "]"}]}], "]"}], ",", 
    RowBox[{"{", "100", "}"}]}], "]"}]}]], "Input",
 CellChangeTimes->{{3.5549180080198407`*^9, 3.554918117043841*^9}, {
  3.5549181484338408`*^9, 3.554918203110841*^9}, {3.586539224624465*^9, 
  3.586539251642329*^9}, {3.586539733541641*^9, 3.586539757215475*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
  "107.9593150976887`", ",", "99.00387677817844`", ",", "73.72304032117219`", 
   ",", "64.76044251705055`", ",", "117.15839021442333`", ",", 
   "100.71383375284587`", ",", "143.3811276786151`", ",", 
   "81.71029566955337`", ",", "69.1909416020382`", ",", "49.7634748307587`", 
   ",", "68.08428510539821`", ",", "86.52401322860474`", ",", 
   "96.24287118318276`", ",", "91.52164390875733`", ",", "77.59720369752037`",
    ",", "65.77613469271436`", ",", "117.75805908044194`", ",", 
   "128.62101920402304`", ",", "72.12035593930482`", ",", 
   "78.48311342721419`", ",", "78.46441405335588`", ",", 
   "101.73729313094319`", ",", "122.42939669678611`", ",", 
   "84.78006438910798`", ",", "92.8189406696788`", ",", "97.52435421594453`", 
   ",", "104.4454596527082`", ",", "123.60451737050916`", ",", 
   "32.87167582272784`", ",", "70.88863953973397`", ",", 
   "112.20069066099909`", ",", "91.50136377848065`", ",", 
   "93.26760048900276`", ",", "68.33855026528136`", ",", "88.17116856145346`",
    ",", "108.96634311413992`", ",", "77.37730322924426`", ",", 
   "78.03389012922523`", ",", "58.59698055564514`", ",", "74.18925175715424`",
    ",", "91.29246451748826`", ",", "88.97273522259582`", ",", 
   "83.67848599059734`", ",", "60.16697343056066`", ",", "70.3211955498001`", 
   ",", "87.91261032975012`", ",", "112.35391417515018`", ",", 
   "63.29247587554394`", ",", "98.35822657541969`", ",", "75.60538184781552`",
    ",", "128.27925429977194`", ",", "75.39802133570689`", ",", 
   "63.81764267040231`", ",", "116.66696758341283`", ",", 
   "103.9581324452931`", ",", "84.91109429243755`", ",", "83.52627848324072`",
    ",", "96.53176827922765`", ",", "106.94892836271838`", ",", 
   "98.78985187970007`", ",", "52.037130794057646`", ",", 
   "73.49711625498927`", ",", "76.21690459845438`", ",", "64.40846652385329`",
    ",", "80.47467137204511`", ",", "70.66007568194793`", ",", 
   "85.08761503549046`", ",", "78.82292981864744`", ",", 
   "112.66964681465674`", ",", "73.15743823371209`", ",", 
   "49.449438952586036`", ",", "94.25029949844607`", ",", 
   "82.93287556842343`", ",", "83.13336413004161`", ",", 
   "126.12465253677902`", ",", "84.59570541299009`", ",", 
   "102.1703552156514`", ",", "97.12095877271277`", ",", "76.76511587838344`",
    ",", "82.49145463135358`", ",", "105.46763697654546`", ",", 
   "85.14722371587722`", ",", "85.4788645087247`", ",", "70.67833299864277`", 
   ",", "81.40544944535414`", ",", "93.55785409866589`", ",", 
   "76.59439079090815`", ",", "67.89636656099097`", ",", "95.1725150418712`", 
   ",", "79.25530676502936`", ",", "91.62747559400799`", ",", 
   "78.39449075467492`", ",", "85.10027954820573`", ",", "85.88242254948179`",
    ",", "72.14137816137034`", ",", "107.19215985356648`", ",", 
   "60.500178888348685`", ",", "94.67635607968954`", ",", 
   "65.82506656255325`", ",", "85.16660169691426`"}], "}"}]], "Output",
 CellChangeTimes->{{3.586539252204873*^9, 3.5865392598305035`*^9}, 
   3.5865397585593395`*^9, 3.5865400302693543`*^9, 3.5865401803442907`*^9, 
   3.5865424344060965`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Histogram", "[", "v", "]"}]], "Input",
 CellChangeTimes->{{3.5549181827168407`*^9, 3.554918188294841*^9}}],

Cell[BoxData[
 GraphicsBox[
  {RGBColor[0.798413061722744, 0.824719615472648, 0.968322270542458], 
   EdgeForm[Opacity[0.623]], {}, 
   {RGBColor[0.798413061722744, 0.824719615472648, 0.968322270542458], 
    EdgeForm[Opacity[0.623]], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{30., 0}, {40., 1}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{12.499999999999986`, 41.73673469387754}, {
         87.81013512357546, 97.15639075372444}}],
       StatusArea[#, 1]& ,
       TagBoxNote->"1"],
      StyleBox["1", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[1, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{40., 0}, {50., 2}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{40.73673469387754, 69.97346938775507}, {
         79.46387949342648, 97.15639075372444}}],
       StatusArea[#, 2]& ,
       TagBoxNote->"2"],
      StyleBox["2", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[2, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{50., 0}, {60., 2}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{68.97346938775507, 98.21020408163261}, {
         79.46387949342648, 97.15639075372444}}],
       StatusArea[#, 2]& ,
       TagBoxNote->"2"],
      StyleBox["2", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[2, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{60., 0}, {70., 12}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{97.21020408163261, 
         126.44693877551018`}, {-3.998676808063294, 97.15639075372444}}],
       StatusArea[#, 12]& ,
       TagBoxNote->"12"],
      StyleBox["12", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[12, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{70., 0}, {80., 23}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{125.44693877551018`, 
         154.6836734693877}, {-95.80748873970204, 97.15639075372444}}],
       StatusArea[#, 23]& ,
       TagBoxNote->"23"],
      StyleBox["23", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[23, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{80., 0}, {90., 21}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{153.6836734693877, 
         182.92040816326525`}, {-79.11497747940409, 97.15639075372444}}],
       StatusArea[#, 21]& ,
       TagBoxNote->"21"],
      StyleBox["21", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[21, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{90., 0}, {100., 17}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{181.92040816326525`, 
         211.1571428571428}, {-45.72995495880819, 97.15639075372444}}],
       StatusArea[#, 17]& ,
       TagBoxNote->"17"],
      StyleBox["17", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[17, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{100., 0}, {110., 10}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{210.1571428571428, 239.39387755102035`}, {
         12.693834452234668`, 97.15639075372444}}],
       StatusArea[#, 10]& ,
       TagBoxNote->"10"],
      StyleBox["10", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[10, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{110., 0}, {120., 6}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{238.39387755102035`, 267.63061224489786`}, {
         46.07885697283057, 97.15639075372444}}],
       StatusArea[#, 6]& ,
       TagBoxNote->"6"],
      StyleBox["6", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[6, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{120., 0}, {130., 5}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{266.63061224489786`, 295.8673469387754}, {
         54.42511260297955, 97.15639075372444}}],
       StatusArea[#, 5]& ,
       TagBoxNote->"5"],
      StyleBox["5", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[5, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{140., 0}, {150., 1}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{323.104081632653, 352.3408163265305}, {
         87.81013512357546, 97.15639075372444}}],
       StatusArea[#, 1]& ,
       TagBoxNote->"1"],
      StyleBox["1", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[1, {
        GrayLevel[0]}], "Tooltip"]& ]}, {}, {}},
  AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
  Axes->{True, True},
  AxesLabel->{None, None},
  AxesOrigin->{30., 0},
  FrameTicks->{{Automatic, Automatic}, {Automatic, Automatic}},
  GridLines->{None, None},
  PlotRange->{{30., 150.}, {All, All}},
  PlotRangePadding->{{
     Scaled[0.02], 
     Scaled[0.02]}, {
     Scaled[0.02], 
     Scaled[0.1]}},
  Ticks->{Automatic, Automatic}]], "Output",
 CellChangeTimes->{{3.5549181897588406`*^9, 3.554918208514841*^9}, 
   3.5865391829960117`*^9, {3.586539255705164*^9, 3.586539261971307*^9}, 
   3.5865397613408165`*^9, 3.5865401829226313`*^9, 3.5865424348123784`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Mean", "[", "v", "]"}]], "Input",
 CellChangeTimes->{{3.554918193823841*^9, 3.554918196325841*^9}}],

