< MATH235 : Statistics

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MATH235 : Statistics

Department:Mathematics and Statistics
Level:Part II (yr 2)
Learning Hours:200
Credit Points:20
Course Convenor:Dr MP Sperrin

Syllabus Rules

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Prior to MATH235, the student must have successfully completed:
The student must take the following modules:
The following modules may not be taken:

Assessment Rules

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  • 85% Exam
  • 15% Coursework

Curriculum Design: Outline Syllabus

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Hypothesis testing and Estimation

  • Estimates and Estimators
  • Paired and unpaired t-tests
  • Confidence Intervals
  • Least squares estimaton
  • Parameter testing and confidence intervals
  • Model comparison
  • Model checking
  • Model interpretation
Likelihood Theory
  • Maximum Lidelihood estimation
  • Distributions of maximum likelihood estimators; Fisher information
  • Confidence intervals of parameters
  • Information suppression and sufficiency

Curriculum Design: Pre-requisites/Co-requisites/Exclusions

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Prerequisites: MATH105 Statistics; MATH230 Probability

Educational Aims: Subject Specific: Knowledge, Understanding and Skills

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At the end of the module students should be able to:

This course aims for students:


-         to appreciate the importance of statistical methodology in making conclusions and decisions.

-         to recognize the role, and limitations, of the linear model for understanding, exploring and making inferences concerning the relationships between variables and making predictions.

-         to appreciate the central role of the likelihood function in statistical inference.

-         to appreciate the role of statistics in making sense of uncertainty.


Educational Aims: General: Knowledge, Understanding and Skills

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This course aims for students:


-  to gain skills in problem solving and critical thinking.

-  to appreciate the importance of communicating technical ideas at an appropriate level. 

-  to appreciate the importance of making evidence-based decisions.

Learning Outcomes: Subject Specific: Knowledge, Understanding and Skills

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 At the end of the course the students should be able to demonstrate subject specific knowledge, understanding and skills and have the ability to:


-         Apply appropriate statistical procedures to answer simple research questions, using appropriate data.

-         Explain the concept of sampling distribution

-         Write down likelihood functions for simple models and calculate maximum likelihood estimators for parameters

-         Fit linear regressions using the least squares method to appropriate data

-         Construct confidence intervals for estimators, perform hypothesis tests, and appreciate the similarities and differences between the two approaches.

-         Understand some of the asymptotic theory and properties of statistical inference methods

-         Critically evaluate whether modelling assumptions are appropriate.

-         Compare and contrast different models, and be able to make an informed choice about which is the most appropriate to answer a given question

-         Interpret the results and conclusions implied by fitted models in real world situations.

-         Use the statistical package ?R' to fit and evaluate models.

Learning Outcomes: General: Knowledge, Understanding and Skills

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  At the end of this course students should be able to:

 - Communicate technical ideas at an appropriate level. Critically evaluate approaches taken to solve problems.

- Make conclusions and decisions based on evidence, and relate these to real world problems.

Assessment: Details of Assessment

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Assessment will be through
(i) weekly coursework, aimed at testing and consolidating understanding of the basic elements of the course;
(ii)  an examination in the Summer which assesses more fully the students' understanding and summative knowledge of the topics.
Lancaster University
LancasterLA1 4YW United Kingdom
+44 (0) 1524 65201