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© 2021 Informa UK Limited, trading as Taylor & Francis Group.Some adjustments are made to design weights to reduce the negative effects of non-response and out-of-scope problems. The calibration approach is a weighting process that agrees with the known population values by using auxiliary information. In this study, alternative calibration approaches and weight trimming process that can be used in large data sets with extreme weights and different correlation structures were analysed. In addition, the effect of the correlation structure of auxiliary variables on the efficiency of the calibration estimators was investigated by a simulation study. The 2017 Annual Industry and Service Statistics data were used in the simulation study and it was seen that restricted calibration estimators were more efficient than the generalized regression estimator in estimating the variables with a high variance such as turnover. Especially in small sample fractions, we recommend the application of restricted calibration estimators, as they are more efficient than the weight trimming in solving the negative and less than one weights problem encountered after the calibration process.

The authors set out to determine the brain dominance characteristics of students enrolled in business statistics courses in a historically Black university in a major southeastern Texas cit...

The basic business statistics course is ideal for incorporating multiple objectives related to program learning goals common at most business schools. Along with quantitative reasoning skil...

The authors perform a classroom experiment to test the effectiveness of discipline-specific case studies on knowledge acquisition and retention in advanced business statistics courses. The results indicate that although students are more engaged in the case studies, overall there is no impact on acquisition or retention of statistics knowledge by having students engage in discipline-specific case studies relative to traditional case studies. However, for high-performing students, discipline-specific case studies have a positive and significant impact on statistical knowledge retention. Moreover, our research reaffirms past research, which has found greater retention for students with higher grade point average, and there exists a significant negative correlation between retention and time.

Typically, the core-required undergraduate business statistics course covers a broad spectrum of topics with applications pertaining to all functional areas of business. The recently updated American Statistical Association's GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report once again stresses the pedagogical importance of topic and application relevancy in an increasingly data-centered world. To this end, only two introductory textbooks have incorporated some financial investment measures (Sharpe ratio and beta coefficient) in the teaching of numerical descriptive measures and simple linear regression analysis, respectively, while a few others include them as real-data application exercises at the end of their respective chapters. Although this latter coverage is in compliance with GAISE College Report recommendations on the importance of using relevant real data applications in the teaching of introductory business statistics, it forgoes an opportunity to provide more detailed discussion within the text itself and add value to a business student's learning. Given that all business students will have an opportunity to learn about financial investment, regardless of their functional area major, this paper offers a more proactive use of these and other financial investment measures as part of the current, traditional course or as part of a suggested dedicated introductory business statistics course for finance majors.

Business analytics continues to become increasingly important in business and therefore in business education. We surveyed faculty who teach statistics or whose institutions offer statistics to business students and conducted web searches of business analytics and data science programs that are offered by these faculties associated with schools of business. The intent of the survey and web searches was to gain insight on the current landscape of business analytics and how it may work synergistically with data science at institutions of higher education, as well as inform the role that statistics education plays in the era of big data. The study presents an analysis of subject areas (Statistics, Operations Research, Management Information Systems, Data Analytics, and Soft Skills) covered in courses offered by institutions with undergraduate degrees in business analytics or data science influencing statistics taught to business students. Given the notable contribution of statistics to the study of b...

Two purposes existed for initiating this qualitative case study involving adults who had completed a college-level business statistics course. The first purpose was to explore adult challenges with stress and anxiety during the course: a phenomenon labeled statistics anxiety in the literature. The second purpose was to gain insight into adult perceptions of an instructional methodology that employed collaborative problem solving as an intervention to reduce student anxiety levels. Findings from this research provided valuable insights regarding adult perceptions of statistics and statistics courses. First, 75% of the participants recalled their performance in the course as being challenged by elevated levels of stress and anxiety. We also found a distinct inverse correlation between adult self-perception of math competency and statistics anxiety levels. Also notable were findings that a majority of the adult participants perceived working collaboratively markedly reduced anxiety levels, resulting ...

The American College Test (ACT) is utilized to determine academic success in business core courses at a midlevel state university. Specifically, subscores are compared to subsequent student grades in selected courses. The results indicate that ACT Mathematics and English subscores are a valid predictor of success or failure of business students in statistics and management classes.

This article presents a comprehensive study of factors that potentially impact student performance and success in a “bottleneck” college-level course in Business Statistics, with the goal of devising effective intervention methods to provide additional support to students who are at risk of failing. The latter are based on statistical models that predict the probability of failure based on relevant factors identified earlier. These models report high accuracy in detecting at-risk students as assessed by cross-validation techniques. Moreover, implementation of our techniques yielded positive outcomes, indicating that those students who took advantage of the intervention significantly improved their performance.

