Group project

2 - Written assignment (including essay) - 25

Exam

1 - Written exam - 75

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<ul><li>Data Visualisation: tables of frequency, cross-tabulation, frequency distributions. Bar charts, histograms, pie charts, line plots, scatter plots. </li><li>Descriptive Statistics: measures of location and of variability. Assessing the shape of distributions and identifying outliers. Measures of association between two variables. </li><li>Introduction to the statistical software Stata for business data analytics. </li><li>Elements of Probability: random experiments, outcomes, events, rules of probability. Conditional probability. </li><li>Random Variables: discrete and continuous random variables. Probability Distributions. </li><li>Introduction to Statistical Inference: sampling and sampling distributions. Point and interval estimation. Elements of hypothesis testing. </li><li>Linear Regression: simple and multiple linear regression, least squares method, , assessing the fit, testing hypotheses in the linear regression model, residuals analysis, prediction with regression. </li></ul>

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Self-directed - 

In-Person - 

Directed Asynchronous - 

Statistics for business and economics

Business Analytics

Introductory Econometrics for Finance

SMM466 - Analytics for Accounting and Business

SMM466-PRD1-PER-ALV1

This module will be taught primarily through lectures and lab sessions where you will learn how to use the software Stata to conduct a variety of practical data analysis exercises. 
You will also be expected to spend a significant number of hours in independent study in order to fully develop your understanding of the material. 
Self-directed learning hours are for independent study and account for the minimum amount of time you should spend studying independently.  For example, carrying out subject research, practising and reflecting on techniques learned in class, reading widely around the subject to develop a deeper understanding, carrying out activities using the VLE, attending induction and career development workshops, working in groups to complete assignments and presentations, and completing individual assignments and other homework.

Contact hours are designed to be flexible and are delivered using a variety of methods to help you learn in the most effective way. A proportion of the core content may be delivered online, and additional support materials and content will also be made available through Moodle.

Analytics for Accounting and Business

Assess the main predictive analytics tools. 

Knowledge

Interpret the meaning and relevance of probability in the analysis of business problems. 

Exemplify the statistical methods available for describing and summarising business data. 

Reflect on the importance of ethical data handling, including privacy concerns, transparency, and responsible use of analytics. 

Values and attitudes

Use the statistical software Stata to carry out data analysis and modelling tasks. 

Skills

Build, estimate and assess regression models for understanding the relationships between variables. 

Appreciate how rigorous and in-depth statistical data analysis is fundamental in any decision-making process.  

Summarise effectively, with relevant tables and graphs, various types of business data. 

Appreciate the importance of providing empirical evidence when commenting on economic and financial events. 

Compute probabilities using discrete and continuous distributions. Test hypotheses and draw conclusions. 

Use the main concepts and tools of inferential statistics that can be used in business decision making.  