Cell[BoxData["86.54310305474884`"], "Output",
 CellChangeTimes->{3.554918211408841*^9, 3.5865391830116377`*^9, 
  3.5865392676592817`*^9, 3.5865397643879433`*^9, 3.586540185016554*^9, 
  3.586542434828004*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"StandardDeviation", "[", "v", "]"}]], "Input",
 CellChangeTimes->{{3.5549182156148405`*^9, 3.5549182237328405`*^9}}],

Cell[BoxData["19.352993160481166`"], "Output",
 CellChangeTimes->{3.5549182248038406`*^9, 3.586539183027264*^9, 
  3.5865392699250946`*^9, 3.586539767716344*^9, 3.586540188016805*^9, 
  3.586542434828004*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Print", "[", 
  RowBox[{"\"\<do conjunto de dados = \>\"", ",", 
   RowBox[{"Round", "[", 
    RowBox[{
     RowBox[{"Mean", "[", "v", "]"}], ",", "0.1"}], "]"}], ",", 
   "\"\< +- \>\"", ",", 
   RowBox[{"Round", "[", 
    RowBox[{
     RowBox[{
      RowBox[{"StandardDeviation", "[", "v", "]"}], "/", 
      RowBox[{"Sqrt", "[", 
       RowBox[{"Length", "[", "v", "]"}], "]"}]}], ",", "0.1"}], "]"}], ",", 
   "\"\< estimado das m\[EAcute]dias  \>\"", ",", 
   RowBox[{"z", "[", 
    RowBox[{"x0", ",", "y0"}], "]"}]}], "]"}]], "Input",
 CellChangeTimes->{{3.5865397722479706`*^9, 3.5865398719593625`*^9}, {
  3.5865399429183683`*^9, 3.5865400160025463`*^9}}],

Cell[BoxData[
 InterpretationBox[
  RowBox[{"\<\"do conjunto de dados = \"\>", "\[InvisibleSpace]", "86.5`", 
   "\[InvisibleSpace]", "\<\" +- \"\>", "\[InvisibleSpace]", 
   "1.9000000000000001`", 
   "\[InvisibleSpace]", "\<\" estimado das m\[EAcute]dias  \"\>", 
   "\[InvisibleSpace]", "83.`"}],
  SequenceForm[
  "do conjunto de dados = ", 86.5, " +- ", 1.9000000000000001`, 
   " estimado das m\[EAcute]dias  ", 83.],
  Editable->False]], "Print",
 CellChangeTimes->{{3.586539825283619*^9, 3.586539873412608*^9}, 
   3.5865399837655*^9, 3.5865400181902313`*^9, 3.5865401933297453`*^9, 
   3.5865424348436627`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
   RowBox[{"StandardDeviation", "[", "v", "]"}], ",", 
   RowBox[{"Sqrt", "[", 
    RowBox[{
     RowBox[{
      RowBox[{"(", 
       RowBox[{"2", "\[Sigma]x0"}], ")"}], "^", "2"}], "+", 
     RowBox[{
      RowBox[{"(", 
       RowBox[{"6", "y0", " ", "\[Sigma]y0"}], ")"}], "^", "2"}]}], "]"}]}], 
  "}"}]], "Input",
 CellChangeTimes->{{3.5865397956405334`*^9, 3.5865398162047415`*^9}, {
  3.586539886569948*^9, 3.5865399274327097`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"19.352993160481166`", ",", "21.304086462460667`"}], 
  "}"}]], "Output",
 CellChangeTimes->{3.586539928167143*^9, 3.586540034394696*^9, 
  3.5865402014397907`*^9, 3.5865424348592567`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"\[Rho]", "=", 
  RowBox[{"Round", "[", 
   RowBox[{
    RowBox[{"1", "/", 
     RowBox[{"Sqrt", "[", "2", "]"}]}], ",", "0.001"}], "]"}]}]], "Input",
 CellChangeTimes->{{3.586540113557506*^9, 3.5865401386533394`*^9}}],

Cell[BoxData["0.707`"], "Output",
 CellChangeTimes->{3.5865401395909133`*^9, 3.58654020883103*^9, 
  3.5865424348748837`*^9}]
}, Open  ]]
}, Open  ]],

