Quantitative Analysis for Decision Makers

Synopsis

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There's no doubt that a manager's job is getting tougher. Do it better, do it faster, do it cheaper are the pressures every manager faces. And at the heart of every manager's job is decision-making: deciding what to do and how to do it. This well-respected text looks at how quantitative analysis techniques can be used effectively to support such decision making.

 

As a manager, developing a good understanding of the quantitative analysis techniques at your disposal is crucial. Knowing how, and when, to use them and what their results really mean can be the difference between making a good or bad decision and, ultimately, between business success and failure.

 

Appealing both to students on introductory-level courses and to MBA and postgraduate students, this internationally successful text provides an accessible introduction to a subject area that students often find difficult. Quantitative Analysis for Decision Makers (formerly known as Quantitative Methods for Decision Makers) helps students to understand the relevance of quantitative methods of analysis to management decision-making by relating techniques directly to real-life business decisions in public and private sector organisations and focuses on developing appropriate skills and understanding of how the techniques fit into the wider management process.

 

Key features:

• The use of real data sets to show how analytical techniques are used in practice
• "QADM in Action" case studies illustrating how organisations benefit from the use of analytical techniques
• Articles from the Financial Times illustrating the use of such techniques in a variety of business settings
• Fully worked examples and exercises supported by Excel data sets
• Student Progress Check activities in each chapter with solutions
• A 300+ page Tutors Solutions Manual

 

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Summary

Chapter 1: Introduction to Quantitative Analysis

* Summary:
* Defines quantitative analysis and its applications in decision-making.
* Explains the importance of data analysis and visualization.
* Introduces basic statistical concepts, such as mean, median, and standard deviation.

* Real Example:
* A company wants to analyze its sales data to identify trends and make better forecasting decisions. They use quantitative analysis techniques to calculate the average sales over time, variance in sales, and correlations between sales and external factors.

Chapter 2: Data Collection and Preparation

* Summary:
* Discusses different methods of data collection, such as surveys, experiments, and secondary data sources.
* Explains how to clean, transform, and prepare data for analysis.
* Introduces data management techniques, such as data warehousing and data mining.

* Real Example:
* A market research firm wants to conduct a survey to gather customer feedback. They design a questionnaire, collect responses through online and offline channels, and use data cleaning tools to remove incomplete and inconsistent data.

Chapter 3: Descriptive Statistics

* Summary:
* Describes measures of central tendency (mean, median, mode) and measures of dispersion (standard deviation, variance).
* Explains how to use frequency distributions, histograms, and box plots to visualize data.
* Introduces basic probability concepts, such as events, sample spaces, and probability distributions.

* Real Example:
* A manufacturing company wants to analyze the distribution of product weights. They collect data and construct a histogram to visualize the spread and central tendency of the weights.

Chapter 4: Inferential Statistics

* Summary:
* Explains the principles of inductive reasoning and hypothesis testing.
* Introduces statistical tests, such as t-tests, z-tests, and chi-square tests.
* Discusses the concepts of confidence intervals, p-values, and statistical significance.

* Real Example:
* A medical researcher wants to test whether a new drug is effective in reducing symptoms. They conduct a clinical trial and use statistical tests to determine if the observed difference between the treatment and control groups is statistically significant.

Chapter 5: Regression Analysis

* Summary:
* Introduces the concepts of linear and multiple regression.
* Explains how to estimate regression parameters, interpret coefficients, and assess model fit.
* Discusses the assumptions of regression analysis and techniques for model validation.

* Real Example:
* A financial analyst wants to predict stock prices based on historical data. They build a regression model that relates stock prices to factors such as earnings, interest rates, and economic growth.

Chapter 6: Forecasting

* Summary:
* Explains different forecasting methods, such as exponential smoothing, time series decomposition, and regression.
* Discusses the importance of forecast accuracy and evaluation.
* Introduces techniques for dealing with uncertainty and improving forecast quality.

* Real Example:
* A retail chain wants to forecast monthly sales in order to plan inventory and staffing. They use exponential smoothing models to predict future sales based on historical data.

Chapter 7: Advanced Topics

* Summary:
* Introduces more advanced topics, such as non-parametric statistics, multivariate analysis, and decision theory.
* Explains how to apply these techniques to solve complex decision-making problems.
* Discusses the role of ethics and communication in quantitative analysis.

* Real Example:
* A non-profit organization wants to identify the most effective programs for reducing poverty. They use multivariate analysis to identify factors that contribute to program effectiveness and develop targeted interventions.