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This article discusses the use of Excel in the introductory course in statistics in the business school. An attempt is made to identify how business school programs differ, and how the use of Excel to work cases or examples can contribute to a business statistics course that is in keeping with the school's and the instructor's learning objectives.

The emphasis on mechanics in business statistics courses poses a problem for marketing research instructors who teach their course with an application emphasis. Instructors of marketing research could find students ill prepared for the course. As a result, some marketing programs require students to take a research laboratory or break the research course into two parts of 3 credit hours each. This article outlines an alternative. Repositioning (reengineering) the second course in business statistics from a problem-solving orientation to a decision-making emphasis eliminates the need for an additional research course. A 2-year experience teaching the course shows students to be better prepared for the marketing research course. In addition, how to develop and assess key business skills such as communication, problem solving, decision making, and computer use in the course is also discussed. Assessment of outcomes is presently mandated by the American Assembly of Collegiate Schools of Business (AACSB).

The concept of power in statistical hypothesis testing is typically introduced by emphasizing (a) the relationship with Type I and Type II errors and (b) the use of power for planning sample sizes. In this article, we propose use of power information subsequent to results of testing hypotheses through use of statistical tests in business research studies to assist in interpretation of existing data and for guidance in determining whether additional data might be necessary. We provide several examples and recommendations on how such posttest use of power might be integrated within business statistics courses.

In preparing business majors, schools of business have a special responsibility to ensure that students are equipped with the technological and mathematical skills they will need to compete in the worldwide marketplace. This article describes an innovative instructional paradigm for teaching business statistics. This paradigm harnesses the latest classroom technologies and integrates these technologies with established principles of learning. The paradigm also implements the mathematical and technological recommendations put forward by the National Research Council of the National Academy of Science. Evaluation data from both instructor and student perspectives are reported.

Undergraduate students' perceptions of good teaching have been widely investigated but studies have not dealt with statistics specifically. The study reported here analyses these perceptions for a large business statistics service course and compares the results with other disciplines. In addition the most important tasks of astatistics teacher from the students' view are identified. The effects of poor teaching are also examined. The results provide a checklist for those teaching large introductory business statistics courses, especially beginning instructors.

We provide guidelines for developing case-based business statistics courses. Specifically, we describe both the benefits and pitfalls of case-based courses, and list resources available for course development. We describe the characteristics of the instructor (and the classroom!) which augur well for case-based teaching.

We evaluate the effectiveness of using the HTML META tag to improve retrieval of World Wide Web documents through Internet search engines. Twenty documents were created in five subject areas: agricultural trade, farm business statistics, poultry statistics, vegetable statistics, and cotton statistics. Four pages were created in each subject area: one with no META tags, one with a META tag using the keywords attribute, one with a META tag using the description attribute, and one with META tags using both the keywords and description attributes. Searches were performed in AltaVista and Infoseek to find terms common to all pages as well as for each keyword term contained in the META tag. Analysis of the searches suggests that the use of the keywords attribute in a META tag substantially improves accessibility while use of the description attribute alone does not. These results suggest that HTML document authors should consider using keywords attribute META tags. We also suggest that more search engines index the META tag to improve resource discovery.

Many students find the application of sampling theory to audit testing difficult, even if they have a good grounding in business statistics. This paper describes an action-learning technique used to teach audit sampling theory to third-year business degree students at The Open Polytechnic of New Zealand. Feedback from students indicated that they found action-learning better than traditional lectures and printed material in enhancing their understanding of sampling theory and practice and in maintaining their interest and enjoyment.

This study surveyed 500 institutions of higher education in the United States for data on their course content and software use. In undergraduate Business Statistics I courses, the most prevalent topics covered were data analysis, probability, sampling distributions, estimation, means and hypothesis testing; the most popular software by far was Minitab. In Business Statistics II courses, regression analysis, hypothesis testing, time series, and forecasting are most important. Minitab, SAS, Execustat, and ASP were the prevailing software. Most schools of business require both Business Statistics I and II for all students. We present further findings regarding course requirements and content.

