{"id":476219,"date":"2023-08-09T07:26:52","date_gmt":"2023-08-09T07:26:52","guid":{"rendered":""},"modified":"2023-11-30T03:36:11","modified_gmt":"2023-11-30T03:36:11","slug":"chi-squared-test","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/chi-squared-test\/","title":{"rendered":"Ki-kare testi"},"content":{"rendered":"<p>Ki-Kare testi, kategorik verileri analiz etmek ve iki veya daha fazla de\u011fi\u015fken aras\u0131nda anlaml\u0131 bir ili\u015fki olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in kullan\u0131lan istatistiksel bir y\u00f6ntemdir. Parametrik olmayan bir testtir, yani verilerin da\u011f\u0131t\u0131m\u0131 hakk\u0131nda hi\u00e7bir varsay\u0131mda bulunmaz ve sosyal bilimler, biyoloji, t\u0131p ve pazarlama gibi \u00e7e\u015fitli alanlarda yayg\u0131n olarak kullan\u0131l\u0131r. Test, verilerdeki kategorilerin g\u00f6zlemlenen s\u0131kl\u0131klar\u0131n\u0131n beklenen s\u0131kl\u0131klardan \u00f6nemli \u00f6l\u00e7\u00fcde farkl\u0131 olup olmad\u0131\u011f\u0131n\u0131 de\u011ferlendirerek de\u011fi\u015fkenler aras\u0131ndaki ili\u015fkilere ili\u015fkin de\u011ferli bilgiler sa\u011flar.<\/p>\n<h2>Ki-Kare Testinin K\u00f6keni Tarihi<\/h2>\n<p>Ki-Kare testinin k\u00f6kleri, kavram\u0131 1900 y\u0131l\u0131nda ortaya koyan \u0130ngiliz matematik\u00e7i ve biyoistatistik\u00e7i Karl Pearson&#039;un \u00e7al\u0131\u015fmalar\u0131na dayanmaktad\u0131r. Pearson&#039;un \u00e7al\u0131\u015fmas\u0131, b\u00fcy\u00fck veri k\u00fcmelerindeki de\u011fi\u015fkenler aras\u0131ndaki ili\u015fkileri anlamak i\u00e7in istatistiksel y\u00f6ntemler geli\u015ftirmeye odaklanm\u0131\u015ft\u0131r. Ki-Kare testi ilk olarak iki veya daha fazla kategorik de\u011fi\u015fkenin ortak da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6steren beklenmedik durum tablolar\u0131n\u0131n analizinde uyguland\u0131.<\/p>\n<h2>Ki-Kare Testi Hakk\u0131nda Detayl\u0131 Bilgi<\/h2>\n<p>Ki-Kare testi, bir veri setinde g\u00f6zlemlenen frekanslar\u0131 (O), de\u011fi\u015fkenlerin ba\u011f\u0131ms\u0131z olmas\u0131 durumunda ortaya \u00e7\u0131kacak beklenen frekanslarla (E) kar\u015f\u0131la\u015ft\u0131rmaya dayan\u0131r. Test, g\u00f6zlemlenen ve beklenen frekanslar aras\u0131ndaki fark\u0131 \u00f6l\u00e7en Ki-Kare istatisti\u011finin hesaplanmas\u0131n\u0131 i\u00e7erir. Ki-Kare istatisti\u011finin form\u00fcl\u00fc \u015f\u00f6yledir:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/oneproxy.pro\/images\/chi_squared_formula.png\" alt=\"Ki-Kare Form\u00fcl\u00fc\" title=\"\"><\/p>\n<p>Nerede:<\/p>\n<ul>\n<li>\u03a7\u00b2 Ki-Kare istatisti\u011fini temsil eder<\/li>\n<li>O\u1d62 i kategorisi i\u00e7in g\u00f6zlemlenen frekanst\u0131r<\/li>\n<li>E\u1d62 i kategorisi i\u00e7in beklenen frekanst\u0131r<\/li>\n<li>\u03a3 t\u00fcm kategorilerdeki toplam\u0131 belirtir<\/li>\n<\/ul>\n<p>Ki-Kare istatisti\u011fi bir Ki-Kare da\u011f\u0131l\u0131m\u0131n\u0131 takip eder ve de\u011feri testle ili\u015fkili p-de\u011ferini belirlemek i\u00e7in kullan\u0131l\u0131r. P de\u011feri, g\u00f6zlemlenen sonu\u00e7lar\u0131n yaln\u0131zca \u015fans eseri elde edilme olas\u0131l\u0131\u011f\u0131n\u0131 g\u00f6sterir. P de\u011feri \u00f6nceden belirlenmi\u015f bir anlaml\u0131l\u0131k seviyesinin (genellikle 0,05) alt\u0131ndaysa, bo\u015f hipotez (de\u011fi\u015fkenlerin ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131) reddedilir ve de\u011fi\u015fkenler aras\u0131nda anlaml\u0131 bir ili\u015fki oldu\u011fu ileri s\u00fcr\u00fcl\u00fcr.