Cell[CellGroupData[{

Cell["\<\
x e y correlacionados, coeficiente de correla\[CCedilla]\[ATilde]o Sqrt[2]\
\>", "Subsection",
 CellChangeTimes->{{3.5865407656740522`*^9, 3.586540810740288*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"RandomVariate", "[", 
  RowBox[{"BinormalDistribution", "[", 
   RowBox[{
    RowBox[{"{", 
     RowBox[{"x0", ",", "y0"}], "}"}], ",", 
    RowBox[{"{", 
     RowBox[{"1", ",", "\[Sigma]y0"}], "}"}], ",", "\[Rho]"}], "]"}], 
  "]"}]], "Input",
 CellChangeTimes->{{3.5549183598878407`*^9, 3.5549184175538406`*^9}, {
  3.554918544538841*^9, 3.5549185599378405`*^9}, {3.5549186530128407`*^9, 
  3.5549186768038406`*^9}, {3.586540062693919*^9, 3.586540066600493*^9}, {
  3.5865402228009377`*^9, 3.586540229692134*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"3.4134847120543603`", ",", "5.009706349360707`"}], "}"}]], "Output",
 CellChangeTimes->{
  3.5865400571778364`*^9, 3.5865401024315844`*^9, {3.5865402307234697`*^9, 
   3.5865402400211124`*^9}, 3.5865424349061356`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"q", "=", 
  RowBox[{"Table", "[", 
   RowBox[{
    RowBox[{"z", "[", 
     RowBox[{
      RowBox[{"RandomVariate", "[", 
       RowBox[{"BinormalDistribution", "[", 
        RowBox[{
         RowBox[{"{", 
          RowBox[{"4", ",", "5"}], "}"}], ",", 
         RowBox[{"{", 
          RowBox[{"1", ",", "0.707"}], "}"}], ",", "0.707"}], "]"}], "]"}], "/.", 
      RowBox[{"List", "\[Rule]", "Sequence"}]}], "]"}], ",", 
    RowBox[{"{", "100", "}"}]}], "]"}]}]], "Input",
 CellChangeTimes->{{3.5549187958948407`*^9, 3.5549188200918407`*^9}, {
  3.554918854681841*^9, 3.554918858057841*^9}, {3.5549188946318407`*^9, 
  3.554918897983841*^9}, {3.5865393449469366`*^9, 3.586539427531907*^9}, {
  3.586539557292656*^9, 3.586539581497791*^9}, {3.5865396293923845`*^9, 
  3.586539630048689*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
  "70.80123251962384`", ",", "130.40343134826844`", ",", 
   "55.789409378659805`", ",", "64.39865549928251`", ",", "88.4718248305264`",
    ",", "89.51945403863944`", ",", "78.69249801345342`", ",", 
   "107.51692030029575`", ",", "39.82266162307802`", ",", 
   "98.44731385120365`", ",", "95.19922868114891`", ",", 
   "124.17938992522046`", ",", "106.17471468691016`", ",", 
   "42.61613047353078`", ",", "37.444724676144304`", ",", 
   "107.05687671493884`", ",", "102.4945943926695`", ",", 
   "69.74625225968748`", ",", "55.96379480136994`", ",", "82.58664709714537`",
    ",", "117.34391974667399`", ",", "110.04893083420947`", ",", 
   "57.95219598406179`", ",", "102.30502344626031`", ",", 
   "91.34581567103623`", ",", "70.85905133140487`", ",", "65.53803349865284`",
    ",", "81.14056673216031`", ",", "107.6150182592389`", ",", 
   "111.55308428675733`", ",", "61.54665344299149`", ",", "98.477498143584`", 
   ",", "118.03061293007`", ",", "96.68047769722673`", ",", 
   "72.7440745759837`", ",", "93.23186464890965`", ",", "82.32777195709494`", 
   ",", "53.71096391378228`", ",", "54.09440058520582`", ",", 
   "65.40494190094745`", ",", "53.638396155664424`", ",", 
   "94.89422716746203`", ",", "44.88035175095044`", ",", "52.63320925486974`",
    ",", "121.89738726207496`", ",", "80.35556001780469`", ",", 
   "150.63573869829045`", ",", "90.06046883028631`", ",", 
   "67.69244280601038`", ",", "118.87296349385812`", ",", 
   "111.3760492695301`", ",", "84.85238347330127`", ",", "87.45845688563524`",
    ",", "105.1471101567895`", ",", "110.71824148750349`", ",", 
   "95.74486860191611`", ",", "88.81275784235922`", ",", 
   "62.480630836393736`", ",", "111.1442140629892`", ",", 
   "83.75542435820547`", ",", "123.97133051846703`", ",", 
   "69.59484723783103`", ",", "68.3967767140446`", ",", "41.16394749676838`", 
   ",", "52.96802678030639`", ",", "90.57381108863768`", ",", 
   "110.01896609805863`", ",", "90.60780735228906`", ",", 
   "124.04763444835515`", ",", "58.77144221145836`", ",", 
   "115.18013073539342`", ",", "56.969411504147295`", ",", 
   "108.00048619888304`", ",", "117.7118996745678`", ",", 
   "79.87883301709198`", ",", "134.2673232716845`", ",", "90.95583939125405`",
    ",", "102.63138518722315`", ",", "85.89456743610904`", ",", 
   "75.69657557056716`", ",", "55.39351909120671`", ",", "76.59513844776366`",
    ",", "62.74644530958753`", ",", "67.53773529978416`", ",", 
   "58.80496353537251`", ",", "111.16981992028913`", ",", 
   "81.81147367854383`", ",", "56.88458063532067`", ",", 
   "117.55172594846809`", ",", "70.31623854672084`", ",", "85.9476688577637`",
    ",", "102.45640017979113`", ",", "102.15475704948014`", ",", 
   "95.5965238837812`", ",", "67.35840509438466`", ",", "82.7046957184991`", 
   ",", "56.994824671631584`", ",", "74.99209261543362`", ",", 
   "97.3979098804407`", ",", "80.41337885839295`"}], "}"}]], "Output",
 CellChangeTimes->{{3.5865393725585976`*^9, 3.586539428594493*^9}, {
   3.5865395771849327`*^9, 3.5865395821540937`*^9}, 3.58653963179883*^9, 
   3.586540243115118*^9, 3.5865424349217606`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Histogram", "[", "q", "]"}]], "Input",
 CellChangeTimes->{{3.586540252068985*^9, 3.586540255116116*^9}}],

