rfin

Stat/TP1/3_Pyramide_des_ages/pa.py

@@ -40,7 +40,7 @@ def ex1_3_4():
     axes2.plot(data['2019_F']-data['2019_H'],data['age'],label="Différence",color="green")
     axes2.set_title("2019")
     fig.suptitle("Pyramide des ages en France")
-    axes1.legend(handles=[line1, line2,line3],loc='lower center', bbox_to_anchor=(0.5, -0.2, 0., 0.5),ncol=3)
+    #axes1.legend(handles=[line1, line2,line3],loc='lower center', bbox_to_anchor=(0.5, -0.2, 0., 0.5),ncol=3)
     fig.tight_layout()
     plt.show()
 

Stat/TP2/1_Correlation_lineaire/ExAsetA.csv

+x,y
+1.66682253523682,1.6933312906529299
+1.9798324641605407,2.0364079286370838
+1.723740278334009,1.8699489420268274
+1.716738638335051,1.7934590422794545
+1.7572019608715153,1.724191260530918
+1.7128236623145292,1.611283961632483
+1.4356423932965918,1.4597001484133523
+1.9214554554292935,1.9298095708727983
+1.5533981568451292,1.5177357384146326
+1.994902623470273,1.9693550553997061
+1.671861041552685,1.694422292428485
+1.328628077559553,1.3175072625461275
+1.2018198903492894,1.2996850674067788
+1.949546739426648,1.8979854430541028
+1.5346784924919425,1.624305619261097

Stat/TP2/1_Correlation_lineaire/ExAsetB.csv

+x,y
+2.959857425632835,0.09614976443288098
+2.820299457583266,0.2609419938242007
+2.499176001543351,0.45921796025963224
+2.8131452004056756,0.19898503166467565
+2.260174444899172,0.6161078394970023
+2.1664360785922985,0.8585215169693703
+3.0056357124336546,0.06297495601881718
+2.2505109146262714,0.740488416846824
+2.7340684650959943,0.3150932238423231
+2.1971113883120243,0.8121167756233967
+2.4256037612386496,0.7024383232806811
+2.102684939364214,0.8736390068172122
+2.8004625406232293,0.2861213110002471
+2.8935494071246004,0.12557308307063414
+2.5383508380548276,0.5290381234225771

Stat/TP2/1_Correlation_lineaire/ExBsetA.csv

+x,y
+1.0007574322589754,0.9545973217592059
+0.9891307774541653,1.004727276499499
+0.9848428424365473,1.0295502709968907
+1.0015260962969876,0.9886039422641724
+1.0043820746964234,0.9752271300993305
+0.995818063402338,0.9962012053114743
+1.000215112846741,0.963062961006867
+0.964422229925692,0.9963631869439187
+0.9983846751050857,1.0118547061938419
+1.006766433672599,1.0023081088285481
+0.9764262164756188,0.9652473760494117
+1.0393023939876607,1.0104980296221109
+0.9898504066230317,0.9985772653900656
+0.9760801628675354,1.0035933902003416
+1.0116897404763951,0.986179585502337

Stat/TP2/1_Correlation_lineaire/ExBsetB.csv

+x,y
+1.5,1.5

Stat/TP2/1_Correlation_lineaire/ExCsetA.csv

+x,y
+0.9881614489045576,0.9940631010678133
+2.0021247117025527,1.4149645619953006
+3.0136779142792602,1.7359947909712345
+3.9949051598674843,1.9987258841240547
+4.995769234426853,2.2351217493521136
+6.002740554205586,2.4500490922031717
+7.024769079076701,2.6504280935495497
+8.017234670554286,2.83147217372064
+8.9746817211973,2.9957773150214786
+9.97685728131858,3.158616355513689
+10.990025913580038,3.315120799243979
+12.006552897582656,3.4650473153454424
+12.989632149669925,3.604113226532974
+13.969641790863797,3.7375983988202632
+14.99851036023298,3.8727910297656107
+16.01512261146078,4.001889879976806
+17.019554768880393,4.125476308122542
+18.010719265503766,4.24390377665467
+18.968928886360022,4.355333384066026
+19.978854640174426,4.469771206692175

