Introduction to Statistical Inference

Nelson Mandela University

Course Description

  • Course Name

    Introduction to Statistical Inference

  • Host University

    Nelson Mandela University

  • Location

    Port Elizabeth, South Africa

  • Area of Study

    Statistics

  • Language Level

    Taught In English

  • Course Level Recommendations

    Lower

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • Host University Units

    15
  • Recommended U.S. Semester Credits
    1
  • Recommended U.S. Quarter Units
    1
  • Overview

    Syllabus

    PURPOSE
    To teach student concepts and principles related to the application of statistical inference to real world data analysis problems.

    LEARNING OUTCOMES

    • Demonstrate an understanding of the fundamentals of estimation theory, with particular reference to: methods of point estimation, properties of estimators, and interval estimation; and understand and be able to implement mainstream statistical theory and methods for estimating parameters of an unknown population.
    • Demonstrate an understanding of the fundamentals of Hypothesis Testing, with particular reference to: problems of hypothesis testing, testing simple hypotheses, statistical tests for means, variances and proportions, and statistical tests for comparing populations; and understand and be able to implement mainstream statistical theory and methods for testing hypotheses on parameters of an unknown population.
    • Be capable of analytic thought and also of bringing together concepts, theory and methods from different areas of learning in order to solve problems; apply the appropriate statistical methods and statistical software to analyse data and interpret the results.
    • Demonstrate the use of statistical principles and concepts, and the integration of knowledge from other disciplines, across real-world contexts.

    CORE CONTENT

    • Random samples.
    • Method of Moments Estimation.
    • Maximum Likelihood Estimation.
    • Least Squares Estimation.
    • Properties of Estimators: Consistent, Unbiased, Sufficient, Efficiency & Fisher Information.
    • Confidence Intervals.
    • Hypothesis testing concepts.
    • Testing of simple hypotheses: Neyman-Pearson Lemma.
    • UMP Tests.
    • Likelihood Ratio Test.
    • One and Two Sample tests for means, variances and proportions.
    • Chi-Square Goodness of Fit test.
    • Contingency Tables.