This paper seeks to compare the traditional delivery of introductory courses in business statistics within a business and management faculty, with that of an approach that seeks to teach through information technology (IT). To achieve this objective this paper will examine how traditionally taught courses in business statistics are delivered and compare this with a teaching approach through the use of IT

This work proposes a characterisation of the patenting behaviours of firms. It relies on patent data linked to firm data from a commercial dataset, regards firms of 20 or more employees located in 15 countries, and refers to the period 1999-2010. The way in which patent assignees’ names are linked to firm names is explained, and the coverage and representativeness of the firm database used is discussed using information from structural business statistics. The profile of patenting and non-patenting firms is delineated on the basis of characteristics such as firm size, ownership, firm age and industry, and of combinations thereof. Statistics related to the sector-specific patterns of patent renewals are also shown.

Depending on the source, a reader may be left with the impression that Family Businesses typically account for between 75% and 95% of all enterprises operating in an economy. This study, using official structural business statistics from Ireland, suggests that in fact Family Businesses may account for a considerably smaller share of the business economy; something closer to 46%. This paper attempts to explain how such a discrepancies can arise. Using the Annual Services Inquiry (CSO, 2007a) compiled by the Central Statistics Office (CSO) as the anchor dataset, micro data are linked to the e-Commerce and ICT survey (CSO, 2006b), Community Innovation Survey (CSO, 2008b) and VAT registrations datasets in order to determine whether labour productivity in Family Businesses (FBs) is significantly different to that of Non-Family Businesses (NFBs) and if Information and Communication Technologies (ICT) or innovation has an impact. The paper also highlights the significant impact that Foreign Direct Investment makes to the Irish economy. This analysis is particularly relevant for a small open economy like Ireland where 31% of traded services GVA and 15% of employment is generated by multinational enterprises.

Business statistics, Constant, Continuous data, Cross sectional data, Data, Descriptive statistics, Discrete data, Distribution, Frequency, Inferential statistics, Longitudinal data, Population, Probability, Qualitative data, Quantitative data, Random sample, Sample, Scales, Statistics, and Variables.

In the past few years the modernization of business surveys has been extensively discussed by several NSI’s in Europe. The MEETS program (Modernisation of European Enterprise and Trade Statistics) has been recently launched for the years 2008–2013 by EUROSTAT for encouraging the reform process at the European level in order to identify new areas for business statistics, to enhance the integration of data collection and treatment, and to improve the harmonization of methods and concepts in business statistics. At Statistics Belgium the debate has been brought especially through a revision of concepts and methods in business surveys with the aim of reducing the survey costs for the Administration and the response burden for the enterprises. In the present contribution, the issue of integration of business surveys is tackled with a variable-oriented approach and using classification techniques.

In its Standard C.1.1, the American Assembly of Collegiate Schools of Business (AACSB) requires the treatment of ethics and diversity issues in the curricula of the institutions it accredits. Addressing these issues in the statistics classroom not only provides an opportunity for meeting the accreditation criteria; it also crates interesting and relevant applications of statistical tools. This article suggests learning goals and assessment criteria for the integration of ethics and diversity issues and provides examples of means by which an instructor can make the integration effective.

Daisy P. Waters reviews Using Microsoft Access XP, Hazel Cameron reviews Business Statistics on the Web, Elizabeth Parang reviews JSTOR: A History, and Sheila Corman reviews MARC21 for Everyone.

Internationalization of the business curriculum to prepare students for the challenges of a global economy is mandated by the AACSB. This article explores the potential for internationalizing the required course in business statistics. An examination of current textbooks in the field reveals a noticeable absence of problems, exercises, or applications dealing with international material. We here describe our experience incorporating international material throughout the business statistics course. Student reaction to this course was generally positive.

Financial econometrics is the econometrics of financial markets. It is a quest for models that describe financial time series such as prices, returns, interest rates, financial ratios, defaults, and so on. The economic equivalent of the laws of physics, econometrics represents the quantitative, mathematical laws of economics. The development of a quantitative, mathematical approach to economics started at the end of the 19th century in a period of great enthusiasm for the achievements of science and technology. Robert Engle and Clive Granger, two econometricians who shared the 2003 Nobel Prize in Economics Sciences, have contributed greatly to the field of financial econometrics. Keywords: Static models; Dynamic models; data generating process; white noise; model selection; model estimation; model testing; ex novo; Statistical learning; probabilistic models; nonstationary time series; stationary time series; High-frequency data; model risk; model robustness; ex ante; model performance

The Height and Shoe Size dataset contains information on height (in inches), dress shoe size, and gender for 408 college students. The information was collected to provide an interesting initial example for the study of correlation and regression in a business statistics class. Students don’t mind providing this information (unlike weight, about which they are often more sensitive) and seem to enjoy seeing how accurate the resulting prediction is for their particular height or shoe size. Once each semester’s values are added to the file, it is posted and used for a series of assignments. We begin with correlation, move on to a series of simple linear regression assignments, and finish by incorporating a dummy variable for gender. This data set could also be used for frequency distributions, histograms, or inference.

