This module will be delivered via four lecture hours and one computer practical per week. Self-tests and tutorial sheets will be available through Blackboard to reinforce learning.
Module Aim:
This module will develop the learner's ability to analyse and understand data through the
use of inferential statistics and to develop the students' understanding of the quality control techniques used in industry.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1
Apply inferential statistics to conduct a variety of hypothesis tests on population parameters and explore the relationship between variables.
LO2
Formulate, solve and interpret scientific problems using differential and integral calculus.
LO3
Use and interpret statistical process control techniques.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
Module Content & Assessment
Indicative Content
Introduction to Hypothesis Testing
Introduction to inferential statistics. The Elements of a Test of Hypothesis. Formulating the null and alternative hypotheses. Setting Up the Rejection Region.
One sample problems for the population mean
Identifying and Estimating the Target Parameter. Confidence Interval for a Population Mean. Students t distribution. Test of Hypothesis about a Population Mean.
Measures and Tests of Association
Scatter diagrams. Pearson and Spearman correlation coefficients, correlation and causation. Independent and dependent variables. Simple Linear regression. The regression equation and prediction, the method of least squares.
Tests of association
Categorical Data and the Multinomial Experiment. Chi-squared test of association. Testing Categorical Probabilities: One-Way Table. Testing Categorical Probabilities: Two-Way (Contingency) Table
Tests for the population variance
Test of Hypothesis about a Population Variance. F test for equality of variances
Calculus
Review of basic calculus. Solve scientific problems using differential and integral calculus. Model scientific situations using elementary differential equations.
Sampling
Acceptance Sampling, Operating Characteristic (OC) curve. Acceptable Quality Level (AQL), Lot Tolerance Percent Defective (LTPD or RQL), producer’s risk and consumer’s risk. Average Outgoing Quality (AOQ) and Average Outgoing Quality Limit (AOQL).
Control Charts
Principles of Statistical Process Control (SPC). Control Charts for Variables: average and range charts, pre-control chart, cumulative sum control chart (CUSUM) and multi-vari charts. Control charts for Attributes: np, p, u and c charts. Interpretation and design of charts. Process Capability Analysis.
Reliability
Reliability calculations, failure rate, mean time to failure (MTTF), life-tests, design for reliability.
Assessment Breakdown
%
Continuous Assessment
70.00%
Practical
30.00%
Special Regulation
Students must achieve a minimum grade (35%) in both the CA and practical components of the course.
Continuous Assessment
Assessment Type
Assessment Description
Outcome addressed
% of total
Assessment Date
Examination
Quantitative Methods and Quality Control examinations and assessments
1,2
70.00
n/a
No Project
Practical
Assessment Type
Assessment Description
Outcome addressed
% of total
Assessment Date
Practical/Skills Evaluation
Computer practicals and assessments.
2,3
30.00
n/a
No End of Module Formal Examination
SETU Carlow Campus reserves the right to alter the nature and timings of assessment