-
Notifications
You must be signed in to change notification settings - Fork 7
Expand file tree
/
Copy pathgettingstarted.html
More file actions
991 lines (738 loc) · 52.9 KB
/
Copy pathgettingstarted.html
File metadata and controls
991 lines (738 loc) · 52.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
<!DOCTYPE html>
<html lang="en" data-content_root="./" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Getting started — statsmodels 0.15.1 (+22)</title>
<link rel="icon" type="image/png" sizes="32x32" href="_static/icons/favicon-32x32.png">
<link rel="icon" type="image/png" sizes="16x16" href="_static/icons/favicon-16x16.png">
<link rel="manifest" href="_static/icons/site.webmanifest">
<link rel="mask-icon" href="_static/icons/safari-pinned-tab.svg" color="#919191">
<meta name="msapplication-TileColor" content="#2b5797">
<meta name="msapplication-config" content="_static/icons/browserconfig.xml">
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!--
this give us a css class that will be invisible only if js is disabled
-->
<noscript>
<style>
.pst-js-only { display: none !important; }
</style>
</noscript>
<!-- Loaded before other Sphinx assets -->
<link href="_static/styles/theme.css?digest=cb8930eaaf36d9849b67" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=cb8930eaaf36d9849b67" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=b86133f3" />
<link rel="stylesheet" type="text/css" href="_static/graphviz.css?v=4ae1632d" />
<link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
<link rel="stylesheet" type="text/css" href="_static/stylesheets/landing.css?v=c20731d6" />
<link rel="stylesheet" type="text/css" href="_static/stylesheets/examples.css?v=24ca25ad" />
<link rel="stylesheet" type="text/css" href="_static/stylesheets/deprecation.css?v=d9b70072" />
<!-- So that users can add custom icons -->
<script defer src="_static/scripts/fontawesome.js?digest=cb8930eaaf36d9849b67"></script>
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=cb8930eaaf36d9849b67" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=cb8930eaaf36d9849b67" />
<script src="_static/documentation_options.js?v=5929fcd5"></script>
<script src="_static/doctools.js?v=fd6eb6e6"></script>
<script src="_static/sphinx_highlight.js?v=6ffebe34"></script>
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@4/tex-mml-chtml.js"></script>
<script>DOCUMENTATION_OPTIONS.pagename = 'gettingstarted';</script>
<script>
DOCUMENTATION_OPTIONS.theme_version = '0.21.0';
DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://www.statsmodels.org/versions-pydata.json';
DOCUMENTATION_OPTIONS.theme_switcher_version_match = 'devel';
DOCUMENTATION_OPTIONS.show_version_warning_banner =
false;
</script>
<script>DOCUMENTATION_OPTIONS.search_as_you_type = false;</script>
<link rel="canonical" href="https://www.statsmodels.org/devel/gettingstarted.html" />
<link rel="icon" href="_static/favicon.ico"/>
<link rel="author" title="About these documents" href="about.html" />
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="statsmodels.tools.web.webdoc" href="generated/statsmodels.tools.web.webdoc.html" />
<link rel="prev" title="Installing" href="install.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docsearch:version" content="0.15.1 (+22)" />
<meta name="docbuild:last-update" content="Sep 01, 2026"/>
<script src="_static/searchtools.js"></script>
<script src="_static/language_data.js"></script>
<script src="searchindex.js"></script>
</head>
<body data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<dialog id="pst-search-dialog">
<form class="bd-search d-flex align-items-center"
action="search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form>
</dialog>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header id="pst-header" class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
<button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
<span class="fa-solid fa-bars"></span>
</button>
<div class="col-lg-3 navbar-header-items__start">
<div class="navbar-item">
<a class="navbar-brand logo" href="index.html">
<img src="_static/statsmodels-logo-v2-no-text.svg" class="logo__image only-light" alt=""/>
<img src="_static/statsmodels-logo-v3-no-text-light.svg" class="logo__image only-dark pst-js-only" alt=""/>
<p class="title logo__title">statsmodels</p>
</a></div>
</div>
<div class="col-lg-9 navbar-header-items">
<div class="me-auto navbar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="install.html">
Installing
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="#">
Getting started
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="user-guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="examples/index.html">
Examples
</a>
</li>
<li class="nav-item dropdown">
<button class="btn dropdown-toggle nav-item" type="button"
data-bs-toggle="dropdown" aria-expanded="false"
aria-controls="pst-nav-more-links">
More
</button>
<ul id="pst-nav-more-links" class="dropdown-menu">
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="api.html">
API Reference
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="about.html">
About statsmodels
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="dev/index.html">
Developer Page
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="release/index.html">
Release 0.15.0
</a>
</li>
</ul>
</li>
</ul>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<button class="btn search-button-field search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
</div>
<div class="navbar-item">
<div class="version-switcher__container dropdown pst-js-only">
<button id="pst-version-switcher-button-2"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-2"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-2"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-2">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div></div>