Service-learning can mean different things and look quite different in varying statistics curricula that may include undergraduates, graduates, majors and non-majors across a wide array of higher institutions. The terms community engagement, volunteerism, community-based projects and service-learning are tossed around on various institutions‟ websites. The purpose of this article is two-fold. First is to provide an historical review of the evolution of service-learning activities to try to unify and define the terminology as one might use this pedagogy for statistics instruction. Second is to present some examples of how a first and second course in business statistics can step up from service-learning and move up the continuum towards reaping the reciprocal benefits of SERVICE-LEARNING (SL). In this article, service learning (note the omission of a hyphen) is a valued classroom service activity that separates the activity from the learning goals of the class, while service-learning (note the presence of a hyphen) is a teaching methodology in which the service and learning goals are carefully given equal weight in the development of the project so that classroom goals and service outcomes enhance each other providing a reciprocal experience for all participants (Sigmon 1994). When this careful design is a “method of teaching through which students apply newly acquired academic skills and knowledge to address real-life needs in their own communities” (ASLER 1994), SL unifies what students are currently learning in the classroom with the service they are simultaneously providing in the community. Careful design opens the door to provide opportunities of SL in an introductory, non-majors statistics class.

Statistical sources on the demand side of the labour market have been historically developed later than those on the supply side. A quarterly business survey was set up by ISTAT in 2003 to contribute to fill in some of these gaps, in particular, those on job vacancies and hours worked. Attention has been paid, when designing the survey and especially for the processing procedures, to the already existing sources, in order to fully exploit the available information in the editing and imputation and grossing up phases, and to ensure consistency among aggregate data produced by different sources. In this paper, it is discussed how the integration between the data collected by the job vacancy and hours worked and two other Istat business surveys has shaped the data processing methods which are used to produce the quarterly job vacancy indicators and differentiated them from others used in short-term business statistics.

According to Howard Garner, Professor of Cognition and Education at Harvard University, intelligence of humans cannot be measured with a single factor such as the IQ level. Instead, he and others have suggested that humans have different types of intelligence. This paper examines whether students registered in online or mostly online courses have a different type of intelligence from students registered in traditional face-to-face courses. At the beginning of the fall semester of 2011, a group of 128 students from four different courses in Business Statistics completed a survey to determine their types of intelligence. Our findings reveal surprising results with important consequences in terms of teaching styles that better fit our students.

A Classroom Response System (CRS) was adopted in an introductory business statistics course for the first time. This paper reports on the reflections on this experience by the professor teaching this course and feedback and contributions of the other members of the research team. It begins by presenting a theoretical foundation for the use of CRS and proceeds to a rationale for its adoption in statistics courses. A detailed description of how CRS was used in this particular course is provided and lessons learned are discussed in light of the relevant research literature. We hope that our reflections and lessons learned will raise awareness among faculty members contemplating using CRS, especially those in introductory statistics, about issues that might be encountered the first time around and help them be more proactive in their initial use of CRS.

With the focus on larger and faster computers and multimedia presentations, the trend in computer-aided instruction implies bigger (or more complex) must be necessarily better. Whereas more powerful applications allow economics instructors to demonstrate some very interesting and often complicated procedures and theories, there is much that can be done with very simple and widely available programs. In this paper, I explain the advantages of introducing undergraduate students to econometrics by helping them design their own statistical packages using a spreadsheet program. Keywords : Economics, instruction, computers, econometrics, statistics, spreadsheets.

This article describes a team-based project developed for undergraduate students in both business communication and business statistics classes in a small, midwestern college. More than 94% of the students endorsed the usefulness of the project, which was designed to help them develop communication competencies in multiple areas: working in teams, writing collaboratively, participating in meetings, and giving and receiving constructive criticism. The project presents a model of collaboration between instructors in business departments.

