Business Math & Statistics

This module equips learners with the quantitative toolkit that recurs across sales, operations, marketing and finance teams. It covers the arithmetic of pricing, costs and margins, then layers in the descriptive and inferential statistics needed to summarise data, forecast simple trends and run basic experiments. Methods are taught through workplace cases such as pricing a SKU, planning a break-even target or interpreting an A/B test. By the end, a learner can set up the right calculation unprompted and explain the result to a non-technical manager.

Learning Outcomes

Calculate markup, markdown, gross margin and contribution margin for products and services.

Perform break-even analysis in units and revenue and apply margin of safety.

Compute mean, median, mode, variance and standard deviation and know when each measure is appropriate.

Build and interpret a simple linear forecast and seasonal index from historical sales data.

Design and interpret A/B tests using lift, confidence intervals and basic significance reasoning.

Apply probability and expected-value reasoning to pricing, promotions and inventory decisions.

Translate word problems into equations and inequalities for pricing, discounting and allocation.

Communicate statistical results to a non-technical audience without over-claiming precision.

Topics Covered

Percentages, markups and markdowns

Cost, revenue and profit relationships

Gross margin and contribution margin

Break-even analysis and margin of safety

Pricing strategy and elasticity basics

Descriptive statistics: mean, median, mode

Spread: range, variance, standard deviation

Distributions and the normal curve

Correlation and simple linear regression

Forecasting: moving averages and trend lines

Seasonality and indexing

Probability and expected value

Sampling and sample size basics

A/B testing, lift and confidence intervals

Hypothesis testing fundamentals

Skills Developed

Translating business language to equations Spreadsheet modelling of margin and break-even Summarising datasets with the right statistic Visual diagnostics of distributions and outliers Simple forecasting and model evaluation Experiment design and read-outs Probability reasoning for commercial decisions Clear written and verbal reporting of numbers

Suggested Resources

{'title': 'Naked Statistics', 'author': 'Charles Wheelan', 'source': 'W. W. Norton, 2013'}

{'title': 'Business Mathematics and Statistics', 'author': 'Andy Francis', 'source': 'Cengage Learning EMEA, 2004'}

{'title': 'How to Measure Anything', 'author': 'Douglas W. Hubbard', 'source': 'Wiley, 2014'}

{'title': 'Trustworthy Online Controlled Experiments', 'author': 'Kohavi, Tang and Xu', 'source': 'Cambridge University Press, 2020'}

{'title': 'OpenIntro Statistics', 'author': 'Diez, Cetinkaya-Rundel and Barr', 'source': 'openintro.org (4th edition, 2019)'}

{'title': 'Khan Academy: Statistics and Probability', 'author': 'Khan Academy', 'source': 'khanacademy.org/math/statistics-probability'}

{'title': 'HBR Guide to Data Analytics Basics for Managers', 'author': 'Harvard Business Review', 'source': 'Harvard Business Review Press, 2018'}

Assessment

Assessment balances calculation accuracy with the ability to pick the right technique for a messy business problem. Learners submit a mini-forecasting project and an A/B-test readout, both based on supplied datasets, in addition to graded quizzes and a final written exam.

weekly_mcq_quizzes17%

spreadsheet_margin_and_break_even_tasks17%

statistics_problem_sets17%

forecasting_mini_project17%

ab_test_readout_assignment17%

final_proctored_exam17%

Practice bank: 200 MCQs · Timed mock & self-paced modes

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