<div class="navbar-item">
<div class="theme-switch-container dropdown pst-js-only" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Color mode">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button dropdown-toggle" aria-label="Color mode" data-bs-toggle="dropdown">
<i class="theme-switch fa-solid fa-sun fa-lg fa-fw" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg fa-fw" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg fa-fw" data-mode="auto" title="System Settings"></i>
</button>
<ul class="dropdown-menu dropdown-menu-end">
<li><button class="dropdown-item d-flex align-items-center theme-change-button" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg fa-fw me-1"></i>System Settings</button></li>
<li><button class="dropdown-item d-flex align-items-center theme-change-button" data-mode="light"><i class="fa-solid fa-sun fa-lg fa-fw me-1"></i>Light</button></li>
<li><button class="dropdown-item d-flex align-items-center theme-change-button" data-mode="dark"><i class="fa-solid fa-moon fa-lg fa-fw me-1"></i>Dark</button></li>
</ul>
</div></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/statsmodels/statsmodels/" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-github fa-lg" aria-hidden="true"></i><span class="visually-hidden">GitHub</span></a>
</li>
<li class="nav-item">
<a href="https://pypi.org/project/statsmodels/" title="PyPI" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-python fa-lg" aria-hidden="true"></i><span class="visually-hidden">PyPI</span></a>
</li>
<li class="nav-item">
<a href="https://doi.org/10.5281/zenodo.593847" title="DOI" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-solid fa-quote-left fa-lg" aria-hidden="true"></i><span class="visually-hidden">DOI</span></a>
</li>
</ul></div>
</div>
</div>
<div class="navbar-persistent--mobile">
<button class="btn search-button-field search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
</div>
<button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
<span class="fa-solid fa-outdent"></span>
</button>
</div>
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<dialog id="pst-primary-sidebar-modal"></dialog>
<div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
<div class="sidebar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="install.html">
Installing
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="#">
Getting started
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="user-guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="api.html">
API Reference
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="about.html">
About statsmodels
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="dev/index.html">
Developer Page
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="release/index.html">
Release 0.15.0
</a>
</li>
</ul>
</nav></div>
</div>
<div class="sidebar-header-items__end">
<div class="navbar-item">
<div class="version-switcher__container dropdown pst-js-only">
<button id="pst-version-switcher-button-3"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-3"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-3"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-3">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div></div>
<div class="navbar-item">
<div class="theme-switch-container dropdown pst-js-only" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Color mode">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button dropdown-toggle" aria-label="Color mode" data-bs-toggle="dropdown">
<i class="theme-switch fa-solid fa-sun fa-lg fa-fw" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg fa-fw" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg fa-fw" data-mode="auto" title="System Settings"></i>
</button>
<ul class="dropdown-menu dropdown-menu-end">
<li><button class="dropdown-item d-flex align-items-center theme-change-button" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg fa-fw me-1"></i>System Settings</button></li>
<li><button class="dropdown-item d-flex align-items-center theme-change-button" data-mode="light"><i class="fa-solid fa-sun fa-lg fa-fw me-1"></i>Light</button></li>
<li><button class="dropdown-item d-flex align-items-center theme-change-button" data-mode="dark"><i class="fa-solid fa-moon fa-lg fa-fw me-1"></i>Dark</button></li>
</ul>
</div></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/statsmodels/statsmodels/" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-github fa-lg" aria-hidden="true"></i><span class="visually-hidden">GitHub</span></a>
</li>
<li class="nav-item">
<a href="https://pypi.org/project/statsmodels/" title="PyPI" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-python fa-lg" aria-hidden="true"></i><span class="visually-hidden">PyPI</span></a>
</li>
<li class="nav-item">
<a href="https://doi.org/10.5281/zenodo.593847" title="DOI" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-solid fa-quote-left fa-lg" aria-hidden="true"></i><span class="visually-hidden">DOI</span></a>
</li>
</ul></div>
</div>
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item pst-sidebar-collapse"><button id="pst-collapse-sidebar-button" aria-expanded="true" aria-controls="pst-primary-sidebar">
<svg class="pst-icon" role="img" aria-hidden="true" focusable="false" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg">