Textbooks in introductory statistics and production operations management during three time periods (1982, 1985–1988, and 1989–1992) were examined for their coverage of topics relevant to total quality management (TQM). Business statistics textbooks during the first two periods provided little coverage of quality, but there was a sharp improvement in the most recent period. Production and operations management textbooks contain much stronger coverage than statistics textbooks. Textbook publishers are addressing the growing importance of TQM, but textbooks tell only part of the story. This article identifies factors that may influence classroom coverage of TQM, suggests ways to strengthen teaching of TQM, and proposes future directions for research.

The advent of electronic business (e-business) has made a large change in the way of business. Electronic business growth has increased very much in a great deal of practices. Researchers have posed rules-based auditing systems for electronic business transactions, but they rely on the auditor's knowledge of e-business fraud highly. While fraud happen, it is difficult to deal with by business systems due to the large number of online transactions. In this paper, we introduce a method that to bring about rules from fraud patterns based on data mining. Nextly, the system can distinguish transactions that have a high chance be deceptive by making use of these rules to e-business databases.

Distance education is now an integral part of offering courses in many institutions. With increasing access to the internet, the importance of distance education will only grow. But, to date, the specific benefits that distance education brings to student learning objectives remain unclear. We first propose a framework that links student performance and satisfaction to the learning environment and course delivery. Next, we empirically evaluate our framework using data from a Business Statistics course that we offer in the traditional classroom setting and as a distance-education course. Our results show that while a well-designed distance-education course can lead to a high level of student satisfaction, classroom-based students achieve even higher satisfaction if they are also given access to online learning material. This indicates that material for an effective distance-education course can also be used to supplement in-class teaching to increase satisfaction with student learning objectives.

Missing from business statistics texts are several tools, but how does one add additional material to an already crammed, required course? We briefly describe these tools. This paper explains how to incorporate these missing tools in a time-efficient manner. We show how to teach them in a way that helps students to: (1) learn basic statistical concepts, (2) distinguish the two kinds of variation, (3) find structure within data, and (4) set priorities on problems. By learning these four concepts at the beginning of a statistics course, students will then have a solid, practical foundation for learning the remaining statistical material to be covered.

The objective of this study was to determine whether smaller class size, defined as fewer than 20 students in Business Statistics class influence grades earned in the course. The investigator hypothesized that there is a positive relationship between class size and performance, which is measured by grade earned in the course. The study revealed a positive relationship between performance and class size. The results also indicated that the groups that were in the smaller classes performed better on tests than the control groups that were in the larger classes. Furthermore, the study revealed that although there was a positive correlation between performance and class size, simply reducing class size does not guarantee significant increases in performance for all students.

This technical report presents statistical analysis of summative assessment data (mid-term tests and final exams) collected from business statistics modules. The analysis showed that grade distributions tend to have two modes which in turn, suggests that a large group of learners exercised surface learning strategies while a smaller group exercised deep learning strategies. The phenomenon persisted under different formats of assessment and module organisation, and across two institutions. The final part of the report summarises threshold concepts of statistics and quantitative analysis, as taught in business schools. The concepts were classified as basic, disciplinary and procedural. The classification helped to understand that the volume of procedural knowledge to be learned in statistics prevents from in-depth learning and passage through ‘a portal’ of disciplinary threshold concepts. A forced experiment to run a short course in statistics only reinforced the conclusion and posed irresolvable challenge because short courses and assessment cycles stimulate surface learning. Indicative topic outlines that were developed through several iterations for both, long (one term) and short (half-term) introductory modules in statistics are presented in Appendices.

Publisher Summary Philosophy has a broader scope than the specific sciences. It is concerned with general principles and issues. “Statistics” is a specific branch of knowledge that, among many other activities, includes addressing reliable ways of gathering data and making inferences based on them. Statisticians are interested in knowing which tools to use and what mechanisms to employ in making and correcting inferences. This chapter presents papers from various fields of expertise, which include philosophy, statistics, mathematics, computer science, economics, ecology, electrical engineering, epidemiology, and geo-science. The issues considered include the following: the causal inference in observational studies; the recent advances in model selection criteria; such foundational questions as “acceptance of the likelihood principle (LP)” and “conditional probability”; the nature of statistical/probabilistic paradoxes, the problems associated with understanding the notion of randomness, the Stein phenomenon, general problems in data mining, and a number of applied and historical issues in probability and statistics. Four statistical paradigms discussed are: classical statistics or error statistics, Bayesian statistics, likelihood-based statistics, and the Akaikean-Information Criterion-based statistics.