Cell[BoxData[
 GraphicsBox[
  {RGBColor[0.798413061722744, 0.824719615472648, 0.968322270542458], 
   EdgeForm[Opacity[0.651]], {}, 
   {RGBColor[0.798413061722744, 0.824719615472648, 0.968322270542458], 
    EdgeForm[Opacity[0.651]], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{20., 0}, {40., 2}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{12.5, 61.857142857142854`}, {82.93736021603587, 
         97.16293238333606}}],
       StatusArea[#, 2]& ,
       TagBoxNote->"2"],
      StyleBox["2", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[2, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{40., 0}, {60., 17}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{60.857142857142854`, 
         110.2142857142857}, {-16.254431038715552`, 97.16293238333606}}],
       StatusArea[#, 17]& ,
       TagBoxNote->"17"],
      StyleBox["17", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[17, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{60., 0}, {80., 21}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{109.2142857142857, 
         158.57142857142856`}, {-42.705575373315924`, 97.16293238333606}}],
       StatusArea[#, 21]& ,
       TagBoxNote->"21"],
      StyleBox["21", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[21, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{80., 0}, {100., 29}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{157.57142857142856`, 
         206.92857142857142`}, {-95.6078640425167, 97.16293238333606}}],
       StatusArea[#, 29]& ,
       TagBoxNote->"29"],
      StyleBox["29", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[29, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{100., 0}, {120., 24}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{205.92857142857142`, 
         255.28571428571425`}, {-62.54393362426623, 97.16293238333606}}],
       StatusArea[#, 24]& ,
       TagBoxNote->"24"],
      StyleBox["24", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[24, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{120., 0}, {140., 6}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{254.28571428571425`, 303.64285714285717`}, {
         56.486215881435484`, 97.16293238333606}}],
       StatusArea[#, 6]& ,
       TagBoxNote->"6"],
      StyleBox["6", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[6, {
        GrayLevel[0]}], "Tooltip"]& ], 
    TagBox[
     TooltipBox[
      TagBox[
       DynamicBox[{
         FEPrivate`If[
          CurrentValue["MouseOver"], 
          EdgeForm[{
            GrayLevel[0.5], 
            AbsoluteThickness[1.5], 
            Opacity[0.66]}], {}, {}], 
         RectangleBox[{140., 0}, {160., 1}, "RoundingRadius" -> 0]},
        ImageSizeCache->{{302.64285714285717`, 352.}, {89.55014629968596, 
         97.16293238333606}}],
       StatusArea[#, 1]& ,
       TagBoxNote->"1"],
      StyleBox["1", {
        GrayLevel[0]}, StripOnInput -> False]],
     Annotation[#, 
      Style[1, {
        GrayLevel[0]}], "Tooltip"]& ]}, {}, {}},
  AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
  Axes->{True, True},
  AxesLabel->{None, None},
  AxesOrigin->{20., 0},
  FrameTicks->{{Automatic, Automatic}, {Automatic, Automatic}},
  GridLines->{None, None},
  PlotRange->{{20., 160.}, {All, All}},
  PlotRangePadding->{{
     Scaled[0.02], 
     Scaled[0.02]}, {
     Scaled[0.02], 
     Scaled[0.1]}},
  Ticks->{Automatic, Automatic}]], "Output",
 CellChangeTimes->{3.5865402558818007`*^9, 3.5865424349530144`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Print", "[", 
  RowBox[{"\"\<do conjunto de dados = \>\"", ",", 
   RowBox[{"Round", "[", 
    RowBox[{
     RowBox[{"Mean", "[", "q", "]"}], ",", "0.1"}], "]"}], ",", 
   "\"\< +- \>\"", ",", 
   RowBox[{"Round", "[", 
    RowBox[{
     RowBox[{
      RowBox[{"StandardDeviation", "[", "q", "]"}], "/", 
      RowBox[{"Sqrt", "[", 
       RowBox[{"Length", "[", "q", "]"}], "]"}]}], ",", "0.1"}], "]"}], ",", 
   "\"\< estimado das m\[EAcute]dias  \>\"", ",", 
   RowBox[{"z", "[", 
    RowBox[{"x0", ",", "y0"}], "]"}]}], "]"}]], "Input",
 CellChangeTimes->{{3.554919027875841*^9, 3.554919039734841*^9}, {
  3.554919076908841*^9, 3.554919078859841*^9}, {3.5865402728519588`*^9, 
  3.5865403037138934`*^9}}],

Cell[BoxData[
 InterpretationBox[
  RowBox[{"\<\"do conjunto de dados = \"\>", "\[InvisibleSpace]", 
   "85.80000000000001`", "\[InvisibleSpace]", "\<\" +- \"\>", 
   "\[InvisibleSpace]", "2.4000000000000004`", 
   "\[InvisibleSpace]", "\<\" estimado das m\[EAcute]dias  \"\>", 
   "\[InvisibleSpace]", "83.`"}],
  SequenceForm[
  "do conjunto de dados = ", 85.80000000000001, " +- ", 2.4000000000000004`, 
   " estimado das m\[EAcute]dias  ", 83.],
  Editable->False]], "Print",
 CellChangeTimes->{3.586540274258323*^9, 3.586540304667097*^9, 
  3.5865424349530144`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"StandardDeviation", "[", "q", "]"}]], "Input",
 CellChangeTimes->{{3.554919043120841*^9, 3.5549190847638407`*^9}}],

Cell[BoxData["24.349345869354945`"], "Output",
 CellChangeTimes->{
  3.554919054688841*^9, {3.554919086102841*^9, 3.554919147188841*^9}, {
   3.554919242273841*^9, 3.554919242318841*^9}, 3.586539188340209*^9, 
   3.586539641705905*^9, 3.586542434968645*^9}]
}, Open  ]],

Cell["\<\
cuidado, propaga\[CCedilla]\[ATilde]o de incerteza errada para grandezas \
correlacionadas\
\>", "Text",
 CellChangeTimes->{{3.586540838414456*^9, 3.5865408560565434`*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"{", 
   RowBox[{
    RowBox[{"StandardDeviation", "[", "q", "]"}], ",", 
    RowBox[{"Sqrt", "[", 
     RowBox[{
      RowBox[{
       RowBox[{"(", 
        RowBox[{"2", "\[Sigma]x0"}], ")"}], "^", "2"}], "+", 
      RowBox[{
       RowBox[{"(", 
        RowBox[{"6", "y0", " ", "\[Sigma]y0"}], ")"}], "^", "2"}]}], "]"}]}], 
   "}"}], " ", 
  RowBox[{"(*", 
   RowBox[{"opa", ",", " ", 
    RowBox[{
     RowBox[{"propaga\[CCedilla]\[ATilde]o", " ", "errada"}], "..."}]}], " ", 
   "*)"}]}]], "Input",
 CellChangeTimes->{{3.586540406066126*^9, 3.5865404066911774`*^9}, {
  3.5865423653378725`*^9, 3.5865423765263*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"24.349345869354945`", ",", "21.304086462460667`"}], 
  "}"}]], "Output",
 CellChangeTimes->{
  3.586540410832142*^9, {3.5865424051067944`*^9, 3.586542434984267*^9}}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"f", "=", 
  RowBox[{"{", 
   RowBox[{"2", ",", 
    RowBox[{"6", "y0"}]}], "}"}]}]], "Input",
 CellChangeTimes->{{3.5865404282710876`*^9, 3.5865404528668766`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"2", ",", "30.`"}], "}"}]], "Output",
 CellChangeTimes->{3.586540455929633*^9, 3.586542434999895*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"var", "=", 
  RowBox[{"{", 
   RowBox[{
    RowBox[{"{", 
     RowBox[{
      RowBox[{"\[Sigma]x0", "^", "2"}], ",", 
      RowBox[{"\[Rho]", " ", "\[Sigma]x0", " ", "\[Sigma]y0"}]}], "}"}], ",", 
    RowBox[{"{", 
     RowBox[{
      RowBox[{"\[Rho]", " ", "\[Sigma]x0", " ", "\[Sigma]y0"}], ",", 
      RowBox[{"\[Sigma]y0", "^", "2"}]}], "}"}]}], "}"}]}]], "Input",
 CellChangeTimes->{{3.5865404611800685`*^9, 3.586540513715672*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
   RowBox[{"{", 
    RowBox[{"1.`", ",", "0.49984899999999993`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"0.49984899999999993`", ",", "0.49984899999999993`"}], "}"}]}], 
  "}"}]], "Output",
 CellChangeTimes->{3.586540514387602*^9, 3.58654243501552*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"Round", "[", 
   RowBox[{"%", ",", "0.001"}], "]"}], "//", "MatrixForm"}]], "Input",
 CellChangeTimes->{{3.5865405279824758`*^9, 3.586540541467972*^9}}],