<\/p>\n<h2>Ki-Kare Testinin \u0130\u00e7 Yap\u0131s\u0131<\/h2>\n<p>Ki-Kare testi iki ana t\u00fcre ayr\u0131labilir: Pearson&#039;un Ki-Kare testi ve Olabilirlik Oran\u0131 Ki-Kare testi (G-Testi olarak da bilinir). Her iki test de Ki-Kare istatisti\u011fi i\u00e7in ayn\u0131 form\u00fcl\u00fc kullan\u0131r, ancak beklenen frekanslar\u0131 hesaplama y\u00f6ntemleri farkl\u0131d\u0131r.<\/p>\n<ol>\n<li>Pearson&#039;un Ki-Kare Testi:\n<ul>\n<li>De\u011fi\u015fkenlerin yakla\u015f\u0131k olarak normal da\u011f\u0131l\u0131ma sahip oldu\u011fu varsay\u0131lmaktad\u0131r.<\/li>\n<li>Genellikle \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fc b\u00fcy\u00fck oldu\u011funda kullan\u0131l\u0131r.<\/li>\n<\/ul>\n<\/li>\n<li>Olas\u0131l\u0131k Oran\u0131 Ki-Kare Testi (G-Testi):\n<ul>\n<li>Olabilirlik oran\u0131na dayal\u0131 olarak verilerin da\u011f\u0131l\u0131m\u0131 hakk\u0131nda daha az varsay\u0131mda bulunulmas\u0131.<\/li>\n<li>K\u00fc\u00e7\u00fck numune boyutlar\u0131 veya beklenen frekans\u0131n be\u015ften az oldu\u011fu durumlar i\u00e7in uygundur.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>Ki-Kare Testinin Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>Ki-Kare testi, onu de\u011ferli bir istatistiksel ara\u00e7 haline getiren birka\u00e7 temel \u00f6zelli\u011fe sahiptir:<\/p>\n<ul>\n<li><strong>Kategorik Veri Analizi:<\/strong> Ki-Kare testi, kategorik veriler i\u00e7in \u00f6zel olarak tasarlanm\u0131\u015ft\u0131r ve ara\u015ft\u0131rmac\u0131lar\u0131n say\u0131sal olmayan verilerden anlaml\u0131 sonu\u00e7lar \u00e7\u0131karmas\u0131na olanak tan\u0131r.<\/li>\n<li><strong>Parametrik Olmayan Test:<\/strong> Parametrik olmayan bir test olan Ki-Kare testi, verilerin belirli bir da\u011f\u0131l\u0131m\u0131 takip etmesini gerektirmez, bu da onu \u00e7ok y\u00f6nl\u00fc ve \u00e7e\u015fitli senaryolara uygulanabilir hale getirir.<\/li>\n<li><strong>Ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131n De\u011ferlendirilmesi:<\/strong> Test, iki veya daha fazla kategorik de\u011fi\u015fken aras\u0131nda bir ili\u015fki olup olmad\u0131\u011f\u0131n\u0131n belirlenmesine yard\u0131mc\u0131 olarak verilerdeki kal\u0131plar\u0131n ve ili\u015fkilerin anla\u015f\u0131lmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<li><strong>\u00c7\u0131kar\u0131m Testi:<\/strong> Ki-Kare testi, bir p de\u011feri sa\u011flayarak ara\u015ft\u0131rmac\u0131lar\u0131n veriler hakk\u0131nda istatistiksel \u00e7\u0131kar\u0131mlar yapmas\u0131na ve belirli bir g\u00fcven d\u00fczeyinde sonu\u00e7 \u00e7\u0131karmas\u0131na olanak tan\u0131r.<\/li>\n<\/ul>\n<h2>Ki-Kare Testi T\u00fcrleri<\/h2>\n<p>\u0130ki ana Ki-Kare testi t\u00fcr\u00fc vard\u0131r: Pearson&#039;un Ki-Kare testi ve Olabilirlik Oran\u0131 Ki-Kare testi. \u0130\u015fte \u00f6zelliklerinin bir kar\u015f\u0131la\u015ft\u0131rmas\u0131:<\/p>\n<table>\n<thead>\n<tr>\n<th>Kriterler<\/th>\n<th>Pearson&#039;un Ki-Kare Testi<\/th>\n<th>Olas\u0131l\u0131k Oran\u0131 Ki-Kare Testi<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Varsay\u0131mlar<\/td>\n<td>Verilerin normal da\u011f\u0131l\u0131m\u0131n\u0131 varsayar<\/td>\n<td>Veri da\u011f\u0131t\u0131m\u0131 hakk\u0131nda daha az varsay\u0131mda bulunur<\/td>\n<\/tr>\n<tr>\n<td>K\u00fc\u00e7\u00fck