[ab initio math] section 197th statistical cases

plot summary:
[Machine Xiao Wei] in the [engineer Ah Wei] accompanied by the [nine turn elixir] of the five-turn of the cultivation.
This is a [statistical case] study.

Drama Start:

Star Calendar April 26, 2016 16:34:30, the Milky Way Galaxy Earles the Chinese Empire Jiangnan Line province.
[Engineer Ah Wei] is working with [machine Xiao Wei] to study [statistical cases].

<span style= "FONT-SIZE:18PX;" >x= [165, 165, 157, he, 175, 165, 155, 170]y= [48, 57, 50, 54, 64, 61, 43, 59] Fitting Result: Y =    0.84848 X +  85.71212 , r=   0.79847def linefit (x, y):        N = float (len (x))        sx,sy,sxx,syy,sxy=0,0,0,0,0 for        i in range (0,int (N)):            SX  + x[i]            sy  + y[i]            sxx + x[i]*x[i]            syy + = Y[i]*y[i]            sxy + = X[i]*y[i]        a = (sy*sx /N-SXY)/(sx*sx/n-sxx)        B = (SY-A*SX)/n        r = ABS (SY*SX/N-SXY)/math.sqrt ((sxx-sx*sx/n) * (syy-sy*sy/n))        return a,b,r            def tmp ():        x=[165, 165, 157,, 175, 165, 155, [];       Y=[, the ",", ",", ",", ",";            A,b,r=linefit (x, y) print ("        x=", x) print ("        y=", y) print (        "fit result: Y =%10.5f X +%10.5f, r=%10.5f"% (a,b,r));    </span>

<span style= "FONT-SIZE:18PX;"                  > if (1) {var r = 20;                    Config.setsector (10,5,9,1);                  Config.graphpaper2d (0, 0, R);                                  Config.axis2d (0, 0, 320, 1.6);                Axis set var ScaleX = 2*r, ScaleY = 2*r;                 var SpaceX = 2, SpaceY = 10;                var XS =, XE = 180;                var YS = 0, YE = 70;                  Config.axisspacing (XS, XE, SpaceX, ScaleX, ' X ');                                       Config.axisspacing (YS, YE, SpaceY, ScaleY, ' Y ');                            var x=[165, 165, 157, 54, 175, 165, 155, [], y=[48, 57, 50,, 64, 61, 43, 59];            var array = [];                        var size = X.length;            for (var i = 0; i < size; i++) {Array.push ([x[i], y[i]]);            } var transform = new transform ();                        var tmp = []; Array = Transform. Scale (Transform.translate (array,-xs,-ys), Scalex/spacex, Scaley/spacey);                  TMP = [].concat (array);                   Shape.pointdraw (TMP, ' green ');          array = [];            for (var i = 0; i < size; i++) {Array.push ([x[i], Taskfun (X[i])]);            } array = Transform.scale (transform.translate (array,-xs,-ys), Scalex/spacex, Scaley/spacey);                  TMP = [].concat (array);                 Shape.multilinedraw (TMP, ' red ');          Plot.setfillstyle (' Blue ');                              Plot.filltext (' fitting results: y = 0.84848x-85.71212 ', 30,-270, 200); }} function Taskfun (x) {return 0.84848*x-85.71212;} </span>

<span style= "FONT-SIZE:18PX;" >[21, 23, 25, 27, 29, 32, 35][1.9459101490553132, 2.3978952727983707, 3.044522437723423, 3.1780538303479458, 4.189654742026425, 4.74493212836325, 5.783825182329737]x= [1.9459101490553132, 2.397895272, +, +, +,] 7983707, 3.044522437723423, 3.1780538303479458, 4.189654742026425, 4.74493212836325, 5.783825182329737] fit result: y =    0.27203 x +   -3.84917, r=   0.99260</span>

