{"id":478465,"date":"2023-08-09T09:33:12","date_gmt":"2023-08-09T09:33:12","guid":{"rendered":""},"modified":"2023-09-05T11:16:48","modified_gmt":"2023-09-05T11:16:48","slug":"polynomial-regression","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/my\/wiki\/polynomial-regression\/","title":{"rendered":"Regresi polinomial"},"content":{"rendered":"<p>Regresi polinomial ialah sejenis analisis regresi dalam statistik yang berkaitan dengan pemodelan hubungan antara pembolehubah bebas. <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>X<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">X<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.07847em;\">X<\/span><\/span><\/span><\/span><\/span> dan pembolehubah bersandar <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>y<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">y<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><\/span><\/span><\/span><\/span> sebagai polinomial darjah ke-n. Tidak seperti regresi linear, yang memodelkan perhubungan sebagai garis lurus, regresi polinomial memadankan lengkung ke titik data, memberikan kesesuaian yang lebih fleksibel.<\/p>\n<h2>Sejarah Asal Usul Regresi Polinomial dan Sebutan Pertamanya<\/h2>\n<p>Regresi polinomial berakar umbi dalam bidang interpolasi polinomial yang lebih luas, yang bermula sejak karya matematik Isaac Newton dan Carl Friedrich Gauss. Kaedah interpolasi polinomial Newton telah dibangunkan pada akhir abad ke-17 dan menyediakan salah satu teknik terawal untuk menyesuaikan lengkung polinomial pada titik data.<\/p>\n<p>Dalam konteks analisis regresi, regresi polinomial mula mendapat daya tarikan pada abad ke-20 apabila alat pengiraan maju, membolehkan pemodelan perhubungan antara pembolehubah yang lebih kompleks.<\/p>\n<h2>Maklumat Terperinci tentang Regresi Polinomial. Memperluas Regresi Polinomial Topik<\/h2>\n<p>Regresi polinomial berkembang pada regresi linear mudah dengan membenarkan hubungan antara pembolehubah bebas dan pembolehubah bersandar dimodelkan sebagai persamaan polinomial dalam bentuk:<br \/>\n<span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>y<\/mi><mo>=<\/mo><msub><mi>\u03b2<\/mi><mn>0<\/mn><\/msub><mo>+<\/mo><msub><mi>\u03b2<\/mi><mn>1<\/mn><\/msub><mi>x<\/mi><mo>+<\/mo><msub><mi>\u03b2<\/mi><mn>2<\/mn><\/msub><msup><mi>x<\/mi><mn>2<\/mn><\/msup><mo>+<\/mo><mo>\u2026<\/mo><mo>+<\/mo><msub><mi>\u03b2<\/mi><mi>n<\/mi><\/msub><msup><mi>x<\/mi><mi>n<\/mi><\/msup><mo>+<\/mo><mi>\u03f5<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">y = beta_0 + beta_1 x + beta_2 x^2 + ldots + beta_n x^n + epsilon<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><span class=\"mrel\">=<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3011em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">0<\/span><\/span><\/span><\/span><span class=\"vlist-s\">,<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3011em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><span class=\"vlist-s\">,<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord mathnormal\">x<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.0085em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3011em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><span class=\"vlist-s\">,<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\"><span class=\"mord mathnormal\">x<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8141em;\"><span style=\"top: -3.063em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6667em; vertical-align: -0.0833em;\"><\/span><span class=\"minner\">\u2026<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.1514em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">n<\/span><\/span><\/span><\/span><span class=\"vlist-s\">,<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\"><span class=\"mord mathnormal\">x<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.6644em;\"><span style=\"top: -3.063em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">n<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">\u03f5<\/span><\/span><\/span><\/span><\/span><\/p>\n<h3>Penjelasan Persamaan:<\/h3>\n<ul>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>y<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">y<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><\/span><\/span><\/span><\/span>: Pembolehubah bersandar<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><msub><mi>\u03b2<\/mi><mi>i<\/mi><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">beta_i<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">i<\/span><\/span><\/span><\/span><span class=\"vlist-s\">,<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>: Pekali<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>x<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">x<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">x<\/span><\/span><\/span><\/span><\/span>: Pembolehubah bebas<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>\u03f5<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">epsilon<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">\u03f5<\/span><\/span><\/span><\/span><\/span>: Istilah ralat<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>n<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">n<\/span><\/span><\/span><\/span><\/span>: Darjah polinomial<\/li>\n<\/ul>\n<p>Dengan memasangkan persamaan polinomial pada data, model boleh menangkap perhubungan tak linear dan memberikan pemahaman yang lebih bernuansa tentang corak asas dalam data.<\/p>\n<h2>Struktur Dalaman Regresi Polinomial. Bagaimana Regresi Polinomial Berfungsi<\/h2>\n<p>Regresi polinomial berfungsi dengan mencari pekali yang meminimumkan jumlah perbezaan kuasa dua antara nilai yang diperhatikan dan nilai yang diramalkan oleh model polinomial. Proses ini biasanya dilakukan melalui kaedah kuasa dua terkecil.<\/p>\n<h3>Langkah-langkah dalam Regresi Polinomial:<\/h3>\n<ol>\n<li><strong>Pilih Darjah Polinomial<\/strong>: Darjah polinomial mesti dipilih berdasarkan hubungan asas dalam data.<\/li>\n<li><strong>Mengubah Data<\/strong>: Cipta ciri polinomial untuk darjah yang dipilih.<\/li>\n<li><strong>Sesuaikan Model<\/strong>: Gunakan teknik regresi linear untuk mencari pekali yang meminimumkan ralat.<\/li>\n<li><strong>Nilaikan Model<\/strong>: Menilai kesesuaian model menggunakan metrik seperti R-kuadrat, ralat kuasa dua min, dsb.<\/li>\n<\/ol>\n<h2>Analisis Ciri Utama Regresi Polinomial<\/h2>\n<ul>\n<li><strong>Fleksibiliti<\/strong>: Boleh memodelkan hubungan tak linear.<\/li>\n<li><strong>Kesederhanaan<\/strong>: Memanjangkan regresi linear dan boleh diselesaikan dengan teknik linear.<\/li>\n<li><strong>Risiko Overfitting<\/strong>: Polinomial darjah lebih tinggi boleh melebihkan data, menangkap bunyi daripada isyarat.<\/li>\n<li><strong>Tafsiran<\/strong>: Tafsiran boleh menjadi lebih mencabar berbanding regresi linear mudah.<\/li>\n<\/ul>\n<h2>Jenis Regresi Polinomial<\/h2>\n<p>Regresi polinomial boleh dikategorikan berdasarkan darjah polinomial:<\/p>\n<table>\n<thead>\n<tr>\n<th>Ijazah<\/th>\n<th>Penerangan<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1<\/td>\n<td>Linear (Garisan Lurus)<\/td>\n<\/tr>\n<tr>\n<td>2<\/td>\n<td>Kuadratik (Keluk Parabola)<\/td>\n<\/tr>\n<tr>\n<td>3<\/td>\n<td>Kubik (Lengkung Berbentuk S)<\/td>\n<\/tr>\n<tr>\n<td>n<\/td>\n<td>Keluk Polinomial darjah ke-<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Cara Menggunakan Regresi Polinomial, Masalah dan Penyelesaiannya Berkaitan dengan Penggunaan<\/h2>\n<h3>Kegunaan:<\/h3>\n<ul>\n<li>Ekonomi dan kewangan untuk memodelkan arah aliran tak linear.<\/li>\n<li>Sains alam sekitar untuk memodelkan corak pertumbuhan.<\/li>\n<li>Kejuruteraan untuk analisis sistem.<\/li>\n<\/ul>\n<h3>Masalah dan Penyelesaian:<\/h3>\n<ul>\n<li><strong>Terlalu pasang<\/strong>: Penyelesaian adalah dengan menggunakan pengesahan silang dan regularisasi.<\/li>\n<li><strong>Multikolineariti<\/strong>: Penyelesaian adalah dengan menggunakan penskalaan atau transformasi.<\/li>\n<\/ul>\n<h2>Ciri Utama dan Perbandingan Lain dengan Istilah Serupa<\/h2>\n<table>\n<thead>\n<tr>\n<th>ciri-ciri<\/th>\n<th>Regresi Polinomial<\/th>\n<th>Regresi Linear<\/th>\n<th>Regresi Tak Linear<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Perhubungan<\/td>\n<td>Tak linear<\/td>\n<td>Linear<\/td>\n<td>Tak linear<\/td>\n<\/tr>\n<tr>\n<td>Fleksibiliti<\/td>\n<td>tinggi<\/td>\n<td>rendah<\/td>\n<td>Pembolehubah<\/td>\n<\/tr>\n<tr>\n<td>Kerumitan Pengiraan<\/td>\n<td>Sederhana<\/td>\n<td>rendah<\/td>\n<td>tinggi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perspektif dan Teknologi Masa Depan Berkaitan dengan Regresi Polinomial<\/h2>\n<p>Kemajuan dalam pembelajaran mesin dan kecerdasan buatan berkemungkinan meningkatkan aplikasi regresi polinomial, menggabungkan teknik seperti regularisasi, kaedah ensemble dan penalaan hiperparameter automatik.<\/p>\n<h2>Bagaimana Pelayan Proksi Boleh Digunakan atau Dikaitkan dengan Regresi Polinomial<\/h2>\n<p>Pelayan proksi, seperti yang disediakan oleh OneProxy, boleh digunakan bersama dengan regresi polinomial dalam pengumpulan dan analisis data. Dengan membenarkan capaian yang selamat dan tanpa nama kepada data, pelayan proksi boleh memudahkan pengumpulan maklumat untuk pemodelan, memastikan hasil yang tidak berat sebelah dan pematuhan kepada peraturan privasi.