<path fill="currentColor" d="M3 15.5C2.36232 15.5 1.74874 15.2564 1.28478 14.8189C0.820828 14.3815 0.541576 13.7832 0.504167 13.1467L0.5 13L0.5 3C0.499965 2.36232 0.743605 1.74874 1.18107 1.28478C1.61854 0.820828 2.21676 0.541576 2.85333 0.504167L3 0.5L13 0.5C13.6377 0.499965 14.2513 0.743605 14.7152 1.18107C15.1792 1.61854 15.4584 2.21676 15.4958 2.85333L15.5 3L15.5 13C15.5 13.6377 15.2564 14.2513 14.8189 14.7152C14.3815 15.1792 13.7832 15.4584 13.1467 15.4958L13 15.5L3 15.5ZM3 13.8333L10.5 13.8333L10.5 2.16667L3 2.16667C2.79589 2.16669 2.59889 2.24163 2.44636 2.37726C2.29383 2.5129 2.19638 2.69979 2.1725 2.9025L2.16667 3L2.16667 13C2.16669 13.2041 2.24163 13.4011 2.37726 13.5536C2.5129 13.7062 2.69979 13.8036 2.9025 13.8275L3 13.8333ZM6.65583 10.325L6.5775 10.2558L4.91083 8.58917C4.76735 8.44567 4.68116 8.25476 4.66843 8.05223C4.65569 7.84971 4.71729 7.6495 4.84167 7.48917L4.91083 7.41083L6.5775 5.74417C6.72747 5.59471 6.9287 5.50794 7.14032 5.50148C7.35194 5.49502 7.55809 5.56935 7.7169 5.70937C7.8757 5.8494 7.97525 6.04463 7.99533 6.25539C8.01541 6.46616 7.95451 6.67667 7.825 6.84417L7.75583 6.9225L6.67917 8L7.75583 9.0775C7.89931 9.22099 7.98551 9.41191 7.99824 9.61443C8.01097 9.81695 7.94938 10.0172 7.825 10.1775L7.75583 10.2558C7.61234 10.3993 7.42142 10.4855 7.2189 10.4982C7.01638 10.511 6.81617 10.4494 6.65583 10.325Z"/>
</svg>
<span class="pst-collapse-sidebar-label">Collapse Sidebar</span>
<span class="pst-expand-sidebar-label">Expand Sidebar</span>
</button></div>
<div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
aria-label="Section Navigation">
<p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
<div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="generated/statsmodels.tools.web.webdoc.html">statsmodels.tools.web.webdoc</a></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
<div class="sidebar-primary-item">
<div id="ethical-ad-placement"
class="flat"
data-ea-publisher="readthedocs"
data-ea-type="readthedocs-sidebar"
data-ea-manual="true">
</div></div>
</div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item">
<nav aria-label="Breadcrumb" class="d-print-none">
<ul class="bd-breadcrumbs">
<li class="breadcrumb-item breadcrumb-home">
<a href="index.html" class="nav-link" aria-label="Home">
<i class="fa-solid fa-home"></i>
</a>
</li>
<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Getting started</span></li>
</ul>
</nav>
</div>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section id="getting-started">
<h1>Getting started<a class="headerlink" href="#getting-started" title="Link to this heading">#</a></h1>
<p>This very simple case-study is designed to get you up-and-running quickly with
<code class="docutils literal notranslate"><span class="pre">statsmodels</span></code>. Starting from raw data, we will show the steps needed to
estimate a statistical model and to draw a diagnostic plot. We will only use
functions provided by <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> or its <code class="docutils literal notranslate"><span class="pre">pandas</span></code> and <code class="docutils literal notranslate"><span class="pre">patsy</span></code>
dependencies.</p>
<section id="loading-modules-and-functions">
<h2>Loading modules and functions<a class="headerlink" href="#loading-modules-and-functions" title="Link to this heading">#</a></h2>
<p>After <a class="reference external" href="install.html">installing statsmodels and its dependencies</a>, we load a
few modules and functions:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sm</span>
<span class="gp">In [2]: </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span>
<span class="gp">In [3]: </span><span class="kn">from</span><span class="w"> </span><span class="nn">patsy</span><span class="w"> </span><span class="kn">import</span> <span class="n">dmatrices</span>
</pre></div>
</div>
<p><a class="reference external" href="https://pandas.pydata.org/">pandas</a> builds on <code class="docutils literal notranslate"><span class="pre">numpy</span></code> arrays to provide
rich data structures and data analysis tools. The <code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> function
provides labelled arrays of (potentially heterogenous) data, similar to the
<code class="docutils literal notranslate"><span class="pre">R</span></code> “data.frame”. The <code class="docutils literal notranslate"><span class="pre">pandas.read_csv</span></code> function can be used to convert a
comma-separated values file to a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> object.</p>
<p><a class="reference external" href="https://github.com/pydata/patsy">patsy</a> is a Python library for describing
statistical models and building <a class="reference external" href="https://en.wikipedia.org/wiki/Design_matrix">Design Matrices</a> using <code class="docutils literal notranslate"><span class="pre">R</span></code>-like formulas.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This example uses the API interface. See <a class="reference internal" href="api-structure.html#importpaths"><span class="std std-ref">Import Paths and Structure</span></a> for information on
the difference between importing the API interfaces (<code class="docutils literal notranslate"><span class="pre">statsmodels.api</span></code> and
<code class="docutils literal notranslate"><span class="pre">statsmodels.tsa.api</span></code>) and directly importing from the module that defines
the model.</p>
</div>
</section>
<section id="data">
<h2>Data<a class="headerlink" href="#data" title="Link to this heading">#</a></h2>
<p>We download the <a class="reference external" href="https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Guerry.html">Guerry dataset</a>, a
collection of historical data used in support of Andre-Michel Guerry’s 1833
<em>Essay on the Moral Statistics of France</em>. The data set is hosted online in
comma-separated values format (CSV) by the <a class="reference external" href="https://github.com/vincentarelbundock/Rdatasets/">Rdatasets</a> repository.
