немесе
деп белгілеп, қос сызықтық регрессия теңдеуін аламыз
![](data:image/png;base64,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) (3)
Ең кіші квадраттар әдісін пайдаланып және коэффициенттерін табу қиын емес.
(4)
(4) жүйе белгісіздерден тұрады. Мұндай жүйенің көп жағдайда шексіз көп шешімі болады. Ендеше толық мультиколлинеарлық регрессия теңдеуінің коэффициенттерін бірмәнді анықтауға және түсіндіруші айнымалылардың тәуелді айнымалыға әсер ету мөлшерін анықтауға мүмкіндік бермейді.
Бұл жағдайда аталған коэффициенттер туралы негізделген статистикалық тұжырым жасауға болмайды. Сондықтан мультиколлинеарлық болған жағдайда регрессияның коэффициенттері және регрессия теңдеуі туралы тұжырым сенімді емес. Толық мультиколлинеарлық көбіне теориялық мысал ретінде кездеседі. Нақты жағдайларда түсіндіруші айнымалылар арасындағы корреляциялық тәуелділік тығыз, ал функционалдық тәуелділік қатал емес болады. Мұндай тәуелділік толық емес (жетілмеген) мультиколлинеарлық деп аталады.
Достарыңызбен бөлісу: |