This evaluation examines the impact on student success rates related to changes in instructional programmes in undergraduate mathematics and statistics courses. Success for students taking courses with a computer-based homework component was compared with success of students who took the course in prior semesters without the computer-based component. Graphical and analytical tools are used to compare results. Results come from multiple semesters of each type of homework application, for both pre-calculus algebra and business statistics courses. Students whose performance is utilised in this study are undergraduate students taking introductory level college mathematics or business statistics courses, with mostly no prior instruction at this level. Comparing the success of the intervention group with the success of the baseline control group, findings support that the students using the computer-based homework instruction are just as successful as those using the traditional method of homework instruction. ...

This study investigates students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement in higher education. Guided by learning environments concepts and attitudinal theory, a theoretical model was proposed with two instruments, one for measuring the learning environment and the other for measuring student attitude. Data were collected from 453 postgraduate business students in a UK university. Factor analyses were carried out to validate the instruments, whilst structural equation modelling was employed to validate the proposed model. The results demonstrate the importance of students' perceptions of subject integration within the learning environment and the relationship between their perceptions of cohesiveness, task orientation, and anxiety. The findings show that attitude towards statistics is significantly related to achievement, explaining almost 40% of variation, whilst perception of the learning environment (indirectly) exp...

In the modern business environment, managers are increasingly required to perform decision making and evaluate related risks based on quantitative information in the face of uncertainty, which in turn increases demand for business professionals with sound skills and hands-on experience with statistical data analysis. Computer-based training technologies allow the new cadre of business professionals to obtain such a hands-on experience in an environment where mistakes can be made and outcomes can be measured. The authors discuss their experiences in developing a new e-learning tool designed to apply methodological forecasting concepts to real-life business and finance problems through an interactive self-learning and self-assessing module of online case studies.

Peer-assisted study session (PASS) programs have been shown to positively affect students' grades in a majority of studies. This study extends that analysis in two ways: controlling for ability and other factors, with focus on international students, and by presenting results for PASS in business statistics. Ordinary least squares, random effects and quantile regression models have been used to model data from first-year business statistics students. The findings indicate that the impact of PASS has remained highly significant in both years for both local and international students but is more pronounced for international students. We also find that lower-achieving students derive a higher marginal benefit from attending PASS than higher-achieving students using quantile regression. These findings are significant for institutions implementing similar programs as well as institutional efforts to enhance student performance and improve student retention, or specifically to support international students mor...

This study investigates the effects of collaboration and competition on students’ learning performance in a course of business statistics. The collaboration involved a simultaneously organised group competition project with analysis of real-life business problems among students. Students from the following schools participated: JAMK University of Applied Sciences in Finland, Universidad Complutense de Madrid in Spain, and Sabanci University in Turkey. The results support earlier literature on positive impacts of group collaboration on learning performance but deny any negative impacts of competition. It is also found out that learning performance may be influenced to a certain degree by cultural differences in perceptions towards collaboration and competition. Overall the international competition and the touch to real-life business problems stimulate students’ engagement and result in enhanced learning towards becoming ‘intelligent consumers of business statistics’.

URL: http://users.dickinson.edu/∼erfle/ExcelGraphics/Multicollinearity.xlsxInstructors teaching empirical techniques in managerial economics or economic and business statistics courses often face a...

The article examines basic problems of the current system of government statistics from the perspective of business as a consumer of statistical data. Alternative possibilities for providing information on consumer markets and sectoral dynamics are discussed. Suggestions are made for improving the system of government statistics.

This study utilises an integrated conceptual model of academic performance which captures a series of psychological factors: cognitive style; self-theories such as self-esteem and self-efficacy; achievement goals such as mastery, performance, performance avoidance and work avoidance; study-processing strategies such as deep and surface learning; and effort. We investigate a group of first-year university undergraduates taking a course in business statistics at a British university. The results show a significant causal path of the form: self-esteem→ self-efficacy→ mastery→ effort→ performance. We conclude that the strengthening of any of the elements in this path would have beneficial effects on students’ academic performance and discuss various approaches to pedagogy, primarily assessment and feedback, to achieve this goal.

Anecdotal evidence suggests that those students who take their lower level business principles courses at junior or community colleges are less prepared for success in intermediate business courses than their classmates who take them at a 4-year institution. Using data on student performance in four typical intermediate business courses—Intermediate Microeconomics, Intermediate Macroeconomics, Intermediate Business Statistics, and Intermediate Financial Accounting I—the authors found that average student performance in these courses is essentially completely determined by the underlying academic quality of the student, as measured by grade point average. Where they take their lower level prerequisite courses, and what letter grade they earn in those courses, do not generally play a significant role.