We could download the file locally and then load it using <code class="docutils literal notranslate"><span class="pre">read_csv</span></code>, but
<code class="docutils literal notranslate"><span class="pre">pandas</span></code> takes care of all of this automatically for us:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_rdataset</span><span class="p">(</span><span class="s2">"Guerry"</span><span class="p">,</span> <span class="s2">"HistData"</span><span class="p">)</span><span class="o">.</span><span class="n">data</span>
</pre></div>
</div>
<p>The <a class="reference external" href="iolib.html">Input/Output doc page</a> shows how to import from various
other formats.</p>
<p>We select the variables of interest and look at the bottom 5 rows:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="nb">vars</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Department'</span><span class="p">,</span> <span class="s1">'Lottery'</span><span class="p">,</span> <span class="s1">'Literacy'</span><span class="p">,</span> <span class="s1">'Wealth'</span><span class="p">,</span> <span class="s1">'Region'</span><span class="p">]</span>
<span class="gp">In [6]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="nb">vars</span><span class="p">]</span>
<span class="gp">In [7]: </span><span class="n">df</span><span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">:]</span>
<span class="gh">Out[7]: </span>
<span class="go"> Department Lottery Literacy Wealth Region</span>
<span class="go">81 Vienne 40 25 68 W</span>
<span class="go">82 Haute-Vienne 55 13 67 C</span>
<span class="go">83 Vosges 14 62 82 E</span>
<span class="go">84 Yonne 51 47 30 C</span>
<span class="go">85 Corse 83 49 37 NaN</span>
</pre></div>
</div>
<p>Notice that there is one missing observation in the <em>Region</em> column. We
eliminate it using a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> method provided by <code class="docutils literal notranslate"><span class="pre">pandas</span></code>:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="gp">In [9]: </span><span class="n">df</span><span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">:]</span>
<span class="gh">Out[9]: </span>
<span class="go"> Department Lottery Literacy Wealth Region</span>
<span class="go">80 Vendee 68 28 56 W</span>
<span class="go">81 Vienne 40 25 68 W</span>
<span class="go">82 Haute-Vienne 55 13 67 C</span>
<span class="go">83 Vosges 14 62 82 E</span>
<span class="go">84 Yonne 51 47 30 C</span>
</pre></div>
</div>
</section>
<section id="substantive-motivation-and-model">
<h2>Substantive motivation and model<a class="headerlink" href="#substantive-motivation-and-model" title="Link to this heading">#</a></h2>
<p>We want to know whether literacy rates in the 86 French departments are
associated with per capita wagers on the Royal Lottery in the 1820s. We need to
control for the level of wealth in each department, and we also want to include
a series of dummy variables on the right-hand side of our regression equation to
control for unobserved heterogeneity due to regional effects. The model is
estimated using ordinary least squares regression (OLS).</p>
</section>
<section id="design-matrices-endog-exog">
<h2>Design matrices (<em>endog</em> & <em>exog</em>)<a class="headerlink" href="#design-matrices-endog-exog" title="Link to this heading">#</a></h2>
<p>To fit most of the models covered by <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code>, you will need to create
two design matrices. The first is a matrix of endogenous variable(s) (i.e.