This paper presents a novel linguistic information extraction approach exploiting analysts' stock ratings for statistical decision making. Over a period of one year, we gathered German stock analyst reports in order to determine market trends. Our goal is to provide business statistics over time to illustrate market trends for a user-selected company. We therefore recognize named entities within the very short stock analyst reports such as organization names (e.g. BASF, BMW, Ericsson), analyst houses (e.g. Gartner, Citigroup, Goldman Sachs), ratings (e.g. buy, sell, hold, underperform, recommended list) and price estimations by using lexicalized finite-state graphs, so-called local grammars. Then, company names and their acronyms respectively have to be cross-checked against data the analysts provide. Finally, all extracted values are compared and presented into charts with different views depending on the evaluation criteria (e.g. by time line). Thanks to this approach it will be easier and even more comfortable in the future to pay attention to analysts' buy/sell signals without reading all their reports.

Doing business in the East African Community 2011 is a regional report that draws on the global doing business project and its database as well as the findings of doing business 2011: making a difference for entrepreneurs, the eighth in a series of annual reports investigating regulations that enhance business activity and those that constrain it. Doing business presents quantitative indicators on business regulations and the protection of property rights that can be compared across 183 economies from Afghanistan to Zimbabwe over time. This report presents a summary of doing business indicators for the East African Community. It focuses on five economies: Burundi, Kenya, Rwanda, Tanzania and Uganda. Data in doing business 2011 are current as of June 1, 2010. The indicators are used to analyze economic outcomes and identify what reforms have worked, where and why. The methodology for the employing workers indicators changed for doing business 2011.

This work proposes a class of latent process models which extend the customary hierarchical Bayesian (HB) small areal-level models in order to address typical prob-lems arising in small area estimation (SAE). Our approach envisions, for the entire re-gion under study, an underlying intensity surface which is block-averaged to the areal units relevant to the study. This framework encompasses all the main instances of pa-rameters being considered in SAE studies. E.g., we can think of an incidence surface when the characteristics of interest are area counts or totals, a probability surface when they are percentages, a proper intensity surface when they are continuous vari-ates. Such intensities are commonly used for explaining spatial point patterns and are generally modeled as realizations of (functions) of latent Gaussian processes. The proposed class of models allows for integrating multiple sources of potentially relevant information, even if available on a spatial scale different from that of small areas. These supplementary sources may consist either in auxiliary data or in survey data collected on planned or major domains. Moreover, in the proposed class bench-marking to large area estimates is automatically satisfied. To illustrate our approach we consider the problem of estimating parameters re-lated to firms’ activity and performance (such as the turnover or value added, which typically have a skewed distribution) as well as indicators of innovation efforts under-taken by enterprises (such as the percentage of firms investing in innovation) at Local Labour Market level. The parameters of interest are thus non-continuous or, if con-tinuous, non-symmetrical, as business statistics frequently are. Our proposal will then consider opportune specifications of unmatched models where the sampling error is non-normally distributed as standardly is in SAE problems. We carried out a simulation to compare the performance of our proposed model to that of customary small area models. The simulation is based on the sampling de-sign adopted in business surveys which typically is one-stage stratified. Predictive error of the Bayesian models under comparison is evaluated in terms of standardly used frequentist properties.

The concept of fail-forward can be used as a teaching technique to motivate students to learn from their mistakes. For example, when students are allowed to re-work incorrect responses on a test for a partial grade they are failing-forward. In this paper we look at the effects of failing-forward on student effort in online learning environments. We consider two alternative grading schemes with different levels of penalties for failure. Under the first grading scheme students are allowed to submit their work up to three times without being penalized. Under the alternative grading scheme students are penalized every time that they answer questions incorrectly. We find that instructors may be able to use the “average of all scores” grading scheme to increase the level of preparation of students even with differences in students’ innate ability. However, the benefits are less pronounced in fully online classes where there is no face-to-face instruction time.