<\/p>\n<h2>Pautan Berkaitan<\/h2>\n<ul>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Polynomial_regression\" target=\"_new\" rel=\"noopener nofollow\">Wikipedia: Regresi Polinomial<\/a><\/li>\n<li><a href=\"https:\/\/www.jstor.org\/stable\/xyz\" target=\"_new\" rel=\"noopener nofollow\">Kaedah Pembelajaran Statistik untuk Regresi Polinomial<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/my\/\" target=\"_new\" rel=\"noopener\">OneProxy: Pengumpulan Data Selamat untuk Analisis<\/a><\/li>\n<\/ul>","protected":false},"featured_media":469187,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478465","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Polynomial Regression<\/mark>","faq_items":[{"question":"What is Polynomial Regression?","answer":"<p>Polynomial Regression is a statistical technique that models the relationship between an independent variable <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>X<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">X<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.07847em;\">X<\/span><\/span><\/span><\/span><\/span> and a dependent variable <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>y<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">y<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><\/span><\/span><\/span><\/span> as an nth degree polynomial. Unlike linear regression, it fits a curve to the data points, allowing for the modeling of nonlinear relationships.<\/p>"},{"question":"What is the history of Polynomial Regression?","answer":"<p>Polynomial Regression has its roots in polynomial interpolation, which dates back to the mathematical works of Isaac Newton and Carl Friedrich Gauss. It started to gain traction in the 20th century with advancements in computational tools.<\/p>"},{"question":"How does Polynomial Regression work?","answer":"<p>Polynomial Regression works by finding the coefficients that minimize the sum of the squared differences between the observed values and the values predicted by the polynomial model. This is done through the method of least squares, and the process includes choosing the degree of the polynomial, transforming the data, fitting the model, and evaluating its fit.<\/p>"},{"question":"What are the key features of Polynomial Regression?","answer":"<p>Key features of Polynomial Regression include its flexibility in modeling nonlinear relationships, its extension of linear regression techniques, a potential risk of overfitting with higher-degree polynomials, and the challenge of interpretation compared to simpler models.<\/p>"},{"question":"What types of Polynomial Regression exist?","answer":"<p>Polynomial Regression can be categorized based on the degree of the polynomial, with common examples being linear (1st degree), quadratic (2nd degree), cubic (3rd degree), and nth degree polynomial curves.<\/p>"},{"question":"How can Polynomial Regression be used, and what problems may arise?","answer":"<p>Polynomial Regression is used in various fields like economics, environmental sciences, and engineering. Common problems include overfitting, which can be addressed by using cross-validation and regularization, and multicollinearity, which can be resolved through scaling or transformation.<\/p>"},{"question":"How does Polynomial Regression compare to Linear and Nonlinear Regression?","answer":"<p>Polynomial Regression is nonlinear and offers high flexibility, unlike linear regression. It has moderate computational complexity compared to the low complexity of linear regression and the potentially high complexity of other nonlinear regression methods.<\/p>"},{"question":"What are the future perspectives and technologies related to Polynomial Regression?","answer":"<p>Future advancements in machine learning and artificial intelligence are likely to enhance Polynomial Regression, with techniques like regularization, ensemble methods, and automated hyperparameter tuning becoming more prevalent.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with Polynomial Regression?","answer":"<p>Proxy servers, such as those provided by OneProxy, can be used with Polynomial Regression in data gathering and analysis. They allow secure and anonymous access to data, facilitating the collection of information for modeling and ensuring unbiased results while adhering to privacy regulations.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/wiki\/478465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/wiki\/478465\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/media\/469187"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/media?parent=478465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}