The international business environment has always been characterized by continual change. The task facing MNE managers is to manage that change. The situation in the third decade of the twenty-first century is no different. Important shifts in political, social, economic, and technological forces have combined to create management challenges for today’s MNEs that differ fundamentally from those facing companies in the early 2000s. Yet, despite intense study by academics, consultants, and practicing managers, both the nature of the various external forces and their strategic and organizational implications are still disputed.

Chapter 11 introduces students to bivariate (simple) regression and multiple regression. Students learn the importance of linear relationships and how linearity can be used to make predictions on one variable from the knowledge of another variable or multiple variables. Interpretation and conceptual understanding of critical concepts in regression are emphasized.

Chapter 6 introduces the hypothesis-testing process and relevance of standard error in reaching statistical conclusions about whether to accept or reject the null hypothesis using the z-test statistic. Type I and Type II errors, along with the types of statistical tests researchers apply in testing hypotheses, are presented; these include one-tailed (directional) versus two-tailed (nondirectional) tests. Three important decision rules are the sampling distribution of means, the level of significance, and critical regions. Type I and Type II errors influence the decisions we make about our predictions of relationships between variables. Statistical decision-making is never error-free, but we have some control in reducing these types of errors.

For most transnational companies, the twenty-first century offers exciting prospects of continued growth and prosperity. Yet, in the poorest nations on Earth, the reputation of large MNEs from the world’s most developed countries was shaky from 2000 onwards, and in some quarters, in complete tatters. Indeed, a series of widely publicized events in the early decades of the twenty-first century led many to ask what additional constraints and controls needed to be placed on their largely unregulated activities.

Chapter 12 examines some nonparametric statistical tests designed for data applications appropriate for the nominal or ordinal level of measurement. Chi-square tests have few restrictive assumptions underlying their application and are used for data which violate one or more of the formal assumptions regarding the use of parametric statistics. A presentation illustrating tabular construction for one variable, two variables, and k variables is provided, then chi-square tests for a single sample, two samples, and k samples are described. The chapter also presents the chi-square test for independence, which can be applied to frequency data that are cross-tabulated for two or more nominal variables. This test evaluates frequency data to determine the relationship between two variables in the population.

Chapter 1 explores the link between the research process and theory and the role of statistics in scientific discovery. Discrete and continuous variables, the building blocks of methodology, take center stage, with clear and elaborate examples and their applicability to scales of measurement and measures of central tendency. Understanding statistics allows us to become better consumers of science and make better judgments and decisions about claims and facts allegedly supported by statistical results.