\u0130ki de\u011fi\u015fken ( X<\/em> ve Y)<\/em> aras\u0131ndaki kovaryans\u0131 hesaplamak i\u00e7in kullan\u0131lan form\u00fcl:<\/span><\/p>\n COV(<\/strong> X<\/strong><\/em> ,<\/strong> Y<\/strong><\/em> )<\/strong> = \u03a3(x- x<\/span> )(y- y<\/span> ) \/ n<\/span><\/p>\n Kovaryans matrisi,<\/strong> bir veri k\u00fcmesindeki farkl\u0131 de\u011fi\u015fkenler aras\u0131ndaki kovaryans\u0131 g\u00f6steren bir kare matristir.<\/span><\/p>\n Bu e\u011fitimde SPSS’de belirli bir veri seti i\u00e7in kovaryans matrisinin nas\u0131l olu\u015fturulaca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n 10 farkl\u0131 \u00f6\u011frencinin \u00fc\u00e7 dersteki (matematik, fen bilimleri ve tarih) test puanlar\u0131n\u0131 g\u00f6steren a\u015fa\u011f\u0131daki veri setine sahip oldu\u011fumuzu varsayal\u0131m:<\/span> <\/p>\n Bu veri k\u00fcmesi i\u00e7in bir kovaryans matrisi olu\u015fturmak i\u00e7in Analiz Et<\/strong> sekmesine, ard\u0131ndan Korele Et’e<\/strong> ve ard\u0131ndan \u0130ki De\u011fi\u015fkenli’ye<\/strong> t\u0131klay\u0131n:<\/span> <\/p>\n A\u00e7\u0131lan yeni pencerede, \u00fc\u00e7 de\u011fi\u015fkenin her birini De\u011fi\u015fkenler<\/strong> etiketli kutuya s\u00fcr\u00fckleyin:<\/span> <\/p>\n Ard\u0131ndan Se\u00e7enekler’i<\/strong> t\u0131klay\u0131n. \u00dcr\u00fcnler aras\u0131ndaki farklar ve kovaryanslar’\u0131n<\/strong> yan\u0131ndaki kutuyu i\u015faretleyin. Daha sonra Devam’\u0131<\/strong> t\u0131klay\u0131n.<\/span> <\/p>\n Daha sonra Tamam’\u0131<\/strong> t\u0131klay\u0131n. Sonu\u00e7 yeni bir pencerede g\u00f6r\u00fcnecektir:<\/span> <\/p>\n De\u011fi\u015fkenlerin her ikili kombinasyonunun kovaryans\u0131n\u0131 elde etmek i\u00e7in, kareler ve vekt\u00f6r \u00e7arp\u0131mlar\u0131n\u0131n toplam\u0131n\u0131<\/strong> N’ye<\/strong> b\u00f6lmeniz gerekir.<\/span><\/p>\n \u00d6rne\u011fin matematik ve fen bilimleri aras\u0131ndaki kovaryans \u015fu \u015fekilde hesaplanabilir:<\/span><\/p>\n COV (matematik, fen bilimleri) = 332.000 \/ 10 = 33,2<\/strong> .<\/span><\/p>\n Benzer \u015fekilde matematik ile tarih aras\u0131ndaki kovaryans da \u015fu \u015fekilde hesaplanabilir:<\/span><\/p>\n COV (matematik, tarih) = -244,400 \/ 10 = -24,44<\/strong> .<\/span><\/p>\n Ayr\u0131ca her de\u011fi\u015fkenin varyans\u0131n\u0131 , karelerin ve vekt\u00f6r \u00e7arp\u0131mlar\u0131n\u0131n toplam\u0131n\u0131<\/strong> N’ye<\/strong> b\u00f6lerek de elde edebilirsiniz.<\/span><\/p>\n \u00d6rne\u011fin matematikteki varyans \u015fu \u015fekilde hesaplanabilir:<\/span><\/p>\n VAR(matematik) = 649,600 \/ 10 = 64,96<\/strong> .<\/span><\/p>\n Benzer hesaplamalar\u0131 yaparak bu veri seti i\u00e7in tam kovaryans matrisini elde edebilirsiniz:<\/span> <\/p>\n Kovaryans matrisinin k\u00f6\u015fegenleri boyunca yer alan de\u011ferler, her bir konunun varyanslar\u0131d\u0131r. \u00d6rne\u011fin:<\/span><\/p>\n Matrisin di\u011fer de\u011ferleri farkl\u0131 konular aras\u0131ndaki kovaryanslar\u0131 temsil eder. \u00d6rne\u011fin:<\/span><\/p>\n Kovaryans i\u00e7in pozitif bir say\u0131,<\/strong> iki de\u011fi\u015fkenin art arda artma veya azalma e\u011filiminde oldu\u011funu g\u00f6sterir. \u00d6rne\u011fin matematik ve fen bilimleri aras\u0131nda pozitif bir kovaryans (33.2) vard\u0131r; bu da matematikte y\u00fcksek puan alan \u00f6\u011frencilerin fen bilimlerinde de y\u00fcksek puan alma e\u011filiminde oldu\u011funu g\u00f6sterir. Benzer \u015fekilde matematikte zay\u0131f performans g\u00f6steren \u00f6\u011frenciler fen alan\u0131nda da d\u00fc\u015f\u00fck performans g\u00f6sterme e\u011filimindedir.<\/span><\/p>\n Kovaryans i\u00e7in negatif bir say\u0131,<\/strong> bir de\u011fi\u015fken artarken ikinci bir de\u011fi\u015fkenin azalma e\u011filiminde oldu\u011funu g\u00f6sterir. \u00d6rne\u011fin, bilim ve tarihin negatif bir kovaryans\u0131 (-24,1) vard\u0131r, bu da fen bilimlerinde y\u00fcksek puan alan \u00f6\u011frencilerin tarihte d\u00fc\u015f\u00fck puan alma e\u011filiminde oldu\u011funu g\u00f6sterir. Benzer \u015fekilde fen bilimlerinde d\u00fc\u015f\u00fck puan alan \u00f6\u011frenciler tarihte de y\u00fcksek puan alma e\u011filimindedir.<\/span><\/p>\n SPSS’de Korelasyon Matrisi Nas\u0131l Olu\u015fturulur<\/a> Kovaryans , bir de\u011fi\u015fkendeki de\u011fi\u015fikliklerin ikinci bir de\u011fi\u015fkendeki de\u011fi\u015fikliklerle nas\u0131l ili\u015fkilendirildi\u011finin bir \u00f6l\u00e7\u00fcs\u00fcd\u00fcr. Daha spesifik olarak, iki de\u011fi\u015fkenin do\u011frusal olarak ili\u015fkili olma derecesinin bir \u00f6l\u00e7\u00fcs\u00fcd\u00fcr. \u0130ki de\u011fi\u015fken ( X ve Y) aras\u0131ndaki kovaryans\u0131 hesaplamak i\u00e7in kullan\u0131lan form\u00fcl: COV( X , Y ) = \u03a3(x- x )(y- y ) \/ n Kovaryans matrisi, bir veri k\u00fcmesindeki […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-817","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek: SPSS’deki Kovaryans matrisi<\/strong><\/h3>\n
![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/covariancespss1.png\")
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<\/p>\n![\"SPSS'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/covariancespss5.png\")
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<\/p>\n Kovaryans matrisi nas\u0131l yorumlan\u0131r?<\/strong><\/h3>\n
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Ek kaynaklar<\/strong><\/h3>\n
SPSS’de k\u0131smi korelasyon nas\u0131l hesaplan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"