dependent, response, regressand, etc.). The second is a matrix of exogenous
variable(s) (i.e. independent, predictor, regressor, etc.). The OLS coefficient
estimates are calculated as usual:</p>
<div class="math notranslate nohighlight">
\[\hat{\beta} = (X'X)^{-1} X'y\]</div>
<p>where <span class="math notranslate nohighlight">\(y\)</span> is an <span class="math notranslate nohighlight">\(N \times 1\)</span> column of data on lottery wagers per
capita (<em>Lottery</em>). <span class="math notranslate nohighlight">\(X\)</span> is <span class="math notranslate nohighlight">\(N \times 7\)</span> with an intercept, the
<em>Literacy</em> and <em>Wealth</em> variables, and 4 region binary variables.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">patsy</span></code> module provides a convenient function to prepare design matrices
using <code class="docutils literal notranslate"><span class="pre">R</span></code>-like formulas. You can find more information <a class="reference external" href="https://patsy.readthedocs.io/en/latest/">here</a>.</p>
<p>We use <code class="docutils literal notranslate"><span class="pre">patsy</span></code>’s <code class="docutils literal notranslate"><span class="pre">dmatrices</span></code> function to create design matrices:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">y</span><span class="p">,</span> <span class="n">X</span> <span class="o">=</span> <span class="n">dmatrices</span><span class="p">(</span><span class="s1">'Lottery ~ Literacy + Wealth + Region'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">'dataframe'</span><span class="p">)</span>
</pre></div>
</div>
<p>The resulting matrices/data frames look like this:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="n">y</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="gh">Out[11]: </span>
<span class="go"> Lottery</span>
<span class="go">0 41.0</span>
<span class="go">1 38.0</span>
<span class="go">2 66.0</span>
<span class="gp">In [12]: </span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="gh">Out[12]: </span>
<span class="go"> Intercept Region[T.E] Region[T.N] ... Region[T.W] Literacy Wealth</span>
<span class="go">0 1.0 1.0 0.0 ... 0.0 37.0 73.0</span>
<span class="go">1 1.0 0.0 1.0 ... 0.0 51.0 22.0</span>
<span class="go">2 1.0 0.0 0.0 ... 0.0 13.0 61.0</span>
<span class="go">[3 rows x 7 columns]</span>
</pre></div>
</div>
<p>Notice that <code class="docutils literal notranslate"><span class="pre">dmatrices</span></code> has</p>
<ul class="simple">
<li><p>split the categorical <em>Region</em> variable into a set of indicator variables.</p></li>
<li><p>added a constant to the exogenous regressors matrix.</p></li>
<li><p>returned <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames instead of simple numpy arrays. This is useful because DataFrames allow <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> to carry-over meta-data (e.g. variable names) when reporting results.</p></li>
</ul>
<p>The above behavior can of course be altered. See the <a class="reference external" href="https://patsy.readthedocs.io/en/latest/">patsy doc pages</a>.</p>
</section>
<section id="model-fit-and-summary">
<h2>Model fit and summary<a class="headerlink" href="#model-fit-and-summary" title="Link to this heading">#</a></h2>
<p>Fitting a model in <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> typically involves 3 easy steps:</p>
<ol class="arabic simple">
<li><p>Use the model class to describe the model</p></li>
<li><p>Fit the model using a class method</p></li>
<li><p>Inspect the results using a summary method</p></li>
</ol>
<p>For OLS, this is achieved by:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="n">mod</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="c1"># Describe model</span>
<span class="gp">In [14]: </span><span class="n">res</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span> <span class="c1"># Fit model</span>
<span class="gp">In [15]: </span><span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span> <span class="c1"># Summarize model</span>
<span class="go"> OLS Regression Results </span>
<span class="go">==============================================================================</span>
<span class="go">Dep. Variable: Lottery R-squared: 0.338</span>
<span class="go">Model: OLS Adj. R-squared: 0.287</span>
<span class="go">Method: Least Squares F-statistic: 6.636</span>
<span class="go">Date: Tue, 01 Sep 2026 Prob (F-statistic): 1.07e-05</span>
<span class="go">Time: 18:21:15 Log-Likelihood: -375.30</span>
<span class="go">No. Observations: 85 AIC: 764.6</span>
<span class="go">Df Residuals: 78 BIC: 781.7</span>
<span class="go">Df Model: 6 </span>
<span class="go">Covariance Type: nonrobust </span>
<span class="go">===============================================================================</span>
<span class="go"> coef std err t P>|t| [0.025 0.975]</span>
