{"id":4230,"date":"2026-01-16T10:26:11","date_gmt":"2026-01-16T10:26:11","guid":{"rendered":"https:\/\/devserver.admin.uoc.gr\/damsl\/?page_id=4230"},"modified":"2026-01-16T10:28:40","modified_gmt":"2026-01-16T10:28:40","slug":"damsl-104-applied-linear-regression","status":"publish","type":"page","link":"https:\/\/mscs.uoc.gr\/damsl\/damsl-104-applied-linear-regression\/","title":{"rendered":"DAMSL-104 Applied Linear Regression"},"content":{"rendered":"\t\t
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Type<\/strong><\/p>

Elective<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Course Code<\/strong><\/p>

DAMSL-104<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\tTeaching Semester<\/strong>
\n\nA semester<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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ECTS Credits<\/strong><\/p>

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Student Performance Evaluation<\/h6>

Homework\/Lab Assignments, Midterm, Final exam<\/p><\/div><\/div><\/div>

Prerequisite Courses<\/h6>

Calculus, Linear Algebra, Probabilities, Python Programming<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Syllabus<\/span><\/h6>
  • Introduction to R\u00a0<\/li>
  • Linear Regression with One Predictor\u00a0<\/li>
  • Inference in Regression and Correlation\u00a0<\/li>
  • Diagnostics and Remedial Measures\u00a0<\/li>
  • Simultaneous Inferences<\/li>
  • Matrix Approach to Simple Linear Regression<\/li>
  • Multiple Regression<\/li>
  • Models for Quantitative and Qualitative Predictors<\/li>
  • Collinearity<\/li>
  • Model Selection and Validation\u00a0<\/li>
  • Weighted Least Squares; Robust Regression<\/li>
  • Quantile Regression<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Learning Outcomes<\/span><\/h6>
    • Students will develop an in-depth understanding of the linear regression model.\u00a0<\/li>
    • Students will have learned alternative regression model-building strategies, depending on the number of available predictors.\u00a0<\/li>
    • Students will gain hands-on experience in classic (e.g. exploratory analyses, diagnostic checks) and modern topics (e.g. cross-validation, bootstrap, quantile regression, penalized estimation) by analyzing real and simulated datasets using R.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      <\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      Type Elective Course Code DAMSL-104 Teaching Semester A semester ECTS Credits 10 Student Performance Evaluation Homework\/Lab Assignments, Midterm, Final exam Prerequisite Courses Calculus, Linear Algebra, Probabilities, Python Programming Syllabus Introduction to R\u00a0 Linear Regression with One Predictor\u00a0 Inference in Regression and Correlation\u00a0 Diagnostics and Remedial Measures\u00a0 Simultaneous Inferences Matrix Approach to Simple Linear Regression Multiple Regression Models for Quantitative and Qualitative Predictors Collinearity Model Selection and Validation\u00a0 Weighted Least Squares; Robust Regression Quantile Regression Learning Outcomes Students will develop an in-depth understanding of the linear regression model.\u00a0 Students will have learned alternative regression model-building strategies, depending on the number of available predictors.\u00a0 Students will gain hands-on experience in classic (e.g. exploratory analyses, diagnostic checks) and modern topics (e.g. cross-validation, bootstrap, quantile regression, penalized estimation) by analyzing real and simulated datasets using R.<\/p>\n","protected":false},"author":194,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-4230","page","type-page","status-publish","hentry","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4230","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/comments?post=4230"}],"version-history":[{"count":4,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4230\/revisions"}],"predecessor-version":[{"id":4235,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4230\/revisions\/4235"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=4230"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}