Cell[BoxData[
 TagBox[
  RowBox[{"(", "\[NoBreak]", GridBox[{
     {"1.`", "0.5`"},
     {"0.5`", "0.5`"}
    },
    GridBoxAlignment->{
     "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, 
      "RowsIndexed" -> {}},
    GridBoxSpacings->{"Columns" -> {
        Offset[0.27999999999999997`], {
         Offset[0.7]}, 
        Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> {
        Offset[0.2], {
         Offset[0.4]}, 
        Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}],
  Function[BoxForm`e$, 
   MatrixForm[BoxForm`e$]]]], "Output",
 CellChangeTimes->{{3.5865405356237373`*^9, 3.586540542171153*^9}, 
   3.586542435031146*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"{", 
   RowBox[{
    RowBox[{"StandardDeviation", "[", "q", "]"}], ",", 
    RowBox[{"Sqrt", "[", 
     RowBox[{"f", ".", "var", ".", "f"}], "]"}]}], "}"}], " ", 
  RowBox[{"(*", " ", 
   RowBox[{"OK", ",", " ", 
    RowBox[{"covariancia", " ", "incluida", " ", "na", " ", "propagacao"}]}], 
   " ", "*)"}]}]], "Input",
 CellChangeTimes->{{3.5865405558754168`*^9, 3.5865405729080777`*^9}, {
  3.5865423812454414`*^9, 3.5865424012939777`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"24.349345869354945`", ",", "22.668171077526303`"}], 
  "}"}]], "Output",
 CellChangeTimes->{{3.586540564579258*^9, 3.5865405734393716`*^9}, 
   3.586542402059666*^9, 3.586542435046772*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 StyleBox[
  RowBox[{"Vari\[AHat]ncia", " ", "da", " ", "vari\[AHat]ncia"}], 
  "Section"]], "Input",
 CellChangeTimes->{{3.586540893622153*^9, 3.5865409010446463`*^9}}],

Cell[BoxData[
 RowBox[{"da", " ", "vari\[AHat]ncia", " ", "Vari\[AHat]ncia"}]], "Output",
 CellChangeTimes->{3.586542435062398*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"u", "=", 
  RowBox[{"RandomVariate", "[", 
   RowBox[{
    RowBox[{"NormalDistribution", "[", 
     RowBox[{"x0", ",", "\[Sigma]x0"}], "]"}], ",", "10"}], "]"}]}]], "Input",
 CellChangeTimes->{{3.5865409409073215`*^9, 3.586540968065822*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
  "3.2096163569187084`", ",", "4.85393679544729`", ",", "3.7780590758345984`",
    ",", "3.6020089726620075`", ",", "3.9846718030184003`", ",", 
   "4.813733590740025`", ",", "4.5357118718833425`", ",", 
   "3.251509486953472`", ",", "3.8922769859175537`", ",", 
   "5.034016403267759`"}], "}"}]], "Output",
 CellChangeTimes->{3.5865409687065043`*^9, 3.586542435078024*^9}]
}, Open  ]],

Cell[BoxData[
 RowBox[{
  RowBox[{"m", "[", 
   RowBox[{"media_", ",", "sigma_", ",", "n_"}], "]"}], ":=", 
  RowBox[{"Round", "[", 
   RowBox[{
    RowBox[{"RandomVariate", "[", 
     RowBox[{
      RowBox[{"NormalDistribution", "[", 
       RowBox[{"media", ",", "sigma"}], "]"}], ",", "n"}], "]"}], ",", 
    "0.01"}], "]"}]}]], "Input",
 CellChangeTimes->{{3.58654116691043*^9, 3.5865411787551613`*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"m", "[", 
  RowBox[{"x0", ",", "\[Sigma]x0", ",", "10"}], "]"}]], "Input",
 CellChangeTimes->{{3.5865411074836264`*^9, 3.586541118718936*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
  "4.39`", ",", "3.66`", ",", "3.52`", ",", "3.87`", ",", "3.63`", ",", 
   "3.35`", ",", "4.55`", ",", "2.84`", ",", "2.5300000000000002`", ",", 
   "4.2700000000000005`"}], "}"}]], "Output",
 CellChangeTimes->{{3.586541119328357*^9, 3.586541146502488*^9}, {
   3.5865411823960876`*^9, 3.5865411865995617`*^9}, 3.5865424350936513`*^9}]
}, Open  ]],

Cell[BoxData[
 RowBox[{
  RowBox[{"medida", "[", "m_", "]"}], ":=", 
  RowBox[{"{", 
   RowBox[{
    RowBox[{"Mean", "[", "m", "]"}], ",", 
    RowBox[{"StandardDeviation", "[", "m", "]"}]}], "}"}]}]], "Input",
 CellChangeTimes->{{3.586541193318865*^9, 3.5865412149769135`*^9}}],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"medida", "[", "u", "]"}]], "Input",
 CellChangeTimes->{{3.5865412190866246`*^9, 3.586541223696386*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"4.095554134264315`", ",", "0.6717042833046835`"}], "}"}]], "Output",
 CellChangeTimes->{3.586541224555829*^9, 3.5865424351092777`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"medida", "[", 
  RowBox[{"m", "[", 
   RowBox[{"x0", ",", "\[Sigma]x0", ",", "10"}], "]"}], "]"}]], "Input",
 CellChangeTimes->{{3.5865412292593436`*^9, 3.5865412392758017`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"4.158`", ",", "0.9914613232776938`"}], "}"}]], "Output",
 CellChangeTimes->{{3.586541241916643*^9, 3.586541245245043*^9}, 
   3.5865424351249037`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"t", "=", 
  RowBox[{"Table", "[", 
   RowBox[{
    RowBox[{"medida", "[", 
     RowBox[{"m", "[", 
      RowBox[{"x0", ",", "\[Sigma]x0", ",", "10"}], "]"}], "]"}], ",", 
    RowBox[{"{", "10", "}"}]}], "]"}]}]], "Input",
 CellChangeTimes->{{3.5865412627308674`*^9, 3.5865413055625453`*^9}, {
  3.5865415332376633`*^9, 3.5865415352534556`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
   RowBox[{"{", 
    RowBox[{"3.9490000000000007`", ",", "0.6908038795490368`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.882`", ",", "1.1539766991678047`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.172`", ",", "1.0887893378529303`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.164`", ",", "0.48543680215748874`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.596`", ",", "0.6781543580434571`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.88`", ",", "1.076083433361724`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.7619999999999996`", ",", "0.8726944227824283`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.321`", ",", "0.8434644166900119`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.321`", ",", "0.8969633957600127`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.582`", ",", "1.2123512508986642`"}], "}"}]}], "}"}]], "Output",
 CellChangeTimes->{{3.5865412827325287`*^9, 3.5865413065313716`*^9}, 
   3.5865415383005805`*^9, 3.586541621057438*^9, 3.5865424351249037`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"r", "=", 
  RowBox[{"Table", "[", 
   RowBox[{
    RowBox[{"medida", "[", 
     RowBox[{"m", "[", 
      RowBox[{"x0", ",", "\[Sigma]x0", ",", "10"}], "]"}], "]"}], ",", 
    RowBox[{"{", "10000", "}"}]}], "]"}]}]], "Input",
 CellChangeTimes->{{3.5865413124224887`*^9, 3.586541312750641*^9}}],