<span class="gt">-------------------------------------------------------------------------------</span>
<span class="n">Intercept</span> <span class="mf">38.6517</span> <span class="mf">9.456</span> <span class="mf">4.087</span> <span class="mf">0.000</span> <span class="mf">19.826</span> <span class="mf">57.478</span>
<span class="n">Region</span><span class="p">[</span><span class="n">T</span><span class="o">.</span><span class="n">E</span><span class="p">]</span> <span class="o">-</span><span class="mf">15.4278</span> <span class="mf">9.727</span> <span class="o">-</span><span class="mf">1.586</span> <span class="mf">0.117</span> <span class="o">-</span><span class="mf">34.793</span> <span class="mf">3.938</span>
<span class="n">Region</span><span class="p">[</span><span class="n">T</span><span class="o">.</span><span class="n">N</span><span class="p">]</span> <span class="o">-</span><span class="mf">10.0170</span> <span class="mf">9.260</span> <span class="o">-</span><span class="mf">1.082</span> <span class="mf">0.283</span> <span class="o">-</span><span class="mf">28.453</span> <span class="mf">8.419</span>
<span class="n">Region</span><span class="p">[</span><span class="n">T</span><span class="o">.</span><span class="n">S</span><span class="p">]</span> <span class="o">-</span><span class="mf">4.5483</span> <span class="mf">7.279</span> <span class="o">-</span><span class="mf">0.625</span> <span class="mf">0.534</span> <span class="o">-</span><span class="mf">19.039</span> <span class="mf">9.943</span>
<span class="n">Region</span><span class="p">[</span><span class="n">T</span><span class="o">.</span><span class="n">W</span><span class="p">]</span> <span class="o">-</span><span class="mf">10.0913</span> <span class="mf">7.196</span> <span class="o">-</span><span class="mf">1.402</span> <span class="mf">0.165</span> <span class="o">-</span><span class="mf">24.418</span> <span class="mf">4.235</span>
<span class="n">Literacy</span> <span class="o">-</span><span class="mf">0.1858</span> <span class="mf">0.210</span> <span class="o">-</span><span class="mf">0.886</span> <span class="mf">0.378</span> <span class="o">-</span><span class="mf">0.603</span> <span class="mf">0.232</span>
<span class="n">Wealth</span> <span class="mf">0.4515</span> <span class="mf">0.103</span> <span class="mf">4.390</span> <span class="mf">0.000</span> <span class="mf">0.247</span> <span class="mf">0.656</span>
<span class="o">==============================================================================</span>
<span class="ne">Omnibus</span>: 3.049 Durbin-Watson: 1.785
<span class="n">Prob</span><span class="p">(</span><span class="n">Omnibus</span><span class="p">):</span> <span class="mf">0.218</span> <span class="n">Jarque</span><span class="o">-</span><span class="n">Bera</span> <span class="p">(</span><span class="n">JB</span><span class="p">):</span> <span class="mf">2.694</span>
<span class="ne">Skew</span>: -0.340 Prob(JB): 0.260
<span class="ne">Kurtosis</span>: 2.454 Cond. No. 371.
<span class="o">==============================================================================</span>
<span class="ne">Notes</span>:
<span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="n">Standard</span> <span class="n">Errors</span> <span class="n">assume</span> <span class="n">that</span> <span class="n">the</span> <span class="n">covariance</span> <span class="n">matrix</span> <span class="n">of</span> <span class="n">the</span> <span class="n">errors</span> <span class="ow">is</span> <span class="n">correctly</span> <span class="n">specified</span><span class="o">.</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">res</span></code> object has many useful attributes. For example, we can extract
parameter estimates and r-squared by typing:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="n">res</span><span class="o">.</span><span class="n">params</span>
<span class="gh">Out[16]: </span>
<span class="go">Intercept 38.651655</span>
<span class="go">Region[T.E] -15.427785</span>
<span class="go">Region[T.N] -10.016961</span>
<span class="go">Region[T.S] -4.548257</span>
<span class="go">Region[T.W] -10.091276</span>
<span class="go">Literacy -0.185819</span>
<span class="go">Wealth 0.451475</span>
<span class="go">dtype: float64</span>
<span class="gp">In [17]: </span><span class="n">res</span><span class="o">.</span><span class="n">rsquared</span>
<span class="gh">Out[17]: </span><span class="go">np.float64(0.3379508691928822)</span>
</pre></div>
</div>
<p>Type <code class="docutils literal notranslate"><span class="pre">dir(res)</span></code> for a full list of attributes.</p>
<p>For more information and examples, see the <a class="reference external" href="regression.html">Regression doc page</a></p>
</section>
<section id="diagnostics-and-specification-tests">
