This textbook is aimed at students of management who need to have an appreciation of the role of statistics in management decision making.

The statistical treatment of business data is relevant in all areas of business activity and across all management functions (i.e. Marketing, finance, human resources, operations and logistics, accounting, information systems and technology). Statistics provide evidence-based information which makes it an important decision support tool in management.

The text emphasises four themes to support evidence-based management decision making: 1. Setting the statistical landscape in a management context 2. Interpretative decision making based on patterns revealed by exploratory data analyses 3. Statistical decision making guided by the test-based findings of inferential analyses 4. Predictive decision making using statistical modelling evidence. The thread that links them is the role of data analytics as a management decision-support tool. This fifth edition builds on the strengths of the fourth edition by: - Highlighting newer trends in statistical applications in management practice - Strengthening the Excel-based generation of statistical evidence using a custom-built software product, called X-Static - Enhancing the graphic visualisation of statistical evidence. Target market: - Undergraduate students of Management at universities (BCom, BAcc, etc) - Management diploma students working towards professional qualifications at institutions such as the IMM, CSSA, CIMA, IAC, etc. - Postgraduate students (MBA and PGDip) of Management at business schools.

Basic Mathematics is aimed primarily at management students preparing to write the GMAT test which requires a strong foundation in fundamentals of mathematics. It is also of value to anyone wanting a general revision of basic mathematics. The text focuses only on those areas of mathematics required for the GMAT test, consisting of four main topics: basic arithmetic; fundamental algebra; geometry and introductory statistics. After a brief review of each topic's basic rules and methods, there is at least one worked example followed by an extensive set of self-practice exercises. The student should attempt as many exercises as is necessary to master the topic.

This text aims to differentiate itself from other business statistics texts in two important ways. It seeks: to present the material in a non-technical manner to make it easier for a student with limited mathematical background to grasp the subject matter; and to develop an intuitive understanding of the techniques by framing them in the context of a management question, giving layman-type explanations of methods, using illustrative business examples and focusing on the management interpretations of the statistical findings. This further edition continues the theme of using Excel as a computational tool to perform statistical analysis. While all statistical functions have been adjusted to the Excel (2013) format, the statistical output remains unchanged. Using Excel to perform the statistical analysis in this text allows a student: To examine more realistic business problems with larger datasets; To focus more on the statistical interpretation of the statistical findings; and to transfer this skill of performing statistical analysis more easily to the work environment. Its overall purpose is to develop a management student's statistical reasoning and statistical decision-making skills to give him or her a competitive advantage in the workplace.

Applied business statistics: methods and excel-based applications is an introductory statistics text for management students who want to know how to use statistics to analyse data generated from any area of business - be it marketing, finance, human resources, production or logistics. This text highlights the role of statistics as a decision support tool in management decision making. Throughout, emphasis is placed on (i) the importance of data and data quality, (ii) the use of numerous worked examples to show the range of possible applications, (iii) clear explanations of how each technique works, and (iv) the valid management interpretation of the statistical findings.