{"id":11364,"date":"2026-05-27T22:57:31","date_gmt":"2026-05-27T12:57:31","guid":{"rendered":"https:\/\/casestudyhelp.com\/sample-questions\/?p=11364"},"modified":"2026-05-27T22:57:31","modified_gmt":"2026-05-27T12:57:31","slug":"sem310ds-logistic-regression-data-science-assessment-solution","status":"publish","type":"post","link":"https:\/\/casestudyhelp.com\/sample-questions\/sem310ds-logistic-regression-data-science-assessment-solution\/","title":{"rendered":"SEM310DS Logistic Regression Data Science Assessment Solution"},"content":{"rendered":"

Need a SEM310DS Logistic Regression Data Science Assessment Solution? Contact Casestudyhelp.com for expert assignment help<\/strong><\/a> at the university level. They cover a wide range of assignments, including MATLAB Programming<\/strong><\/a>, Programming assignment<\/strong><\/a>, CS 367 Java Programming<\/strong><\/a>, etc., with 100% AI-free and plagiarism-free content.<\/em><\/p><\/blockquote>\n

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ASSESSMENT TASKS<\/strong><\/h2>\n

Task 1 FORMATIVE TASK Logistic Regression in Principle<\/strong><\/h3>\n

FORMATIVE TASK<\/strong><\/span><\/p>\n

Instruction: Produce a briefing document for the intern in your department that explains logistic regression and its potential uses in your organisation. The report must contain the following:<\/p>\n

    \n
  • An outline of what is understood by logistic regression, and how it is different from linear<\/li>\n
  • Identify and explain the characteristics of the Logistic Function, the Odds Ratio and the Logit function<\/li>\n
  • Discuss the potential uses for logistic regression within your organisation, and a judgment as to its overall value to the organisation<\/li>\n<\/ul>\n

    Task 2 SUMMATIVE TASK Logistic Regression in your Organisation<\/strong><\/h3>\n

    SUMMATIVE TASK<\/strong><\/span><\/p>\n

    Instruction: Carry out an evaluation of a logistic regression undertaken within your organisation. Your report must contain the following:<\/p>\n

      \n
    • An outline of how Python was used to create the logistic regression, including calculating correctly the probability values of inputs belonging to classes using the Logistic function (LO 1, 3.1)<\/li>\n
    • Identify and explain how the Odds-ratio metrics were calculated accurately (LO 2,)<\/li>\n
    • A judgment as to the conclusions that can be drawn from the data, and the accuracy of these conclusions in making future decisions (LO 3, 3.2)<\/li>\n<\/ul>\n\n\n\n\n\n
      Learning Outcomes:<\/strong><\/p>\n

      To achieve this unit, the learner must be able to:<\/strong><\/td>\n

      		Assessment Criteria:<\/strong><\/p>\n

      Assessment of these learning outcomes will require a learner to demonstrate that they can:<\/strong><\/td>\n<\/tr>\n

      2. Be able to perform logistic regression calculations.<\/td>\n2.1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Calculate correctly the probability values of inputs belonging to classes using the Logistic function.<\/p>\n

      2.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Calculate correctly the Odds-ratio.<\/p>\n

      2.3\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Calculate correctly relevant classification evaluation metrics for logistic regression model outputs.<\/td>\n<\/tr>\n

      3.\u00a0\u00a0\u00a0\u00a0 Be able to create logistic regression models.<\/td>\n3.1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Use Python to build an accurate logistic regression model for datasets.<\/p>\n

      3.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Use Python to evaluate the accuracy of the model built in 3.1. and analyse the results.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n