<h2>Diagnostics and specification tests<a class="headerlink" href="#diagnostics-and-specification-tests" title="Link to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> allows you to conduct a range of useful <a class="reference external" href="stats.html#residual-diagnostics-and-specification-tests">regression diagnostics
and specification tests</a>. For instance,
apply the Rainbow test for linearity (the null hypothesis is that the
relationship is properly modelled as linear):</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [18]: </span><span class="n">sm</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">linear_rainbow</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
<span class="gh">Out[18]: </span><span class="go">(np.float64(0.8472339976156905), np.float64(0.6997965543621659))</span>
</pre></div>
</div>
<p>Admittedly, the output produced above is not very verbose, but we know from
reading the <a class="reference external" href="generated/statsmodels.stats.diagnostic.linear_rainbow.html">docstring</a>
(also, <code class="docutils literal notranslate"><span class="pre">print(sm.stats.linear_rainbow.__doc__)</span></code>) that the
first number is an F-statistic and that the second is the p-value.</p>
<p><code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> also provides graphics functions. For example, we can draw a
plot of partial regression for a set of regressors by:</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [19]: </span><span class="n">sm</span><span class="o">.</span><span class="n">graphics</span><span class="o">.</span><span class="n">plot_partregress</span><span class="p">(</span><span class="s1">'Lottery'</span><span class="p">,</span> <span class="s1">'Wealth'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'Region'</span><span class="p">,</span> <span class="s1">'Literacy'</span><span class="p">],</span>
<span class="gp"> ....: </span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">obs_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">result_object</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp"> ....: </span>
<span class="gh">Out[19]: </span>
<span class="go">PartRegressPlotResult(fig=<Figure size 640x480 with 1 Axes>, coords=(array([ 12.22573493, -2.49704997, 8.70405279, 19.45743964,</span>
<span class="go"> 37.27179614, 35.58672228, 16.02598063, 19.35501757,</span>
<span class="go"> -36.43009799, -28.12195183, 3.46747993, -41.24119418,</span>
<span class="go"> -14.02686056, 15.16746222, 39.19041682, -27.39901494,</span>
<span class="go"> 10.70405279, 21.23386338, -33.95990858, 13.78662857,</span>
<span class="go"> 30.40594692, 4.7270074 , -12.84744621, 7.57668199,</span>
<span class="go"> -4.49704997, -22.01818129, -20.68356084, -16.30081536,</span>
<span class="go"> -23.53252007, -3.24119418, -36.92882553, -27.94986829,</span>
<span class="go"> 3.01833328, 3.58481044, -18.71329543, 0.46421963,</span>
<span class="go"> 18.15255379, 30.42890152, -26.71329543, -20.77291661,</span>
<span class="go"> 26.46556809, 27.54814387, -29.66045424, -7.82384596,</span>
<span class="go"> -42.16053024, 28.58672228, -18.92204554, 3.74143473,</span>
<span class="go"> -5.85477702, -19.31763563, -16.80280319, 10.80160672,</span>
<span class="go"> 38.31730652, -9.15375025, 31.32408651, -5.00462132,</span>
<span class="go"> 0.68181356, -8.08648173, 7.68181356, 19.56257121,</span>
<span class="go"> 1.52518927, 37.36250036, 48.81164701, 24.46747993,</span>
<span class="go"> -3.01952975, 13.21217496, -51.01274977, 18.56990201,</span>
<span class="go"> -14.12521213, -3.30272719, -14.09326171, -25.25856527,</span>
<span class="go"> -19.08648173, -17.1461029 , -5.45863611, -6.78837586,</span>
<span class="go"> -4.70460361, -20.35365655, -7.29403537, 20.28861641,</span>
<span class="go"> 5.42890152, 16.01833328, 14.70405279, 32.98047025,</span>
<span class="go"> -6.30950718]), array([-2.78660841e+00, 7.78228844e+00, 6.15369012e+00, 3.97962737e+01,</span>
<span class="go"> 4.79525279e+01, 1.90563450e+01, 7.15185659e+00, 2.04734629e+01,</span>
<span class="go"> -7.02845221e+00, 1.84303108e+00, 3.16487371e+01, -4.35645769e+01,</span>
<span class="go"> -1.68196135e+01, 2.93194543e+01, 1.69957656e+01, -6.80994036e+00,</span>
<span class="go"> -1.58463099e+01, 2.37555921e+01, -1.63035420e+00, 2.10338054e+01,</span>
<span class="go"> 2.91346702e+01, 2.68300014e+01, -1.14550801e+01, 1.22038816e+01,</span>
<span class="go"> 1.67822884e+01, 4.47570850e+00, -1.53642926e+01, -2.57683809e+01,</span>
<span class="go"> -2.43512629e+01, 2.74354231e+01, -3.74118424e+01, -2.47778908e+01,</span>
<span class="go"> -1.03833125e+01, 1.07460822e+01, -3.92729378e+01, -2.39713925e+01,</span>
<span class="go"> 9.54491992e+00, 2.68109815e+01, -8.27293775e+00, -1.14767417e+01,</span>
<span class="go"> 5.33847419e+00, -3.57814105e+01, -3.13014676e+01, 2.58620510e+01,</span>
<span class="go"> 7.00527556e+00, 3.50563450e+01, -2.41795085e+01, 2.55975044e+01,</span>
<span class="go"> -3.44053545e+00, 2.61468219e+01, 1.12280994e+01, -1.04455701e+01,</span>
