BS141-4-AP-CO:
Quantitative methods for Life Sciences
PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
2020/21
Life Sciences (School of)
Colchester Campus
Autumn & Spring
Undergraduate: Level 4
Inactive
Thursday 08 October 2020
Friday 26 March 2021
15
05 December 2019
Requisites for this module
(none)
(none)
(none)
(none)
(none)
The aim of this module is to provide all 1st year Bioscience students with the necessary quantitative skills to enhance performance in other first year modules and to prepare you for your second and third year. Students will develop their skills in areas including experimental design, data handling, display and interpretation, basic statistical analysis, and computational data analysis. Teaching and learning will be through a mixture of lectures and practicals.
Learning Outcomes:
To pass this module students will need to be able to:
1. demonstrate an understanding of the scientific method, experimental design and investigation in the biosciences;
2. use scientific units and simple algebra and demonstrate understanding of logarithms, exponentials, geometry and elementary calculus;
3. use basic IT systems effectively for data handling and presentation;
4. be able to analyse data from experiments and draw sound conclusions about the underlying processes using their understanding of mathematics and statistics;
5. be able to analyse biological data using the R programming language.
By the end of the module you are expected to be able to:
1. define the straight line joining two points, or passing through one point with a known slope, in the form y = mx +c and determine the value of the slope m;
2. understand the relationship between logarithms and indices
3. define logarithms and solve equations with indices (e.g. 3z = 9) using logarithms;
4. understand the use exponential, power law and growth models in biological applications;
5. use simple calculus and calculate slopes of lines (differentiation) and areas under lines (integration);
6. use differentiation to locate stationary points, maximum and minimum values of a function;
7. determine the differential of exponential functions;
8. be able to differentiate composite functions using the chain rule;
9. be able to differentiate products and quotients;
10. understand how to apply differentiation to connected rates of change;
11. be able to apply integration and differentiation to biological examples.
12. formulate a simple statement involving a rate of change as a differential equation, including the introduction if necessary of a constant of proportionality
13. Perform simple scientific mathematical calculations relating to molarity, concentrations and dilutions.
14. list the different types of variable, explain the distinction between them, and, for a given variable, say what type it is;
15. produce frequency distributions from raw data, and to decide whether a tabular or graphical presentation is appropriate;
16. present data correctly in tabular form;
17. describe the different forms of graphical presentation, decide which one is appropriate for a given purpose and construct and present correctly different types of graphs;
18. define, calculate, report and know when to use simple descriptive statistics (i.e. mean, median, mode, range, standard deviation and variance);
19. explain why measurements of biological material are variable and explain the consequences it has for biological investigations;
20. define the terms sampling unit, sample, population, statistic and parameter and explain the relation between them;
21. explain the need for other essential elements in the design of a sampling programme or experiment, replication, independence and the avoidance of pseudoreplication and bias;
22. describe the main properties of normal distributions and explain their biological relevance;
23. explain why a parameter can only be estimated and what is meant by the reliability of the estimate of a parameter;
24. calculate and report, in the correct way, a standard error for a sample mean and explain how this interval estimate quantifies reliability;
25. define and explain the meaning of the following terms; parametric and non-parametric data, null hypothesis, alternative hypothesis, test statistic, sampling distribution, significance level, rejection region, critical value, decision rule;
26. use these terms to explain the general principle of an hypothesis test;
27. explain the meaning of one-tailed and two-tailed hypothesis tests and the limitations of their use;
28. interpret and use tables of critical values;
29. use relevant software to analyse and describe simple data sets with tables and graphs;
30. use relevant software to carry out the correct and appropriate statistical tests and correctly interpret the output;
31. present the results of statistical procedures clearly in a form suitable for a scientific paper.
32. be able to read and write files with biological data in a computer using R
33. use basic functions to analyse and produce graphs
1. define the straight line joining two points, or passing through one point with a known slope, in the form y = mx +c and determine the value of the slope m;
2. understand the relationship between logarithms and indices
3. define logarithms and solve equations with indices (e.g. 3z = 9) using logarithms;
4. understand the use exponential, power law and growth models in biological applications;
5. use simple calculus and calculate slopes of lines (differentiation) and areas under lines (integration);
6. use differentiation to locate stationary points, maximum and minimum values of a function;
7. determine the differential of exponential functions;
8. be able to differentiate composite functions using the chain rule;
9. be able to differentiate products and quotients;
10. understand how to apply differentiation to connected rates of change;
11. be able to apply integration and differentiation to biological examples.
12. formulate a simple statement involving a rate of change as a differential equation, including the introduction if necessary of a constant of proportionality
13. Perform simple scientific mathematical calculations relating to molarity, concentrations and dilutions.
14. list the different types of variable, explain the distinction between them, and, for a given variable, say what type it is;
15. produce frequency distributions from raw data, and to decide whether a tabular or graphical presentation is appropriate;
16. present data correctly in tabular form;
17. describe the different forms of graphical presentation, decide which one is appropriate for a given purpose and construct and present correctly different types of graphs;
18. define, calculate, report and know when to use simple descriptive statistics (i.e. mean, median, mode, range, standard deviation and variance);
19. explain why measurements of biological material are variable and explain the consequences it has for biological investigations;
20. define the terms sampling unit, sample, population, statistic and parameter and explain the relation between them;
21. explain the need for other essential elements in the design of a sampling programme or experiment, replication, independence and the avoidance of pseudoreplication and bias;
22. describe the main properties of normal distributions and explain their biological relevance;
23. explain why a parameter can only be estimated and what is meant by the reliability of the estimate of a parameter;
24. calculate and report, in the correct way, a standard error for a sample mean and explain how this interval estimate quantifies reliability;
25. define and explain the meaning of the following terms; parametric and non-parametric data, null hypothesis, alternative hypothesis, test statistic, sampling distribution, significance level, rejection region, critical value, decision rule;
26. use these terms to explain the general principle of an hypothesis test;
27. explain the meaning of one-tailed and two-tailed hypothesis tests and the limitations of their use;
28. interpret and use tables of critical values;
29. use relevant software to analyse and describe simple data sets with tables and graphs;
30. use relevant software to carry out the correct and appropriate statistical tests and correctly interpret the output;
31. present the results of statistical procedures clearly in a form suitable for a scientific paper.
32. be able to read and write files with biological data in a computer using R
33. use basic functions to analyse and produce graphs
One MCQ (Multiple Choice Questions) exam in wk 15 contributing 50% to the overall mark.
Coursework(contribution to the overall mark):
1 Maths on-line assessments: 12.5%
1 Statistics worksheet: 12.5%
Data analysis: 25%
Mixture of lectures, classes and tutorials.
This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Maths |
|
|
| Coursework |
Statistics |
|
|
| Coursework |
Data Analysis |
|
|
| Exam |
MCQ exam: 50 minutes during January
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Antonio Marco, email: amarco@essex.ac.uk.
Dr Nicolae Zabet, email: nzabet@essex.ac.uk.
Excluding tutors, Dr Radu Zabet, Dr Toni Marco, Prof Leo Schalkwyk, Dr Eoin O'Gorman, Dr Emmanuele Conte
School Undergraduate Office, email: bsugoffice (Non essex users should add @essex.ac.uk to create the full email address)
No
No
No
Prof Jacqueline McCormack
Institute Technology Sligo
Vice President
Available via Moodle
Of 30 hours, 24 (80%) hours available to students:
6 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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