Cell[BoxData[
 InterpretationBox[
  TagBox[
   PanelBox[GridBox[{
      {
       StyleBox[
        StyleBox[
         DynamicBox[ToBoxes[
           FEPrivate`FrontEndResource["FEStrings", "sizeExplanation"], 
           StandardForm],
          ImageSizeCache->{299., {3., 9.}}],
         StripOnInput->False,
         DynamicUpdating->True], "Panel",
        StripOnInput->False,
        Background->None]},
      {
       ItemBox[
        TagBox[
         RowBox[{"{", 
          RowBox[{
           RowBox[{"{", 
            RowBox[{"4.157`", ",", "1.0074836640528386`"}], "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"4.156000000000001`", ",", "1.257583220131199`"}], "}"}], 
           ",", 
           RowBox[{"{", 
            RowBox[{"4.183`", ",", "1.5873461430254516`"}], "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"3.742`", ",", "1.0235971429772122`"}], "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"3.81`", ",", "0.7720103626247513`"}], "}"}], ",", 
           RowBox[{"\[LeftSkeleton]", "9990", "\[RightSkeleton]"}], ",", 
           RowBox[{"{", 
            RowBox[{"4.006`", ",", "0.9634336741283462`"}], "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"3.947`", ",", "1.0152399825766427`"}], "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"4.1979999999999995`", ",", "1.0167027316007584`"}], 
            "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"4.214`", ",", "1.2518537365753948`"}], "}"}], ",", 
           RowBox[{"{", 
            RowBox[{"4.2490000000000006`", ",", "0.9767002269546852`"}], 
            "}"}]}], "}"}],
         Short[#, 5]& ],
        Background->GrayLevel[1],
        BaseStyle->{Deployed -> False},
        Frame->True,
        FrameStyle->GrayLevel[0, 0.2],
        StripOnInput->False]},
      {
       RowBox[{
        ButtonBox[
         StyleBox[
          StyleBox[
           DynamicBox[ToBoxes[
             FEPrivate`FrontEndResource["FEStrings", "sizeShowLess"], 
             StandardForm],
            ImageSizeCache->{54., {1., 9.}}],
           StripOnInput->False,
           DynamicUpdating->True], "Panel",
          StripOnInput->False,
          Background->None],
         Appearance->Automatic,
         ButtonFunction:>OutputSizeLimit`ButtonFunction[
          Identity, 31, 32340929138483115861, 5/2],
         Enabled->True,
         Evaluator->Automatic,
         Method->"Queued"], "\[ThinSpace]", 
        ButtonBox[
         StyleBox[
          StyleBox[
           DynamicBox[ToBoxes[
             FEPrivate`FrontEndResource["FEStrings", "sizeShowMore"], 
             StandardForm],
            ImageSizeCache->{60., {1., 9.}}],
           StripOnInput->False,
           DynamicUpdating->True], "Panel",
          StripOnInput->False,
          Background->None],
         Appearance->Automatic,
         ButtonFunction:>OutputSizeLimit`ButtonFunction[
          Identity, 31, 32340929138483115861, 5 2],
         Enabled->True,
         Evaluator->Automatic,
         Method->"Queued"], "\[ThinSpace]", 
        ButtonBox[
         StyleBox[
          StyleBox[
           DynamicBox[ToBoxes[
             FEPrivate`FrontEndResource["FEStrings", "sizeShowAll"], 
             StandardForm],
            ImageSizeCache->{92., {3., 9.}}],
           StripOnInput->False,
           DynamicUpdating->True], "Panel",
          StripOnInput->False,
          Background->None],
         Appearance->Automatic,
         ButtonFunction:>OutputSizeLimit`ButtonFunction[
          Identity, 31, 32340929138483115861, Infinity],
         Enabled->True,
         Evaluator->Automatic,
         Method->"Queued"], "\[ThinSpace]", 
        ButtonBox[
         StyleBox[
          StyleBox[
           DynamicBox[ToBoxes[
             FEPrivate`FrontEndResource["FEStrings", "sizeChangeLimit"], 
             StandardForm],
            ImageSizeCache->{78., {1., 9.}}],
           StripOnInput->False,
           DynamicUpdating->True], "Panel",
          StripOnInput->False,
          Background->None],
         Appearance->Automatic,
         ButtonFunction:>FrontEndExecute[{
            FrontEnd`SetOptions[
            FrontEnd`$FrontEnd, 
             FrontEnd`PreferencesSettings -> {"Page" -> "Evaluation"}], 
            FrontEnd`FrontEndToken["PreferencesDialog"]}],
         Evaluator->None,
         Method->"Preemptive"]}]}
     },
     DefaultBaseStyle->"Column",
     GridBoxAlignment->{
      "Columns" -> {{Left}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, 
       "RowsIndexed" -> {}},
     GridBoxDividers->{
      "Columns" -> {{False}}, "ColumnsIndexed" -> {}, "Rows" -> {{False}}, 
       "RowsIndexed" -> {}},
     GridBoxItemSize->{
      "Columns" -> {{Automatic}}, "ColumnsIndexed" -> {}, "Rows" -> {{1.}}, 
       "RowsIndexed" -> {}},
     GridBoxSpacings->{"Columns" -> {
         Offset[0.27999999999999997`], {
          Offset[0.5599999999999999]}, 
         Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> {
         Offset[0.2], {
          Offset[1.2]}, 
         Offset[0.2]}, "RowsIndexed" -> {}}],
    DefaultBaseStyle->{},
    FrameMargins->5],
   Deploy,
   DefaultBaseStyle->"Deploy"],
  Out[31]]], "Output",
 CellChangeTimes->{3.586541314250763*^9, 3.586542435359297*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Mean", "[", "r", "]"}]], "Input",
 CellChangeTimes->{{3.5865413920853376`*^9, 3.586541396366942*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"3.9962746999999874`", ",", "0.9705524370406594`"}], 
  "}"}]], "Output",
 CellChangeTimes->{3.586541397132634*^9, 3.586542435374925*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"medida", "[", "r", "]"}]], "Input",
 CellChangeTimes->{{3.5865413257517147`*^9, 3.5865413382996335`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
   RowBox[{"{", 
    RowBox[{"3.9962746999999874`", ",", "0.9705524370406594`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"0.315641208300101`", ",", "0.2329082225556056`"}], "}"}]}], 
  "}"}]], "Output",
 CellChangeTimes->{3.586541339002817*^9, 3.586542435453055*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"ListPlot", "[", 
  RowBox[{"r", ",", 
   RowBox[{"AxesLabel", "\[Rule]", 
    RowBox[{"{", 
     RowBox[{"\"\<m\[EAcute]dia\>\"", ",", "\[Sigma]"}], "}"}]}], ",", 
   RowBox[{
   "PlotLabel", "\[Rule]", 
    "\"\<(inexist\[EHat]ncia) da correla\[CCedilla]\[ATilde]o \
\!\(\*OverscriptBox[\(x\), \(-\)]\),\[Sigma]\>\""}], ",", 
   RowBox[{"BaseStyle", "\[Rule]", 
    RowBox[{"{", 
     RowBox[{"FontSize", "\[Rule]", "16"}], "}"}]}]}], "]"}]], "Input",
 CellChangeTimes->{{3.586541429244667*^9, 3.5865414331043653`*^9}, {
  3.5865419529599476`*^9, 3.5865419739148054`*^9}, {3.5865420428267703`*^9, 
  3.586542131193468*^9}, {3.586542177384793*^9, 3.58654222446682*^9}, {
  3.586542279908918*^9, 3.586542319302808*^9}}],

