Chapter 3 examines measures of central tendency and their correspondence to normality and skewness. The three measures of central tendency presented include the mode, median, and mean. The mean is typically thought of as the average. The mode is the score occurring most frequently in a distribution of scores; the median is the central score, or the point which divides a distribution into two equal parts. The median is a robust statistic. The level of measurement assumption is crucial in selecting the best measure of central tendency for specific analyses.

Chapter 4 examines measures of variability. Variability is the degree to which scores in a distribution are spread out or clustered together. Scores in a distribution not only focus around certain reference points or central tendency but also focus in particular ways around these central points. To understand the significance of any distribution of scores, we must know about both its central tendency and its variability. The index of qualitative variation (IQV), range, standard deviation, mean absolute deviation, and variance are all important measures of variability. Variability allows researchers to provide a full picture of a distribution of scores.

As earlier chapters have made clear, the twenty-first century multinational enterprise (MNE) is markedly different from its twentieth century ancestors. It has been transformed by an environment in which multiple, often conflicting forces accelerate simultaneously. The globalization and deglobalization of markets, the acceleration of product and technology life cycles, the assertion of national governments’ demands, and, above all, the intensification of global competition have created an environment of complexity, diversity, and change for most of today’s MNEs.

In the preceding chapters, we described how changes in the international environment have forced MNEs to simultaneously respond to the strategic need for global efficiency, national responsiveness, and worldwide learning. Implementing a complex, three-pronged strategic objective would be difficult under any circumstances, but the very act of “going international” multiplies a company’s organizational complexity.

Few managers operating in today’s international business environment would dispute that this is an extremely exciting time to be engaged in almost any aspect of cross-border management. Fast-changing global developments have created big challenges that appear unusually complex, but at the same time they have opened up new opportunities that seem almost limitless.

Chapter 5 examines the normal distribution, its relationship to z-scores, and its applicability to probability theory and statistical inference. z-scores or standardized scores are values depicting how far a particular score is from the mean in standard deviation units. Different proportions of the normal curve area are associated with z-scores. The conversions of raw scores to z-scores and z-scores to raw scores are illustrated. Nonnormal distributions which differ markedly from normal curve characteristics are also described. The importance of the normal curve as a probability distribution, along with a brief introduction to probability, is discussed.

In Chapter 3, we described how MNEs competing in today’s global competitive environment are required to build layers of competitive advantage: i.e. the ability to capture global-scale efficiencies, local market responsiveness, and worldwide learning capability. As many of these companies found ways to match one another in the more familiar attributes of global-scale efficiency and local responsiveness, they had to find new ways to gain competitive advantage. In this process, competitive battles among leading-edge MNEs (particularly those in knowledge-intensive industries, such as telecommunications, biotechnology, and pharmaceuticals) have shifted their ability to link and leverage their worldwide resources and capabilities to develop and diffuse innovation.

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.