Teaching in a classroom configured with computers has been heralded as an aid to learning. Students receive the benefits of working with large data sets and real-world problems. However, with the advent of network and wireless connections, students can now use the computer for alternating tasks, such as emailing, web browsing, and social networking. Do these distractions diminish the benefits of the computers? An experiment was conducting comparing exam scores in two different classes, one class held in a traditional classroom and another class taught in a computer lab classroom. Holding constant other factors such as student's ability and demographics, the results show a small negative effect from conducting class in a computer equipped classroom.

Bootstrapping methods and random distribution methods are increasingly recommended as better approaches for teaching students about statistical inference in introductory-level statistics courses. The authors examined the effect of teaching undergraduate business statistics students using random distribution and bootstrapping simulations. It is the first such empirical demonstration employing an experimental research design. Results indicate that students in the experimental group—where random distribution and bootstrapping simulations were used to reinforce learning—demonstrated significantly greater gains in learning as indicated by both gain scores on the Assessment of Statistical Inference and Reasoning Ability and final course grade point averages, relative to students in the control group.

Sample surveys are widely used in the social sciences and business. The news media almost daily quote from them, yet they are widely misused. Using students with prior managerial experience embarking on an MBA course, we show that common sample survey results are misunderstood even by those managers who have previously done a statistics course. In general, they fare no better than managers who have never studied statistics. There are implications for teaching, especially in business schools, as well as for consulting.

The logarithmic transformation is often used in regression analysis for a variety of purposes such as the linearization of a nonlinear relationship between two or more variables. We have noticed that when this transformation is applied to the response variable, the computation of the point estimate of the conditional mean of the original response variable is often incorrect. Although the correct procedure has long been known in the scientific community and is well documented, an incorrect or misleading procedure is used in many business statistics textbooks. This incorrect procedure results in errors that can be quite significant. Our article uses a real-data business example which, in the context of making a decision about an advertising charge for a magazine, illustrates the correct procedure. This example also provides a sense of the magnitude of the error that would result if the incorrect procedure were used. The six percent error in our example could be substantially higher for other applic...

Composite indicators (or indexes) are very common in economic and business statistics for benchmarking the mutual and relative progress of countries in a variety of policy domains such as industrial competitiveness, sustainable development, globalisation and innovation. The proliferation of the production of composite indicators by all the major international organizations is a clear symptom of their political importance and operational relevance in policy-making. As a consequence, improvements in the way these indicators are constructed and used seem to be a very important research issue from both the theoretical and operational points of view. This paper aims at contributing to the improvement of the overall quality of composite indicators (or indexes) by looking at one of their technical weaknesses, that is, the aggregation convention used for their construction. For this aim, we build upon concepts coming from multi-criteria decision analysis, measurement theory and social choice. We start from the analysis of the axiomatic system underlying the mathematical modelling commonly used to construct composite indicators. Then a different methodological framework, based on non-compensatory/non-linear aggregation rules, is developed. Main features of the proposed approach are: (i) the axiomatic system is made completely explicit, and (ii) the sources of technical uncertainty and imprecise assessment are reduced to the minimum possible degree.

The authors evaluated the effect on learning of using case studies in business statistics courses. The authors divided students into 3 groups: a control group, a group that completed 1 case study, and a group that completed 3 case studies. Results evidenced that, on average, students whom the authors required to complete a case analysis received significantly higher grades on their comprehensive final examination. Their perceptions of their learning experience were also significantly higher with respect to communication, software use, and the ability to apply statistics to business decisions.

Most homework sets in statistics courses are constructed so that students concentrate or “mass” their practice on a certain topic in one problem set. Distributed practice homework sets include review problems in each set so that practice on a topic is distributed across problem sets. There is a body of research that points to the efficacy of distributed practice for developing a variety of skills from word recall to surgical techniques. A trial was conducted in several sections of a business statistics course where students were randomly assigned to either have massed practice homework sets or distributed practice homework sets. The two groups were then compared on the course assessments. The results show some evidence for the efficacy of distributed practice homework sets, although this effect may be modified significantly by the instructor or by a Hawthorne effect.

This study aimed to quantify the influence of student attributes, coursework resources, and online assessments on student learning in business statistics. Surveys were administered to students at the completion of both online and on-ground classes, covering student perception and utilization of internal and external academic resources, as well as intrinsic motivating factors for success in the course. Student performance as defined by quality points, various assignment points, and time spent on assignments, was not significantly different between on-ground and online students. However, use of resources and tools to complete homework and learn new topics differed. As a whole, students predominantly utilized homework as the first tool to learn new topics and complete homework, suggesting a paradigm shift in the way instructors should cater to student’s learning habits.

