ULMS Electronic Module Catalogue |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Statistics & Data Analysis for Economics and Business | ||
Code | ECON112 | ||
Coordinator |
Dr A Chopra Economics Anand.Chopra@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2024-25 | Level 4 FHEQ | Second Semester | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
ECON111 MATHEMATICS FOR ECONOMICS AND BUSINESS |
Modules for which this module is a pre-requisite: |
Programme(s) (including Year of Study) to which this module is available on a required basis: |
Programme(s) (including Year of Study) to which this module is available on an optional basis: |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
5 |
24 12 |
41 | ||||
Timetable (if known) | |||||||
Private Study | 109 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Assessment 2: Unseen Examination Assessment Type: Examination Duration: 2 hours Weighting: 80% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonym | 2 | 80 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Assessment 1: 5 pieces of coursework associated with tutorials. Assessment Type: Coursework Size: 5 assessments taking approximately 1-2hrs each Weighting: 20% Reassessment Opportunity: Yes | 0 | 20 |
Aims |
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The fundamental aim of this module is to give students an understanding of how statistics operates in Business and Economics; To provide both a foundation for further study and a broadly based introduction to statistics; To enable students to summarize, present and analyze data from a sample; To enable students to understand and apply the practice of statistical inference to sample data to estimate full population variable parameters; To enable students to work comfortably with variables as probability distributions, introducing some common and practicably useful probability distributions. |
Learning Outcomes |
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(LO1) Students will be able to explain the fundamental notion of statistical inference and effectively communicate results of statistical analysis |
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(LO2) Students will be able to summarise, describe and present raw data |
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(LO3) Students will be able to estimate key summary statistics (e.g. mean, median, standard deviation, variance, co-variance, inter-quartile range) |
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(LO4) Students will be able to formulate and test hypotheses about values in the population based on random samples |
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(LO5) Students will be able to carry out basic statistical computations and graphical analysis |
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(LO6) Students will be able to identify and model relationships between two variables |
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(LO7) Students will be able to apply probability models in data and statistics problems |
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(S1) Adaptability |
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(S2) Problem solving skills |
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(S3) Numeracy |
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(S4) Commercial awareness |
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(S5) Confidence |
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(S6) Communication skills |
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(S7) International awareness |
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(S8) Ethical awareness |
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(S9) Digital confidence |
Teaching and Learning Strategies |
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Teaching Method: Large Group Teaching Teaching Method – Tutorial and Assessment Feedback Self Directed Learning Hours - 109 There are the following pre-requisites: This module is a pre-requisite for the following modules: Skills and Attribute Mapping Skill/Other Attribute 1: "Ethical awareness" Skill/Other Attribute 2: "International awareness" Skill/Other Attribute 3: "Communication skills" Skill/Other At
tribute 4: "Confidence" Skill/Other Attribute 5: "Commercial awareness" Skill/Other Attribute 6: "Numeracy" Skill/Other Attribut
e 7: "Problem solving skills" Skill/Other Attribute 8: "Adaptability" Skill/Other Attribute 9: "Digital Confidence" |
Syllabus |
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The module will comprise the following elements which include application of techniques to data sets and practical problems: An Introduction to the Course including - Displaying distributions with graphs; Describing distributions with numbers; Density curves and Normal distributions as well as visual representations of Time series data and Scatterplots. The relationship between 2 variables including Correlation & Least Squares Regression (and cautions in application); Data analysis for 2-way tables and the question of causation. Sampling designs and design of experiments toward statistical inference. The Study of Randomness & probability models including probability and sampling distributions; General Probability Rules and conditional probability. Applications of Binomial & Poisson distributions. Estimating with Confidence via Tests of Significance (including use (and abuse) of significance tests). Inference for decision making i ncluding - Inference for the mean of a population; Comparing 2 means; Inference for a proportion of a population; Inference for Regression parameters. ANOVA (Analysis of Variance) and multi-variate regression. Analysis of Two-Way Tables including - Tests for independence; Comparing several populations and testing Goodness of Fit. |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |