Introduction to Statistical Inference
Nelson Mandela University
Port Elizabeth, South Africa
Area of Study
Taught In English
Course Level Recommendations
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.
Host University Units15
Recommended U.S. Semester Credits1
Recommended U.S. Quarter Units1
Hours & Credits
To teach student concepts and principles related to the application of statistical inference to real world data analysis problems.
- 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.
- 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.