numune boyutlar\u0131 i\u00e7in uygundur<\/td>\n<td>HAYIR<\/td>\n<td>Evet<\/td>\n<\/tr>\n<tr>\n<td>Kullan\u0131m \u00f6rnekleri<\/td>\n<td>B\u00fcy\u00fck numune boyutlar\u0131<\/td>\n<td>K\u00fc\u00e7\u00fck numune boyutlar\u0131<\/td>\n<\/tr>\n<tr>\n<td>Form\u00fcl<\/td>\n<td><img decoding=\"async\" src=\"https:\/\/oneproxy.pro\/images\/pearsons_chi_squared_formula.png\" alt=\"Pearson&#039;un Ki-Kare Form\u00fcl\u00fc\" title=\"\"><\/td>\n<td><img decoding=\"async\" src=\"https:\/\/oneproxy.pro\/images\/likelihood_ratio_chi_squared_formula.png\" alt=\"Olas\u0131l\u0131k Oran\u0131 Ki-Kare Form\u00fcl\u00fc\" title=\"\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Ki-Kare Testini Kullanma Yollar\u0131, Problemler ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<p>Ki-Kare testi a\u015fa\u011f\u0131dakiler de dahil olmak \u00fczere \u00e7e\u015fitli alanlarda uygulamalar bulur:<\/p>\n<ol>\n<li><strong>Formda olman\u0131n g\u00fczelli\u011fi:<\/strong> G\u00f6zlemlenen frekanslar\u0131n beklenen bir da\u011f\u0131l\u0131ma uyup uymad\u0131\u011f\u0131n\u0131 belirleyin.<\/li>\n<li><strong>Ba\u011f\u0131ms\u0131zl\u0131k Testi:<\/strong> \u0130ki kategorik de\u011fi\u015fkenin ili\u015fkili olup olmad\u0131\u011f\u0131n\u0131 de\u011ferlendirin.<\/li>\n<li><strong>Homojenlik Testi:<\/strong> Kategorik de\u011fi\u015fkenlerin farkl\u0131 gruplardaki da\u011f\u0131l\u0131m\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/li>\n<\/ol>\n<p>Ki-Kare testiyle ilgili olas\u0131 sorunlar \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>K\u00fc\u00e7\u00fck \u00d6rnek Boyutu:<\/strong> Ki-Kare testi, k\u00fc\u00e7\u00fck numune boyutlar\u0131 veya beklenen frekanslar\u0131 be\u015ften az olan h\u00fccreler i\u00e7in hatal\u0131 sonu\u00e7lar verebilir. Bu gibi durumlarda Olabilirlik Oran\u0131 Ki-Kare testi tercih edilir.<\/li>\n<li><strong>S\u0131ra verileri:<\/strong> Ki-Kare testi kategorilerin s\u0131ras\u0131n\u0131 dikkate almad\u0131\u011f\u0131ndan s\u0131ral\u0131 veriler i\u00e7in uygun de\u011fildir.<\/li>\n<\/ul>\n<p>Bu sorunlar\u0131 \u00e7\u00f6zmek i\u00e7in ara\u015ft\u0131rmac\u0131lar, k\u00fc\u00e7\u00fck \u00f6rneklem boyutlar\u0131 i\u00e7in Fisher&#039;in Kesin Testi veya s\u0131ral\u0131 veriler i\u00e7in di\u011fer parametrik olmayan testler gibi alternatif testleri kullanabilirler.<\/p>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<p>Ki-Kare testi di\u011fer istatistiksel testlerle benzerlikler ta\u015f\u0131r ancak ayn\u0131 zamanda onu farkl\u0131 k\u0131lan benzersiz \u00f6zelliklere de sahiptir:<\/p>\n<table>\n<thead>\n<tr>\n<th>karakteristik<\/th>\n<th>Ki-Kare Testi<\/th>\n<th>T-Testi<\/th>\n<th>ANOVA<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Test T\u00fcr\u00fc<\/td>\n<td>Kategorik Veri Analizi<\/td>\n<td>Ortalamalar\u0131n Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/td>\n<td>Ortalamalar\u0131n Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/td>\n<\/tr>\n<tr>\n<td>De\u011fi\u015fken Say\u0131s\u0131<\/td>\n<td>2 veya daha fazla<\/td>\n<td>2<\/td>\n<td>3 veya daha fazla<\/td>\n<\/tr>\n<tr>\n<td>Veri tipi<\/td>\n<td>Kategorik<\/td>\n<td>S\u00fcrekli<\/td>\n<td>S\u00fcrekli<\/td>\n<\/tr>\n<tr>\n<td>Varsay\u0131mlar<\/td>\n<td>Parametrik olmayan<\/td>\n<td>Normal Da\u011f\u0131l\u0131m Varsayal\u0131m<\/td>\n<td>Normal Da\u011f\u0131l\u0131m