Cell[BoxData[
 GraphicsBox[{{}, 
   {RGBColor[0.24720000000000014`, 0.24, 0.6], PointBox[CompressedData["
1:eJxEXXdcTW8YD5HqjnNuioRSlBllZPY+9hYqREZmEoqEyAole5aRoiIjIhlJ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     "]]}, {}},
  AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
  Axes->True,
  AxesLabel->{
    FormBox["\"m\[EAcute]dia\"", TraditionalForm], 
    FormBox["\[Sigma]", TraditionalForm]},
  AxesOrigin->{2.7720000000000002`, 0},
  BaseStyle->{FontSize -> 16},
  ImageSize->{748., Automatic},
  Method->{},
  PlotLabel->FormBox[
   "\"(inexist\[EHat]ncia) da correla\[CCedilla]\[ATilde]o \
\\!\\(\\*OverscriptBox[\\(x\\), \\(-\\)]\\),\[Sigma]\"", TraditionalForm],
  PlotRange->{{2.7720000000000002`, 5.2219999999999995`}, {
    0, 1.8554511281326944`}},
  PlotRangeClipping->True,
  PlotRangePadding->{{0.04899999999999999, 0.04899999999999999}, {
   0.03710902256265389, 0.03710902256265389}}]], "Output",
 CellChangeTimes->{
  3.5865414342294564`*^9, {3.5865422093718185`*^9, 3.5865422254825287`*^9}, {
   3.5865423126616306`*^9, 3.58654232025601*^9}, 3.5865424355468135`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData["t"], "Input",
 CellChangeTimes->{3.586541551395415*^9}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
   RowBox[{"{", 
    RowBox[{"3.9490000000000007`", ",", "0.6908038795490368`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.882`", ",", "1.1539766991678047`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.172`", ",", "1.0887893378529303`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.164`", ",", "0.48543680215748874`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.596`", ",", "0.6781543580434571`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.88`", ",", "1.076083433361724`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.7619999999999996`", ",", "0.8726944227824283`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.321`", ",", "0.8434644166900119`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"4.321`", ",", "0.8969633957600127`"}], "}"}], ",", 
   RowBox[{"{", 
    RowBox[{"3.582`", ",", "1.2123512508986642`"}], "}"}]}], "}"}]], "Output",
 CellChangeTimes->{3.586541552207986*^9, 3.586542435578066*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"t", "[", 
  RowBox[{"[", 
   RowBox[{"All", ",", "1"}], "]"}], "]"}]], "Input",
 CellChangeTimes->{{3.5865415563020706`*^9, 3.5865415613181157`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
  "3.9490000000000007`", ",", "3.882`", ",", "4.172`", ",", "4.164`", ",", 
   "4.596`", ",", "3.88`", ",", "3.7619999999999996`", ",", "3.321`", ",", 
   "4.321`", ",", "3.582`"}], "}"}]], "Output",
 CellChangeTimes->{3.5865415620212946`*^9, 3.5865424355936913`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"t", "[", 
  RowBox[{"[", 
   RowBox[{"All", ",", "2"}], "]"}], "]"}]], "Input",
 CellChangeTimes->{{3.586541568162433*^9, 3.5865415724909167`*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{
  "0.6908038795490368`", ",", "1.1539766991678047`", ",", 
   "1.0887893378529303`", ",", "0.48543680215748874`", ",", 
   "0.6781543580434571`", ",", "1.076083433361724`", ",", 
   "0.8726944227824283`", ",", "0.8434644166900119`", ",", 
   "0.8969633957600127`", ",", "1.2123512508986642`"}], "}"}]], "Output",
 CellChangeTimes->{3.5865415731940975`*^9, 3.5865424356093197`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Variance", "[", 
  RowBox[{"t", "[", 
   RowBox[{"[", 
    RowBox[{"All", ",", "1"}], "]"}], "]"}], " ", "]"}]], "Input",
 CellChangeTimes->{{3.5865415846637964`*^9, 3.586541599836933*^9}}],

Cell[BoxData["0.13603409999999996`"], "Output",
 CellChangeTimes->{{3.586541600352601*^9, 3.586541625104652*^9}, 
   3.5865424356249447`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{"Variance", "[", 
  RowBox[{
   RowBox[{"Table", "[", 
    RowBox[{
     RowBox[{"medida", "[", 
      RowBox[{"m", "[", 
       RowBox[{"x0", ",", "\[Sigma]x0", ",", "10"}], "]"}], "]"}], ",", 
     RowBox[{"{", "10", "}"}]}], "]"}], "[", 
   RowBox[{"[", 
    RowBox[{"All", ",", "1"}], "]"}], "]"}], " ", "]"}]], "Input",
 CellChangeTimes->{{3.5865416624202404`*^9, 3.586541675358817*^9}}],

Cell[BoxData["0.10647978888888877`"], "Output",
 CellChangeTimes->{{3.586541663295313*^9, 3.586541682687549*^9}, 
   3.5865424356405725`*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"{", 
   RowBox[{
    RowBox[{"StandardDeviation", "[", 
     RowBox[{"r", "[", 
      RowBox[{"[", 
       RowBox[{"All", ",", "1"}], "]"}], "]"}], " ", "]"}], " ", ",", 
    RowBox[{"\[Sigma]x0", "/", 
     RowBox[{"Sqrt", "[", "10", "]"}]}]}], "}"}], "  ", 
  RowBox[{"(*", " ", 
   RowBox[{"\[Sigma]media", "=", 
    RowBox[{"\[Sigma]", "/", 
     RowBox[{"\[Sqrt]", "N"}]}]}], " ", "*)"}]}]], "Input",
 CellChangeTimes->{{3.5865416111659966`*^9, 3.58654161132226*^9}, {
  3.5865417153152533`*^9, 3.586541816526766*^9}, {3.5865418904547677`*^9, 
  3.586541900002434*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"0.315641208300101`", ",", "0.31622776601683794`"}], 
  "}"}]], "Output",
 CellChangeTimes->{3.5865416119316826`*^9, 3.5865417456302614`*^9, 
  3.5865417841490793`*^9, 3.586541817339329*^9, 3.586542435656207*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell[BoxData[
 RowBox[{
  RowBox[{"{", 
   RowBox[{
    RowBox[{"StandardDeviation", "[", " ", 
     RowBox[{"r", "[", 
      RowBox[{"[", 
       RowBox[{"All", ",", "2"}], "]"}], "]"}], " ", "]"}], ",", 
    RowBox[{"\[Sigma]x0", "/", 
     RowBox[{"(", 
      RowBox[{"Sqrt", "[", "18", "]"}], ")"}]}]}], "}"}], "  ", 
  RowBox[{"(*", " ", 
   RowBox[{
    RowBox[{"\[Sigma]", 
     RowBox[{"(", "\[Sigma]", ")"}]}], "=", 
    RowBox[{
     RowBox[{"\[Sigma]", "/", 
      RowBox[{"\[Sqrt]", "2"}]}], 
     RowBox[{"(", 
      RowBox[{"N", "-", "1"}], ")"}]}]}], " ", "*)"}]}]], "Input",
 CellChangeTimes->{{3.5865416940166097`*^9, 3.586541702454812*^9}, {
  3.5865418236054773`*^9, 3.586541926817156*^9}}],