<span style= "FONT-SIZE:18PX;"                  > if (1) {var r = 20;                    Config.setsector (10,5,9,1);                  Config.graphpaper2d (0, 0, R);                                  Config.axis2d (0, 0, 320, 1.6);                Axis set var ScaleX = 2*r, ScaleY = 2*r;                 var SpaceX = 1.5, SpaceY = 1;                var XS = +, XE = 36;                var YS = 0, YE = 7;                  Config.axisspacing (XS, XE, SpaceX, ScaleX, ' X ');                               Config.axisspacing (YS, YE, SpaceY, ScaleY, ' Y '); var X = [1.9459101490553132, 2.3978952727983707, 3.044522437723423, 3.178053830347945, +, 35];var Y =                        8, 4.189654742026425, 4.74493212836325, 5.783825182329737];            var array = [];                        var size = X.length;            for (var i = 0; i < size; i++) {Array.push ([x[i], y[i]]); } var transform = new transform ();            var tmp = [];                Array = Transform.scale (transform.translate (array,-xs,-ys), Scalex/spacex, Scaley/spacey);                  TMP = [].concat (array);                   Shape.pointdraw (TMP, ' green ');          array = [];            for (var i = 0; i < size; i++) {Array.push ([x[i], Taskfun (X[i])]);            } array = Transform.scale (transform.translate (array,-xs,-ys), Scalex/spacex, Scaley/spacey);                  TMP = [].concat (array);                 Shape.multilinedraw (TMP, ' red ');          Plot.setfillstyle (' Blue ');                              Plot.filltext (' fitting results: y = 0.27203x-3.84917 ', 30,-270, 200); }} function Taskfun (x) {return 0.27203*x-3.84917;} </span>

<span style= "FONT-SIZE:18PX;" >>>> confidence level > 99.9%, k^2 = 56.631879146114834# independence Test def Tmp3 ():    #数据    #[a, b]    #[c, d]    a = 7775;< C6/>b =;    c = 2099;    d =;    Ksquare = (a+b+c+d) * (a*d-b*c) **2/(a+b)/(C+D)/(A+C)/(b+d);    #置信度查对表    Trust = [[0.5,0.455],[0.4,0.708],[0.25,1.323],[0.15,2.072],[0.1,2.706],             [0.025,5.024],[ 0.01,6.635],[0.005,7.879],[0.001,10.828]];    size = Len (trust);    For I in range (size-1,-1,-1):        if Ksquare >= trust[i][1]:            print (' confidence > {0}%, k^2 = {1} '. Format (Round (1-t Rust[i][0]) *100, 3), ksquare));            Return trust[i][0];</span>

<span style= "FONT-SIZE:18PX;" >>>> confidence level > 99.9%, k^2 = 16.37320688824579# Independence Test # example 1def Tmp3 ():    #数据    #[a, b]    #[c, d]    a = 214< C6/>b = 175    c = 451    d = 597    Ksquare = (a+b+c+d) * (a*d-b*c) **2/(a+b)/(C+D)/(A+C)/(b+d);    #置信度查对表    Trust = [[0.5,0.455],[0.4,0.708],[0.25,1.323],[0.15,2.072],[0.1,2.706],             [0.025,5.024],[ 0.01,6.635],[0.005,7.879],[0.001,10.828]];    size = Len (trust);    For I in range (size-1,-1,-1):        if Ksquare >= trust[i][1]:            print (' confidence > {0}%, k^2 = {1} '. Format (Round (1-t Rust[i][0]) *100, 3), ksquare));            Return trust[i][0];</span>

<span style= "FONT-SIZE:18PX;" >>>> confidence level > 97.5%, k^2 = 6.109090909090909# Independence Test # 1def Tmp3 ():    #数据    #[a, b]    #[c, d]    a = 10
   b =    c =    D =    Ksquare = (a+b+c+d) * (a*d-b*c) **2/(a+b)/(C+D)/(A+C)/(b+d);    #置信度查对表    Trust = [[0.5,0.455],[0.4,0.708],[0.25,1.323],[0.15,2.072],[0.1,2.706],             [0.025,5.024],[ 0.01,6.635],[0.005,7.879],[0.001,10.828]];    size = Len (trust);    For I in range (size-1,-1,-1):        if Ksquare >= trust[i][1]:            print (' confidence > {0}%, k^2 = {1} '. Format (Round (1-t Rust[i][0]) *100, 3), ksquare));            Return trust[i][0];</span>

<span style= "FONT-SIZE:18PX;" >>>> x= [126.974, 96.933, 86.656, 63.438, 55.264, 50.976, 39.069, 36.156, 35.209, 32.416]y= [4.224, 3.835, 3.5 1, 3.758, 3.939, 1.809, 2.946, 0.359, 2.48, 2.413] fit result: y =    0.02556 x +    1.33452, r=   0.67615</span>