We revisit the issue of internationalizing the required course in business statistics as a means for introducing international subject matter earlier in the undergraduate business curriculum. A survey of sophomore business students indicates that their level of international knowledge is poor. The results are strikingly similar to a decade ago. Moreover, an examination of a sample of twenty current business statistics textbooks shows little improvement in the extent of international coverage over the last ten years. It appears that an unmet need to impart international information earlier to undergraduate business students still exists, and that the required course in business statistics remains a viable option for this purpose. We include sources of relevant international data for suggested use in course examples and exercises.

In this investigation of student performance in introductory business statistics classes, the authors performed two separate controlled studies to compare performance in (a) distance-learning versus traditionally-delivered courses and (b) multiplechoice versus problem-solving tests. Results of the first study, based on the authors' several semesters of experience teaching the course in both distance-learning and traditional formats, show that the distance-learning students did not fare as well as those taking the course in the traditional format. The results of the second study, in which a common set of students took both multiple-choice and written exams in the same semester, showed no significant difference in performance.

Delivering MBA statistics in the online environment presents significant challenges to education and students alike because of varying student preparedness levels, complexity of content, difficulty in assessing learnng outcomes, and faculty availability and technological expertise. In this article, the author suggests a process model that develops key learning strategies for the online environment and offers accreditation- and literature-based guidelines for overcoming obstacles to sucessful mastery of MBA-level statistics. Intended for faculty members, administrators, and students with limited experience in online education, the model may stimulate discussion and investigation of successful practices appropriate for teaching business statistics.

This chapter outlines key sources for business statistics, including governmental sites and resources from private companies.

Business statistics courses are not typically popular, and students often find the examples and textbook problems tedious and irrelevant. In this article, the author describes a pedagogy project that keeps students interested and working effectively in a large class. Through a student survey, the author gathered data on students' university experience and demographics. The class used the Statistical Package for the Social Sciences (SPSS) data set for demonstration of a range of statistical techniques. The student survey instrument and data set provided examples and problems for each of the major topics of the course. Student comments and performance demonstrated the project's positive results.

In this article, the author proposes the use of one of the tools of the Theory of Constraints—referred to as the prerequisite tree—as a framework for providing structure to the development of graphic organizers used in introductory statistics courses. Even though statistics is an important course for business majors, students often do not have an easy time understanding the material. Graphic organizers could be a strategy to help students overcome their concerns. The author provides guidelines for implementation of graphic organizers in the classroom, especially in statistics.

Since 1986, the Making Statistics More Effective in Schools of Business (MSMESB) conferences have gathered academics and interested parties from government and industry to improve the effectiveness of statistics in business and business schools. We focus on the impact of MSMESB on business statistics courses, textbooks, and software. We trace some of MSMESB's history and draw out some recommendations on which consensus seems clear. Finally, we describe some key challenges and opportunities for the future.

In this study, the author examined the effect of a semester-long, active-learning project on student learning in a business statistics course. The author investigated whether students who engaged in this project performed better on project-related examinations than did students who did not participate in the project. Results showed that, compared with the traditional lecture format, the active-learning technique encouraged students to become more involved with the course content through application of theory to real-life situations. Thus, the active-learning students gained a better understanding of statistics than did the students in the traditional class.

In this study, the author administered a 19-question survey instrument to students in 2 types of business statistics classes at Virginia Commonwealth University. In the 1st class, a traditional 1, students used pocket calculators during exams and had access to tables in a textbook. In the other class, a technology class, each student had a computer and used the Minitab statistical software for calculations. The author compared the 2 classes to investigate differences in attitude toward technology learning and learning enhancement. Results showed that both classes felt comfortable with their corresponding approaches and performed similarly on a common final exam.

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.

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.

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.

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.

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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.

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.

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.

Currently there is a strong movement to revise the introductory business statistics course to make the course more relevant and appealing to business students. A number of major changes in the traditional course have been recommended. Our concern is that we do not ignore the needs of the instructor who teaches the functional area courses and expects the students to have specific statistical skills. We designed and conducted a survey at Miami University to determine which statistical methods are currently being used in upper level business courses. Our survey results indicate that certain areas of statistical instruction that have been recommended for less emphasis, such as formal theory of probability and statistics, including the testing of hypotheses, are currently being used in a large percentage of the business courses. In addition, at least one proposed addition to the course may be of little interest to the instructor in the functional areas.

We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: students’ performance on the final exam and their perceptions of the relevance of statistics for their professional development. We find no statistically significant difference between ASL and CS students with regard to final examination performance, but students who participated in the ASL project as opposed to CS were less likely to agree that ―[they] will have no application for statistics in [their] profession[s].‖ The estimated relationship is both large and statistically significant (p < 0.01).