This Solutions Manual provides solutions to odd-numbered text exercises along with summaries of the key concepts needed to solve the problems.

Part of the best-selling David Moore introductory statistics textbook family, The Practice of Statistics for Business and Economics uses a similar, accessible approach found in The Basic Practice of Statistics but applies to the world of business and economics. With The Practice of Statistics for Business and Economics, instructors can help students develop a working knowledge of data production and interpretation in a business and economics context, giving them the practical tools they need to make data-informed, real-world business and economic decisions from the first day of class. With its expanded, dedicated version of LaunchPad, the text now more than ever is a seamlessly integrated print/online resource, putting powerful statistical tools and interactive learning features in the hands of both students and teachers alike.

This version includes both the textbook and LaunchPad Access. Written by team of leading statisticians led by best-selling statistics textbook author David Moore, this textbook is an essential resource for students using statistics in business and economics. Using data, examples, and exercises drawn from the real world, The Practice of Statistics for Business and Economics teaches students the methods of statistical thinking, making data-based decisions using real data. With this textbook, instructors can help students develop a working knowledge of data production and interpretation in a business and economics context, giving them the practical tools they need to make data-informed, real-world business decisions from the first day of class. With the LaunchPad version of the textbook, both the print and online resource are combined, putting powerful statistical tools and interactive learning features in students’ hands.

Problem definition: Effective responses to abrupt demand shifts encountered by a supply chain require quick identification of market shifts and an update of supply chain contracts; however, such response efforts are often hampered because the updated contracts cannot be timely executed due to contract execution lag. This paper proposes an automated (short-term wholesale price) contract embedding with demand shift analysis, which can be enabled by blockchains and other IT developments, to meet this challenge. We examine the possible adoption of the proposed automated contract by assessing its value to firms in supply chains.Methodology/results: We develop an analytical model in which a supply chain can adopt either a conventional non-automated (long-term) contract or an automated contract in response to a potential demand shift. Under the automated contract, the wholesale prices are dynamically updated based on embedded Bayesian detection of the demand shift. A higher automation level means a smaller lag in executing the updated wholesale prices. A fully automated contract is known as a smart contract. We find that the magnitude and timing uncertainty of the demand shift are positive factors that favor the adoption of the automated contract in a supply chain. We also extend the model and explore the effect of supply chain competition on the adoption of automated contracts when two supply chains have Cournot competition.Managerial implications: Contrary to the conventional wisdom that smart contracts become more valuable as market uncertainties increase, an increase in timing uncertainty does not necessarily favor the adoption of fully automated smart contracts relative to partially automated alternatives. Although automated contracts can help a supply chain gain competitive advantage, a high competition intensity does not favor the adoption of automated contracts because a supply chain's marginal production cost savings of using automated contracts decreases as the competition intensity increases.