Most Master of Business Administration (MBA) programs include a statistics or quantitative analysis course. In Emporia State University’s online MBA program, the course is titled Quantitative Analysis of Business Decisions. It is one of the most valuable courses for future business managers and executives.
This course introduces the student to quantitative approaches in business decision-making. Methods include decision analysis, regression analysis in forecasting, linear and integer programming, project scheduling, inventory management, multi-criteria decision problems, and others. Students learn to utilize modern software like Management Scientist, Excel, and SAS/SPSS to support quantitative analysis and decision-making. These are the types of tools and processes business leaders rely on for success in today’s data-driven environment.
What Are Statistics and Quantitative Analysis?
Quantitative analysis uses mathematical and statistical modeling, measurement, and research to provide insight into past, current, and future events. Any subject involving numbers can be quantified, and the concepts involved in statistics or quantitative analysis can prove useful in fields as wide ranging as social science, chemistry, sports, healthcare, and financial analysis. In business, quantitative analysis is commonly used to analyze return on assets, sales revenues, profit margins, process control, and customer surveys.
The methods of quantitative analysis taught in ESU’s course provide predictive capabilities and the capacity to derive insights from data that drive strategic decision-making. Quantitative analysis processes have applications across business functions, especially when employed by professionals who know how to use analysis-based insight to inform effective decision-making. Understanding the nuts and bolts of quantitative analysis also supports collaboration with the number crunchers or quants who will also help guide future business decision-making.
Quantitative analysis underlies the advanced data analytics technologies that help businesses harness the potential of big data, as well as the business intelligence tools that help business users understand the implications of data. Constantly evolving AI-driven technologies further the possibilities, speed, magnitude and potential impact of analytics. Yet, quantitative analysis is the root of all these developments in technology and business applications.
How Quantitative Analysis Differs From Qualitative Analysis
These two disciplines combine to give business executives a complete set of analysis tools. Qualitative analysis evaluates information that cannot easily be reduced to numeric values, such as employer reputation or employee morale. Whereas qualitative analysis seeks to read meaning and insight into human behavior, quantitative analysis helps extract patterns and explore relationships among two phenomena or quantify business activities.
Key Concepts in Quantitative Analysis
Several factors contribute to the validity of findings in quantitative analysis and research. Proficiency in the methods of quantitative analysis requires a mastery of these fundamental factors or concepts. Validity, itself, is a core component, referring to ensuring that the research — and research process — accurately reflects what the research is intended to measure.
Another essential component of ensuring the validity of quantitative research results is reliability. Reliability refers to measuring the consistency of results, as in: If the analysis is performed repeatedly, the results should be consistent.
Statistical significance — measuring results against the likely margin of error — is also an important factor, as are judging the strength of correlation in terms of the relationship between variables and distinguishing between correlation and causation.
Determining the experimental validity of analysis is also crucial. This involves taking steps to ensure unintended variables do not detract from the validity of the results. For current context, ensuring experimental validity is one of the challenges facing advanced AI technology developers. Without extensive controls, AI-powered software can develop bias from various sources, such as biased training data, flawed algorithms, or bias on the part of the programmers. Thus, experimental validity is a central consideration in the development and application of new AI technologies like generative AI.
Clearly, quantitative analysis and statistics are both extremely important and very complex. Advanced coursework in programs like Emporia State University’s 100% online MBA can help students fully grasp the nuances of quantitative analysis in context of the tech-driven business world. Developing one’s quantitative skills can improve business decision-making and increase positive outcomes in all future business endeavors.
Learn more about Emporia State University’s online MBA program.