<span style= "FONT-SIZE:18PX;"                  > if (1) {var r = 20;                    Config.setsector (10,5,9,1);                  Config.graphpaper2d (0, 0, R);                                  Config.axis2d (0, 0, 320, 1.6);                Axis set var ScaleX = 2*r, ScaleY = 2*r;                 var SpaceX = ten, SpaceY = 1;                var XS = 0, XE = 150;                var YS = 0, YE = 10;                  Config.axisspacing (XS, XE, SpaceX, ScaleX, ' X ');                               Config.axisspacing (YS, YE, SpaceY, ScaleY, ' Y '); var X = [126.974, 96.933,86.656,63.438,55.264,50.976,39.069,36.156,35.209,32.416];var Y = [                        4.224,3.835,3.510,3.758,3.939,1.809,2.946,0.359,2.480,2.413];            var array = [];                        var size = X.length;            for (var i = 0; i < size; i++) {Array.push ([x[i], y[i]]);            } var transform = new transform ();    var tmp = [];                    Array = Transform.scale (transform.translate (array,-xs,-ys), Scalex/spacex, Scaley/spacey);                  TMP = [].concat (array);                   Shape.pointdraw (TMP, ' green ');          array = [];            for (var i = 0; i < size; i++) {Array.push ([x[i], Taskfun (X[i])]);            } array = Transform.scale (transform.translate (array,-xs,-ys), Scalex/spacex, Scaley/spacey);                  TMP = [].concat (array);                 Shape.multilinedraw (TMP, ' red ');          Plot.setfillstyle (' Blue ');                              Plot.filltext (' fit result: y = 0.02556*x + 1.33452 ', 30,-270, 200); } </span>

<span style= "FONT-SIZE:18PX;" > Confidence level > 90.0%, k^2 = 3.6889201613659814# Independence Test # 3def Tmp3 ():    #数据    #[a, b]    #[c, d]    a = from    B = 31
   
    c = 8    d =    Ksquare = (a+b+c+d) * (a*d-b*c) **2/(a+b)/(C+D)/(A+C)/(b+d);    #置信度查对表    Trust = [[0.5,0.455],[0.4,0.708],[0.25,1.323],[0.15,2.072],[0.1,2.706],             [0.025,5.024],[ 0.01,6.635],[0.005,7.879],[0.001,10.828]];    size = Len (trust);    For I in range (size-1,-1,-1):        if Ksquare >= trust[i][1]:            print (' confidence > {0}%, k^2 = {1} '. Format (Round (1-t Rust[i][0]) *100, 3), ksquare));            Return trust[i][0];</span>
   

<span style= "FONT-SIZE:18PX;" >>>> x= [126.974, 96.933, 86.656, 63.438, 55.264, 50.976, 39.069, 36.156, 35.209, 32.416]y= [4.224, 3.835, 3.5 1, 3.758, 3.939, 1.809, 2.946, 0.359, 2.48, 2.413] fitting result: y = 0.02556 x + 1.33452, r= 0.67615SSG = 12.8701800999999 Close, SSE = 6.986174058384116, SSR = 5.884006041615882 residuals: [-0.356302026825019, 0.02262360033670774,-0.039669709794743824 , 0.8018423973734929, 1.1917909702046225,-0.8285966637200308, 0.6127770403049579,-1.8997591485062628, 0.24544861984106747, 0.24984492078520049] regression: [1.653002026825019, 0.885076399663292, 0.6223697097947434, 0.02885760262650683,-0.18009097020462272,-0.28970333627996947,-0.594077040304958,-0.6685408514937374,- 0.6927486198410677, -0.7641449207852009]def tmp (): X = [126.974, 96.933,86.656,63.438,55.264,50.976,39.069,36.156,3    5.209,32.416];    Y = [4.224,3.835,3.510,3.758,3.939,1.809,2.946,0.359,2.480,2.413]; A,b,r=linefit (x, y) print ("x=", X) print ("y=", Y) print ("Fit Result: y =%10.5f x +%10.5f, r=%10.5f "% (a,b,r));    size = Len (X);    #平均值 average = SUM (Y)/size;    SST = 0;    #残差 residual = [];    SSE = 0;    #回归 regression = [];    SSR = 0;        For I in range (size): SST + = (y[i]-average) **2;        value = A*x[i]+b;        Residual.append (Y[i]-value);        SSE + = (y[i]-value) **2;        Regression.append (Value-average);            SSR + = (value-average) **2;    Print (' SSG = {0}, SSE = {1}, SSR = {2} '. Format (SST, SSE, SSR));    Print (' residuals: ', residual); Print (' regression: ', regression);</span>

The end of this section, to know how to funeral, please see tell.

[ab initio math] section 197th statistical cases