Cell[BoxData[
 RowBox[{"{", 
  RowBox[{"0.2329082225556056`", ",", "0.2357022603955158`"}], 
  "}"}]], "Output",
 CellChangeTimes->{
  3.5865417033767595`*^9, {3.5865418655777035`*^9, 3.5865418817977967`*^9}, 
   3.5865424356718254`*^9}]
}, Open  ]]
}, Open  ]]
}, Open  ]]
},
WindowSize->{927, 799},
WindowMargins->{{103, Automatic}, {Automatic, 15}},
PrivateNotebookOptions->{"VersionedStylesheet"->{"Default.nb"[8.] -> False}},
FrontEndVersion->"9.0 for Microsoft Windows (64-bit) (January 25, 2013)",
StyleDefinitions->"Default.nb"
]
(* End of Notebook Content *)

(* Internal cache information *)
(*CellTagsOutline
CellTagsIndex->{}
*)
(*CellTagsIndex
CellTagsIndex->{}
*)
(*NotebookFileOutline
Notebook[{
Cell[CellGroupData[{
Cell[579, 22, 160, 2, 61, "Subtitle"],
Cell[742, 26, 258, 8, 31, "Input"],
Cell[CellGroupData[{
Cell[1025, 38, 463, 10, 92, "Input"],
Cell[1491, 50, 125, 2, 31, "Output"],
Cell[1619, 54, 125, 2, 31, "Output"],
Cell[1747, 58, 125, 2, 31, "Output"],
Cell[1875, 62, 128, 2, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[2040, 69, 158, 3, 39, "Subsubsection"],
Cell[CellGroupData[{
Cell[2223, 76, 668, 15, 52, "Input"],
Cell[2894, 93, 3110, 48, 212, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[6041, 146, 130, 2, 31, "Input"],
Cell[6174, 150, 7897, 241, 246, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[14108, 396, 123, 2, 31, "Input"],
Cell[14234, 400, 209, 3, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[14480, 408, 140, 2, 31, "Input"],
Cell[14623, 412, 208, 3, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[14868, 420, 687, 17, 52, "Input"],
Cell[15558, 439, 619, 13, 23, "Print"]
}, Open  ]],
Cell[CellGroupData[{
Cell[16214, 457, 479, 14, 31, "Input"],
Cell[16696, 473, 227, 5, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[16960, 483, 241, 6, 31, "Input"],
Cell[17204, 491, 125, 2, 31, "Output"]
}, Open  ]]
}, Open  ]],
Cell[CellGroupData[{
Cell[17378, 499, 172, 3, 49, "Subsection"],
Cell[CellGroupData[{
Cell[17575, 506, 539, 12, 31, "Input"],
Cell[18117, 520, 257, 5, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[18411, 530, 815, 19, 31, "Input"],
Cell[19229, 551, 3139, 49, 212, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[22405, 605, 128, 2, 31, "Input"],
Cell[22536, 609, 5188, 159, 245, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[27761, 773, 732, 18, 52, "Input"],
Cell[28496, 793, 570, 12, 23, "Print"]
}, Open  ]],
Cell[CellGroupData[{
Cell[29103, 810, 138, 2, 31, "Input"],
Cell[29244, 814, 257, 4, 31, "Output"]
}, Open  ]],
Cell[29516, 821, 182, 4, 30, "Text"],
Cell[CellGroupData[{
Cell[29723, 829, 654, 20, 31, "Input"],
Cell[30380, 851, 205, 5, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[30622, 861, 187, 5, 31, "Input"],
Cell[30812, 868, 140, 3, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[30989, 876, 460, 12, 31, "Input"],
Cell[31452, 890, 296, 8, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[31785, 903, 187, 4, 31, "Input"],
Cell[31975, 909, 690, 19, 56, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[32702, 933, 477, 12, 31, "Input"],
Cell[33182, 947, 228, 5, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[33447, 957, 183, 4, 54, "Input"],
Cell[33633, 963, 131, 2, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[33801, 970, 265, 6, 31, "Input"],
Cell[34069, 978, 413, 8, 31, "Output"]
}, Open  ]],
Cell[34497, 989, 407, 11, 31, "Input"],
Cell[CellGroupData[{
Cell[34929, 1004, 166, 3, 31, "Input"],
Cell[35098, 1009, 376, 7, 31, "Output"]
}, Open  ]],
Cell[35489, 1019, 278, 7, 31, "Input"],
Cell[CellGroupData[{
Cell[35792, 1030, 127, 2, 31, "Input"],
Cell[35922, 1034, 174, 3, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[36133, 1042, 202, 4, 31, "Input"],
Cell[36338, 1048, 190, 4, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[36565, 1057, 368, 9, 31, "Input"],
Cell[36936, 1068, 1042, 24, 52, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[38015, 1097, 316, 8, 31, "Input"],
Cell[38334, 1107, 5279, 145, 142, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[43650, 1257, 125, 2, 31, "Input"],
Cell[43778, 1261, 176, 4, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[43991, 1270, 129, 2, 31, "Input"],
Cell[44123, 1274, 309, 8, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[44469, 1287, 742, 16, 55, "Input"],
Cell[45214, 1305, 142376, 2341, 506, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[187627, 3651, 69, 1, 31, "Input"],
Cell[187699, 3654, 962, 23, 52, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[188698, 3682, 173, 4, 31, "Input"],
Cell[188874, 3688, 307, 6, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[189218, 3699, 171, 4, 31, "Input"],
Cell[189392, 3705, 420, 8, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[189849, 3718, 213, 5, 31, "Input"],
Cell[190065, 3725, 141, 2, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[190243, 3732, 415, 11, 31, "Input"],
Cell[190661, 3745, 141, 2, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[190839, 3752, 609, 16, 33, "Input"],
Cell[191451, 3770, 251, 5, 31, "Output"]
}, Open  ]],
Cell[CellGroupData[{
Cell[191739, 3780, 709, 21, 33, "Input"],
Cell[192451, 3803, 237, 6, 63, "Output"]
}, Open  ]]
}, Open  ]]
}, Open  ]]
}
]
*)

(* End of internal cache information *)