Stat/TP2/1_Correlation_lineaire/ExDsetA.csv

+x,y
+-4.983756072204823,24.837824587238444
+-4.47757365620345,20.048665846727133
+-3.9493220810944925,15.597144900220533
+-3.4451425630401715,11.869007279671003
+-2.856846771324043,8.161573474824609
+-2.3783789594700395,5.656686474849788
+-1.8065779335871914,3.2637238301241664
+-1.319886261671772,1.742099743749885
+-0.8285736268283929,0.6865342550755569
+-0.2376606793478902,0.05648259850810068
+0.2699742376853415,0.07288608901378127
+0.7855050542706168,0.6170181902846846
+1.30193037017,1.6950226887709934
+1.8821452579262605,3.5424707719343096
+2.3557910398678543,5.549751423521666
+2.907951703891357,8.456183112164647
+3.401831633342589,11.572458461610308
+3.9252597353376157,15.40766398986273
+4.486216662183872,20.126139940056202
+5.012310083714155,25.1232523753026

Stat/TP2/1_Correlation_lineaire/PercenRevParlVSEtude.csv

+Parent Income Rank,College Attendance Rates
+0,"0,246"
+1,"0,261"
+2,"0,271"
+3,"0,282"
+4,"0,288"
+5,"0,299"
+6,"0,306"
+7,"0,314"
+8,"0,319"
+9,"0,323"
+10,"0,329"
+11,"0,337"
+12,"0,343"
+13,"0,349"
+14,"0,356"
+15,"0,360"
+16,"0,369"
+17,"0,377"
+18,"0,380"
+19,"0,389"
+20,"0,395"
+21,"0,403"
+22,"0,410"
+23,"0,415"
+24,"0,418"
+25,"0,427"
+26,"0,432"
+27,"0,440"
+28,"0,443"
+29,"0,451"
+30,"0,459"
+31,"0,468"
+32,"0,472"
+33,"0,479"
+34,"0,487"
+35,"0,493"
+36,"0,501"
+37,"0,506"
+38,"0,509"
+39,"0,519"
+40,"0,523"
+41,"0,530"
+42,"0,533"
+43,"0,541"
+44,"0,544"
+45,"0,552"
+46,"0,562"
+47,"0,564"
+48,"0,569"
+49,"0,577"
+50,"0,585"
+51,"0,593"
+52,"0,594"
+53,"0,606"
+54,"0,609"
+55,"0,612"
+56,"0,620"
+57,"0,629"
+58,"0,635"
+59,"0,640"
+60,"0,646"
+61,"0,654"
+62,"0,660"
+63,"0,666"
+64,"0,672"
+65,"0,684"
+66,"0,689"
+67,"0,696"
+68,"0,703"
+69,"0,713"
+70,"0,716"
+71,"0,726"
+72,"0,732"
+73,"0,739"
+74,"0,747"
+75,"0,754"
+76,"0,764"
+77,"0,771"
+78,"0,779"
+79,"0,786"
+80,"0,792"
+81,"0,803"
+82,"0,811"
+83,"0,819"
+84,"0,823"
+85,"0,833"
+86,"0,842"
+87,"0,850"
+88,"0,861"
+89,"0,866"
+90,"0,875"
+91,"0,885"
+92,"0,893"
+93,"0,900"
+94,"0,908"
+95,"0,914"
+96,"0,920"
+97,"0,927"
+98,"0,934"
+99,"0,938"