Varsayal\u0131m<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Ki-Kare Testine \u0130li\u015fkin Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Veri analizi \u00e7e\u015fitli end\u00fcstrilerde \u00f6nemli bir rol oynamaya devam ederken, Ki-Kare testi kategorik verileri analiz etmek i\u00e7in temel bir ara\u00e7 olmaya devam edecek. Bununla birlikte, istatistiksel metodolojiler ve teknolojilerdeki ilerlemeler, Ki-Kare testinin geli\u015ftirilmi\u015f versiyonlar\u0131na veya uzant\u0131lar\u0131na yol a\u00e7arak s\u0131n\u0131rlamalar\u0131na de\u011finebilir ve onu daha \u00e7ok y\u00f6nl\u00fc ve g\u00fc\u00e7l\u00fc hale getirebilir.<\/p>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Ki-Kare Testiyle \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy gibi sa\u011flay\u0131c\u0131lar taraf\u0131ndan sunulan proxy sunucular\u0131, Ki-Kare testlerinin y\u00fcr\u00fct\u00fclmesi i\u00e7in veri toplanmas\u0131n\u0131 ve analizini kolayla\u015ft\u0131rabilir. Kullan\u0131c\u0131lar\u0131n farkl\u0131 co\u011frafi konumlara eri\u015fmesine olanak tan\u0131r; bu, \u00f6zellikle b\u00f6lgesel farkl\u0131l\u0131klara sahip veri k\u00fcmeleriyle u\u011fra\u015f\u0131rken faydal\u0131d\u0131r. Proxy sunucular\u0131 ayn\u0131 zamanda anonimlik sa\u011flayarak onlar\u0131 web kaz\u0131ma ve veri toplama g\u00f6revleri i\u00e7in de\u011ferli k\u0131lar ve ara\u015ft\u0131rmac\u0131lar\u0131n analizlerinin gizlili\u011fini ve g\u00fcvenli\u011fini korumalar\u0131na yard\u0131mc\u0131 olur.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>Ki-Kare testi hakk\u0131nda daha fazla bilgi i\u00e7in a\u015fa\u011f\u0131daki kaynaklar\u0131 inceleyebilirsiniz:<\/p>\n<ol>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Chi-squared_test\" target=\"_new\" rel=\"noopener nofollow\">Vikipedi \u2013 Ki-Kare Testi<\/a><\/li>\n<li><a href=\"https:\/\/www.statisticssolutions.com\/non-parametric-analysis-chi-square\/\" target=\"_new\" rel=\"noopener nofollow\">\u0130statistik \u00c7\u00f6z\u00fcmleri \u2013 Ki-Kare Testi<\/a><\/li>\n<li><a href=\"https:\/\/www.graphpad.com\/guides\/prism\/8\/statistics\/stat_interpreting_results_chi-square_test.htm\" target=\"_new\" rel=\"noopener nofollow\">GraphPad Prism \u2013 Ki-Kare Testi<\/a><\/li>\n<li><a href=\"https:\/\/ncss-wpengine.netdna-ssl.com\/wp-content\/themes\/ncss\/pdf\/Procedures\/NCSS\/Chi-Square_Test.pdf\" target=\"_new\" rel=\"noopener nofollow\">NCSS \u2013 Ki-Kare Testi<\/a><\/li>\n<\/ol>\n<p>Sonu\u00e7 olarak, Ki-Kare testi kategorik verileri analiz etmek ve de\u011fi\u015fkenler aras\u0131ndaki ili\u015fkileri belirlemek i\u00e7in g\u00fc\u00e7l\u00fc bir istatistiksel y\u00f6ntemdir. \u00c7ok y\u00f6nl\u00fcl\u00fc\u011f\u00fc, kullan\u0131m kolayl\u0131\u011f\u0131 ve \u00e7e\u015fitli alanlardaki uygulamalar\u0131 onu hem ara\u015ft\u0131rmac\u0131lar hem de veri analistleri i\u00e7in \u00f6nemli bir ara\u00e7 haline getiriyor. Teknoloji ilerledik\u00e7e Ki-Kare testi de muhtemelen geli\u015fmeye devam edecek, yenilik\u00e7i metodolojiler ve ara\u00e7larla tamamlanacak ve kategorik veri ili\u015fkilerine dair daha derin bilgiler sunulacakt\u0131r.