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# Quantitative Analysis in Management - MAN00035C

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• Department: The York Management School
• Module co-ordinator: Dr. Sule Sahin
• Credit value: 20 credits
• Credit level: C
• Academic year of delivery: 2024-25
• See module specification for other years: 2023-24

## Module will run

Occurrence Teaching period
A Semester 2 2024-25

## Module aims

The aim of this module is to introduce students to a variety of quantitative tools and statistical methods, including their advantages and disadvantages, with particular emphasis on their application in accounting, business and management. The module will provide students with a solid foundation for collecting, handling and analysing data, formulating and testing hypotheses and forecasting future values. Students should expect some degree of mathematical detail for sufficient understanding of the statistical theories, however, the module focuses on providing a softer approach than a traditional statistics course through a variety of real-world examples, identification and contextual interpretation of results, as well as practical applications in Excel.

## Module learning outcomes

After successful completion, the student should be able to:

Subject Content

• Describe various types of data

• Explain the concept of probability and distributions

• Describe a variety of sampling techniques and calculate basic sample statistics

• State and test basic hypotheses

• Conduct an ANOVA analysis

• Apply simple linear regression and time-series forecasting techniques using Excel

• Explain the concept of ‘Big Data’

• Accurately collect, handle and analyse data

• Apply various quantitative methods in a logical, rigorous, and concise way;

• Apply strict logical reasoning from assumptions to conclusion;

• Critically assess assumptions necessary to draw certain conclusions.

## Module content

1. Data, Descriptive Statistics and Charts

• Types of data (Nominal, ordinal, numerical, grouped/ungrouped)

• Central tendencies (Mean, median and mode)

• Measures of spread (Variance and standard deviation, range)

• Graphical presentation of data

2. Probability

• Basic definition

• Conditional probability and independence

• Bayes’ Law

3. Distributions

• Random variables, theoretical distributions and their properties (expectation, variance and skewness)

• Sampling/Empirical distributions

• Normal distribution

4. Data Sampling

• Methods of sampling

• Sample statistics and estimation

• Central Limit Theorem

• Confidence Intervals

5. Hypothesis Testing

• Null and alternative hypotheses

• Type I and II errors

• Testing for single means, two means and for paired data.

1. Chi-Squared Tests

• Two types of Chi-squared test (Goodness of fit and independence)

• Calculation and interpretation of Chi-squared tests

2. ANOVA

• Hypothesis

• Variance by components

• ANOVA table and analysis

3. Correlation and Regression

• Causality vs. correlation

• Pearson’s correlation coefficient

• Simple Linear Regression (Equation, coefficient of determination, inference)

• Application using Excel

• Forecasting

4. Time-Series Forecasting

• Time-series data

• Decomposition model (trend, seasonality, cyclicality, randomness)

• Application using Excel

• Forecasting

5. Big Data

• What is Big Data?

• Why is Big Data important?

• How do organisations use Big Data?

## Assessment

Task Length % of module mark
Essay/coursework
Essay : Report/coursework
N/A 70
Open Examination: Multiple choice questions online
Open exam : Mid semester exam
1.5 hours 30

None

None

## Module feedback

Feedback on formative online quizzes will be instantaneous through the VLE system.

Feedback on summative mid-semester exam and report/coursework will be inline with university policy.