Stat/TP2/1_Correlation_lineaire/cl.py

+
+import csv as csv
+from turtle import color
+from typing import List
+from unicodedata import decimal
+import matplotlib.pyplot as plt
+from psutil import LINUX
+
+
+def csvopen(A):
+    with open(A, newline='') as csvfile:
+        data = csv.reader(csvfile, delimiter=',')
+        Liste=[]
+        for row in data:
+            Liste.append(list(row))
+        for i in range(1,len(Liste)):
+            Liste[i] = [float(s) for s in Liste[i]] 
+
+        Lx=[]
+        Ly=[]
+        for i in range(1,len(Liste)):
+            Lx.append(Liste[i][0])
+            Ly.append(Liste[i][1])
+    return Lx,Ly
+
+def point_moyen(X,Y):
+    moyX=sum(X)/len(X)
+    moyY=sum(Y)/len(Y)
+    return moyX,moyY
+    
+def coeff_corr(X,Y):
+    add=0
+    for i in range(len(X)):
+        add+=X[i]*Y[i]
+    add=add/len(X)
+    mX,mY=point_moyen(X,Y)
+    return add-mX*mY
+
+def coeffs_droite_reg(X,Y):   
+    mX,mY=point_moyen(X,Y)
+    a=coeff_corr(X,Y)/coeff_corr(X,X)
+    b=mY-(coeff_corr(X,Y)/coeff_corr(X,X)*mX)
+    
+    return a,b
+
+def etude(X,Y):
+    ig, ax = plt.subplots()
+    mX,mY=point_moyen(X,Y)
+    ax.scatter(X, Y,color='blue')
+    ax.scatter(mX, mY,color='Orange')
+    a,b=coeffs_droite_reg(X,Y)
+    correc=[]
+    for i in X:
+        correc.append(a*i+b)
+    ax.plot(X, correc,color='Orange')
+    ax.set_title(f"{coeff_corr(X,Y)}")
+    plt.show()
+
+def main():
+    Lx,Ly=csvopen("ExBsetA.csv")
+    L2x,L2y=csvopen("ExBsetB.csv")
+    Lx+=L2x
+    Ly+=L2y
+    etude(Lx,Ly)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file

Stat/TP2/2_Donnees_trompeuses/regLinData.dat

+#            x           y
+    1.000000     5.350009
+    1.045226     5.330573
+    1.090452     5.846848
+    1.135678     4.565231
+    1.180905     4.629330
+    1.226131     5.989690
+    1.271357     5.846016
+    1.316583     5.025292
+    1.361809     5.096260
+    1.407035     5.814693
+    1.452261     5.318524
+    1.497487     6.351204
+    1.542714     6.739752
+    1.587940     5.983371
+    1.633166     6.704139
+    1.678392     6.827779
+    1.723618     6.776082
+    1.768844     6.826517
+    1.814070     6.278642
+    1.859296     7.571817
+    1.904523     6.444861
+    1.949749     7.583053
+    1.994975     7.351510
+    2.040201     6.520457
+    2.085427     7.698788
+    2.130653     6.824046
+    2.175879     7.764227
+    2.221106     7.837668
+    2.266332     7.567468
+    2.311558     8.400373
+    2.356784     7.561973
+    2.402010     9.095670
+    2.447236     8.881552
+    2.492462     9.174303
+    2.537688     8.821539
+    2.582915     8.589116
+    2.628141     9.343108
+    2.673367     8.473726
+    2.718593     9.303052
+    2.763819     9.436755
+    2.809045     8.800928
+    2.854271    10.150936
+    2.899497     8.806074
+    2.944724     9.474539
+    2.989950     9.485253
+    3.035176    10.150876
+    3.080402     9.991898
+    3.125628     9.888448
+    3.170854     9.716515
+    3.216080    10.981903
+    3.261307     9.604266
+    3.306533    10.889372
+    3.351759    10.458565
+    3.396985    10.092394
+    3.442211    10.431283
+    3.487437    10.451493
+    3.532663    11.093604
+    3.577889    10.404577
+    3.623116    11.040341
+    3.668342    11.413664
+    3.713568    11.769462
+    3.758794    11.648653
+    3.804020    11.797463
+    3.849246    11.152528
+    3.894472    12.047036
+    3.939698    12.080987
+    3.984925    12.648235
+    4.030151    12.200534
+    4.075377    11.598411
+    4.120603    12.383052
+    4.165829    11.824136
+    4.211055    12.563547
+    4.256281    13.422814
+    4.301508    12.871729
+    4.346734    12.352129
+    4.391960    13.390047
+    4.437186    14.121084
+    4.482412    13.810747
+    4.527638    13.280983
+    4.572864    14.007683
+    4.618090    14.839108
+    4.663317    14.665825
+    4.708543    13.713035
+    4.753769    14.959268
+    4.798995    14.305504
+    4.844221    14.257616
+    4.889447    13.899227
+    4.934673    15.351871
+    4.979899    15.722959
+    5.025126    15.399200
+    5.070352    15.401633
+    5.115578    14.475479
+    5.160804    15.386240
+    5.206030    15.240553
+    5.251256    15.660105
+    5.296482    15.371186
+    5.341709    16.035321
+    5.386935    15.353563
+    5.432161    16.211999
+    5.477387    16.514905
+    5.522613    15.607548
+    5.567839    16.314286
+    5.613065    16.099081
+    5.658291    16.119861
+    5.703518    16.157991
+    5.748744    17.125820
+    5.793970    17.748102
+    5.839196    17.343995
+    5.884422    18.428096
+    5.929648    17.097640
+    5.974874    17.204464
+    6.020101    18.157160
+    6.065327    18.443616
+    6.110553    18.441188
+    6.155779    18.415586
+    6.201005    18.936797
+    6.246231    18.744447
+    6.291457    19.312904
+    6.336683    18.147518
+    6.381910    19.786496
+    6.427136    18.276405
+    6.472362    19.360537
+    6.517588    18.941676
+    6.562814    18.847312
+    6.608040    19.670921
+    6.653266    18.967241
+    6.698492    19.784692
+    6.743719    20.355868
+    6.788945    19.520793
+    6.834171    20.118747
+    6.879397    21.303050
+    6.924623    20.744193
+    6.969849    21.592399
+    7.015075    21.700377
+    7.060302    21.648098
+    7.105528    20.824499
+    7.150754    21.681058
+    7.195980    22.421424
+    7.241206    22.119458
+    7.286432    21.442126
+    7.331658    21.505945
+    7.376884    22.332306
+    7.422111    22.289120
+    7.467337    22.742114
+    7.512563    21.848816
+    7.557789    22.332000
+    7.603015    22.903375
+    7.648241    22.833165
+    7.693467    23.068874
+    7.738693    24.113747
+    7.783920    24.138965
+    7.829146    22.989233
+    7.874372    24.090274
+    7.919598    23.956019
+    7.964824    24.021956
+    8.010050    24.364882
+    8.055276    25.200686
+    8.100503    24.471920
+    8.145729    25.209996
+    8.190955    23.901772
+    8.236181    24.868866
+    8.281407    24.249084
+    8.326633    24.834749
+    8.371859    25.109737
+    8.417085    24.830084
+    8.462312    26.448559
+    8.507538    26.089365
+    8.552764    25.224476
+    8.597990    25.357383
+    8.643216    26.201686
+    8.688442    26.635145
+    8.733668    26.034418
+    8.778894    26.440065
+    8.824121    26.620932
+    8.869347    27.168616
+    8.914573    27.387710
+    8.959799    26.606733
+    9.005025    26.906442
+    9.050251    28.101335
+    9.095477    28.461973
+    9.140704    27.451136
+    9.185930    27.921858
+    9.231156    28.330908
+    9.276382    27.685475
+    9.321608    29.232629
+    9.366834    29.529234
+    9.412060    28.297275
+    9.457286    28.803801
+    9.502513    29.744560
+    9.547739    28.656249
+    9.592965    29.211632
+    9.638191    29.045113
+    9.683417    29.685106
+    9.728643    30.384333
+    9.773869    29.762931
+    9.819095    30.551164
+    9.864322    30.785315
+    9.909548    30.181993
+    9.954774    30.174788
+   10.000000    30.584167