BS141-4-AP-CO:
Quantitative methods for Life Sciences

The details
2019/20
Life Sciences (School of)
Colchester Campus
Autumn & Spring
Undergraduate: Level 4
Current
Thursday 03 October 2019
Friday 20 March 2020
15
05 December 2019

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

BSC C700 Biochemistry,
BSC C701 Biochemistry (Including Placement Year),
BSC C703 Biochemistry (Including Year Abroad),
BSC CR00 Biochemistry (Including Foundation Year),
BSC C100 Biological Sciences,
BSC C101 Biological Sciences (Including Year Abroad),
BSC C102 Biological Sciences (Including Placement Year),
BSC CD00 Biological Sciences (Including Foundation Year),
BSC B990 Biomedical Science,
BSC B991 Applied Biomedical Science (NHS placement),
BSC B995 Biomedical Science (Including Year Abroad),
BSC B999 Biomedical Science (Including Placement Year),
BSC BD00 Biomedical Science (Including Foundation Year),
BSC C400 Genetics,
BSC C402 Genetics (Including Year Abroad),
BSC C403 Genetics (Including Placement Year),
BSC CK00 Genetics (Including Foundation Year),
BSC C161 Marine Biology (Including Foundation Year),
BSC C164 Marine Biology,
BSC CC60 Marine Biology (Including Year Abroad),
BSC CC64 Marine Biology (Including Placement Year),
MMB C160 Marine Biology,
BSC C200 Human Biology,
BSC C201 Human Biology (Including Year Abroad),
BSC C202 Human Biology (Including Placement Year)

Module description

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.

Module aims

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

Module learning outcomes

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

Module information

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%

Learning and teaching methods

Mixture of lectures, classes and tutorials.

Bibliography

This module does not appear to have a published bibliography.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Maths    25% 
Coursework   Statistics    25% 
Coursework   Data Analysis    50% 
Exam  50 minutes during January (Multiple Choice) 

Overall assessment

Coursework Exam
50% 50%

Reassessment

Coursework Exam
50% 50%
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)

 

Availability
No
No
No

External examiner

Dr Clive Butler
The University of Exeter
Associate Professor of Microbial Biochemistry
Resources
Available via Moodle
Of 30 hours, 21 (70%) hours available to students:
9 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Life Sciences (School of)

Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.