<span class="go"> 3.69385427e+01, -1.97623906e+01, -1.18291235e+01, 5.94037618e+00,</span>
<span class="go"> -2.56062996e+01, 1.39765825e+01, 2.43937004e+01, -4.01390758e+00,</span>
<span class="go"> -4.45772183e+00, -4.62515629e+00, 3.54068933e+01, -3.83512629e+01,</span>
<span class="go"> -2.88341582e+01, -2.02512761e+01, -3.86018243e+01, 2.99715478e+01,</span>
<span class="go"> -7.77098461e-01, -4.07864373e+00, -2.12557514e+01, -2.44024956e+01,</span>
<span class="go"> -2.34175300e-02, -2.22272215e+01, 2.99862557e+01, -3.00439762e+00,</span>
<span class="go"> 1.32696589e+01, 1.22601490e+01, -2.65360470e+01, -3.89626749e+01,</span>
<span class="go"> 2.18109815e+01, -7.38331249e+00, -4.84630988e+00, -1.98341582e+01,</span>
<span class="go"> 4.68902243e+00])))</span>
</pre></div>
</div>
<img alt="_images/gettingstarted_0.png" src="_images/gettingstarted_0.png" />
</section>
<section id="documentation">
<h2>Documentation<a class="headerlink" href="#documentation" title="Link to this heading">#</a></h2>
<p>Documentation can be accessed from an IPython session
using <a class="reference internal" href="generated/statsmodels.tools.web.webdoc.html#statsmodels.tools.web.webdoc" title="statsmodels.tools.web.webdoc"><code class="xref py py-func docutils literal notranslate"><span class="pre">webdoc</span></code></a>.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.tools.web.webdoc.html#statsmodels.tools.web.webdoc" title="statsmodels.tools.web.webdoc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">webdoc</span></code></a></p></td>
<td><p>Open a browser and display online documentation</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="more">
<h2>More<a class="headerlink" href="#more" title="Link to this heading">#</a></h2>
<p>Congratulations! You’re ready to move on to other topics in the
<a class="reference external" href="index.html#table-of-contents">Table of Contents</a></p>
</section>
</section>
</article>
<footer class="prev-next-footer d-print-none">
<div class="prev-next-area">
<a class="left-prev"
href="install.html"
title="previous page">
<i class="fa-solid fa-angle-left"></i>
<div class="prev-next-info">
<p class="prev-next-subtitle">previous</p>
<p class="prev-next-title">Installing</p>
</div>
</a>
<a class="right-next"
href="generated/statsmodels.tools.web.webdoc.html"
title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
<p class="prev-next-title">statsmodels.tools.web.webdoc</p>
</div>
<i class="fa-solid fa-angle-right"></i>
</a>
</div>
</footer>
</div>
<dialog id="pst-secondary-sidebar-modal"></dialog>
<div id="pst-secondary-sidebar" class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
<div class="sidebar-secondary-item">
<div
id="pst-page-navigation-heading-2"
class="page-toc tocsection onthispage">
<i class="fa-solid fa-list"></i> On this page
</div>
<nav id="pst-page-toc-nav" class="page-toc" aria-labelledby="pst-page-navigation-heading-2">
<ul class="pst-show_toc_level nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-modules-and-functions">Loading modules and functions</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data">Data</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#substantive-motivation-and-model">Substantive motivation and model</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#design-matrices-endog-exog">Design matrices (<em>endog</em> & <em>exog</em>)</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-fit-and-summary">Model fit and summary</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#diagnostics-and-specification-tests">Diagnostics and specification tests</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#documentation">Documentation</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#more">More</a></li>
</ul>
</nav></div>
<div class="sidebar-secondary-item">
<div class="tocsection sourcelink">
<a href="_sources/gettingstarted.rst.txt">
<i class="fa-solid fa-file-lines"></i> Show Source
</a>
</div>
</div>
</div></div>
</div>
<footer class="bd-footer-content">
</footer>
</main>
</div>
</div>
<!-- Scripts loaded after <body> so the DOM is not blocked -->
<script defer src="_static/scripts/bootstrap.js?digest=cb8930eaaf36d9849b67"></script>
<script defer src="_static/scripts/pydata-sphinx-theme.js?digest=cb8930eaaf36d9849b67"></script>
<footer class="bd-footer">
<div class="bd-footer__inner bd-page-width">
<div class="footer-items__start">
<div class="footer-item">
<p class="copyright">
© Copyright 2009-2025, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers.
<br/>
</p>
</div>
<div class="footer-item">
<p class="sphinx-version">
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 9.1.0.
<br/>
</p>
</div>
</div>
<div class="footer-items__end">
<div class="footer-item">
<p class="theme-version">
<!-- # L10n: Setting the PST URL as an argument as this does not need to be localized -->
Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.21.0.
</p></div>
</div>
</div>
</footer>
</body>
</html>