MATH08005 2019 Statistics
Full Title
Statistics
Transcript Title
Statistics
Code
MATH08005
Attendance
N/A %
Subject Area
MATH - 0541 Mathematics
Department
MENG - Mech. and Electronic Eng.
Level
08 - Level 8
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Author(s)
Paul Curran
Programme Membership
SG_EADVA_E08 201900 Certificate in Advanced Lean Sigma Quality
SG_EPOLY_K08 201900 Bachelor of Engineering (Honours) in Polymer Processing
SG_EQLTY_K08 201900 Bachelor of Science (Honours) in Engineering in Quality Management and Technology
SG_EPOLP_K08 202200 Bachelor of Engineering (Honours) in Polymer Process Engineering
SG_EPOLY_K08 202200 Bachelor of Engineering (Honours) in Polymer Process Engineering
SG_EPOLZ_K08 202400 Bachelor of Engineering (Honours) in Polymer Process Engineering
SG_EPOLA_K08 202500 Bachelor of Engineering (Honours) in Polymer Process Engineering
Description
The student should be able to have a foundation in statistical analysis and be able to manipulate statistical data.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Summarise, describe data and identify the position of a data value in a dataset.
2.
Determine the probability of an event using probability rules.
3.
Calculate the probability for outcomes using discrete and continuous distributions.
4.
Calculate confidence intervals for population mean, proportion and variance.
5.
Perform hypothesis testing using parametric and nonparametric tests.
6.
Make decisions and draw conclusions on the basis of statistical analysis.
Teaching and Learning Strategies
The teaching strategy will be through the use of on-line lectures and tutorials with assessments timed to co-incide with the end of delivery of a particular topic so as to re-enforce and embed the knowledge of the material with the student.
Module Assessment Strategies
Assessment will be performed throughout the academic period through assignments and mini-examinations. There will be a final examination at the end of the academic period.
Repeat Assessments
Repeat assessment will be by way of sitting another examination on the subject. Alternatively, at the discretion of the lecturer, assignments covering the deficient areas of the course may be set.
Indicative Syllabus
- Descriptive Statistics: Measures of Central Tendency and Measures of Dispersion, Sampling. Use of corresponding statistical functions in Excel/statistical software.
- Laws of Probability: Addition Rule, Multiplication Rule, Conditional Probability, Bayes's Theorem
- Discrete Probability Distributions: Binomial, Poisson, Geometric and Hypergeometric distributions. Use of statistical software to calculate probability distributions.
- Continuous Probability Distributions: Uniform, Normal and Exponential distributions. Normal distribution as approximation to Binomial. Central Limit Theorem. Use of statistical software to calculate probability distributions.
- Estimation: Point estimation and confidence intervals. Confidence intervals for population mean. T Distribution. Confidence Intervals for a population proportion. Determining sample size. Confidence Intervals for a population variance. Distribution. Use of statistical software to develop confidence intervals.
- Hypothesis Testing: Fundamentals, Testing a claim about a mean, proportion, standard deviation or variance. P-values. Use statistical software to perform hypothesis testing.
- Inference from Two Samples: Inference about two means, independent samples and matched pairs. Inference about two proportions. Comparing variation in two samples. F Distribution. Use of statistical software to perform corresponding test.
- Chi Squared Tests: Goodness of Fit Testing, Contingency Tables
- Nonparametric Statistics: Sign test, Wilcoxon test.
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
20 %
End of Semester / Year Formal Exam
80 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Assignment | Coursework Assessment | UNKNOWN | 20 % | OnGoing | 1,2,3,4,5,6 |
End of Semester / Year Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Final Exam | Final Exam | UNKNOWN | 80 % | End of Term | 1,2,3,4,5,6 |
Full Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Not Specified | Lecture | 2 | Weekly | 2.00 |
| Tutorial | Not Specified | Tutorial | 2 | Weekly | 2.00 |
Total Full Time Average Weekly Learner Contact Time 4.00 Hours
Part Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Not Specified | Lecture | 2 | Weekly | 2.00 |
| Tutorial | Not Specified | Tutorial | 2 | Weekly | 2.00 |
Total Part Time Average Weekly Learner Contact Time 4.00 Hours
Required & Recommended Book List
Required Reading
Elementary Statistics: A Step by Step Approach McGraw Hill
Elementary Statistics: A Step by Step Approach McGraw Hill
Recommended Reading
Probability and Statistics for Engineers and Scientists Pearson
Probability and Statistics for Engineers and Scientists Pearson
Recommended Reading
2014-02 Statistics for Engineers and Scientists
ISBN 1259251608 ISBN-13 9781259251603
2014-02 Statistics for Engineers and Scientists
ISBN 1259251608 ISBN-13 9781259251603
This title stands out for its crystal clear presentation of applied statistics. Suitable for a one or two semester course, the book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work.
Module Resources
Non ISBN Literary Resources
Books already indicated
Journal Resources
N/A
URL Resources
N/A
Other Resources
None
Additional Information
None