<\/p>","protected":false},"featured_media":497617,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476219","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Chi-Squared Test: A Comprehensive Overview<\/mark>","faq_items":[{"question":"What is the Chi-Squared test, and how does it work?","answer":"The Chi-Squared test is a statistical method used to analyze categorical data and determine if there is a significant association between two or more variables. It compares observed frequencies with expected frequencies and provides valuable insights into the relationships between variables."},{"question":"Who introduced the Chi-Squared test and when was it first mentioned?","answer":"The Chi-Squared test was introduced by Karl Pearson, a British mathematician and biostatistician, in 1900. He developed this method to analyze the relationships between variables in large datasets."},{"question":"What is the difference between Pearson's Chi-Squared test and the Likelihood Ratio Chi-Squared test?","answer":"Both Pearson's Chi-Squared test and the Likelihood Ratio Chi-Squared test are used to analyze categorical data, but they differ in their assumptions and applications. Pearson's test assumes normal distribution and is suitable for large sample sizes, while the Likelihood Ratio test makes fewer assumptions and is more appropriate for small sample sizes or cases with expected frequencies less than five."},{"question":"In what situations is the Chi-Squared test commonly used?","answer":"The Chi-Squared test finds applications in various scenarios, including goodness of fit testing, independence testing, and homogeneity testing. It is widely used in social sciences, biology, medicine, marketing, and other fields where categorical data analysis is essential."},{"question":"What problems may arise when using the Chi-Squared test?","answer":"The Chi-Squared test may yield inaccurate results with small sample sizes or cells with expected frequencies less than five. In such cases, the Likelihood Ratio Chi-Squared test is preferred. Additionally, the test is not suitable for ordinal data, as it does not consider the order of categories."},{"question":"How can OneProxy's proxy servers be associated with the Chi-Squared test?","answer":"OneProxy's proxy servers facilitate data collection and analysis by offering access to different geographical locations and ensuring anonymity. Researchers can use proxy servers for web scraping and data gathering tasks, enhancing privacy and security while conducting Chi-Squared tests."},{"question":"What are the advantages of using the Chi-Squared test?","answer":"The Chi-Squared test is a non-parametric test, meaning it makes no assumptions about data distribution. It is suitable for categorical data analysis, providing valuable insights into associations between variables. Additionally, it allows researchers to draw statistical inferences and make confident conclusions based on the obtained p-values."},{"question":"Where can I find more information about the Chi-Squared test?","answer":"For further information about the Chi-Squared test, you can explore additional resources, such as Wikipedia's page on Chi-Squared test, Statistics Solutions' guide, and GraphPad Prism's interpretation of results. Visit OneProxy.pro to learn more about proxy servers' benefits and applications."}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476219","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476219\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/497617"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476219"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}