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Spreadsheet to accompany product 9B16E017.

Spreadsheet to accompany product 9B16E018.

Histograms are graphs that provide a large amount of useful information for all types of data and display the frequency of observations in a data set. The purpose of this note is to introduce histograms for several types of data, provide examples of insights that can be gained and calculations that can be performed using histograms, and demonstrate how to build histograms in Microsoft Excel. In addition, this note reviews the Normal distribution, which can be used to approximate many empirical distributions. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.

This teaching note provides step-by-step illustrations and practice problems to assist students in learning how to use basic mathematical functions in Microsoft Excel. These functions are the necessary building blocks for many spreadsheet models. This teaching note includes practice exercises, answers, and a supporting Excel workbook for students.

The purpose of this note is to illustrate how to use cell references in Microsoft Excel. References are necessary when using functions and building models. This teaching note provides students with practice exercises and a supporting Excel workbook.

The purpose of this note is to illustrate the use of good modelling practices in Microsoft Excel. An effective spreadsheet model can be a valuable decision-making tool for businesses wanting to improve or expand their operations. To do so, a model must provide information relevant to the decision maker.

The purpose of this note is to introduce the concept of logical statements in Microsoft Excel and to illustrate the use of Excel’s built-in logic functions. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.

The purpose of this note is to illustrate the use of Microsoft Excel functions for generating descriptive statistics for continuous data. These functions are common when analyzing data to provide insights for decision making. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.

Student spreadsheet for case W16417.

Student spreadsheet for case W16415.

Student spreadsheet for case W16419.

Student spreadsheet for case W16413.

Student spreadsheet for case W16411.

The purpose of this note is to illustrate the use of Microsoft Excel functions for generating descriptive statistics for continuous data. These functions are common when analyzing data to provide insights for decision making. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.

Student spreadsheet for case W16406.

The purpose of this note is to illustrate the use of Microsoft Excel's Goal Seek feature. The technical note includes practice exercises and their solutions, and a supporting Excel workbook for students.

The newly appointed director of Surgical Business Analytics has been charged with the task of improving predictions of surgical case volume at Vanderbilt University Medical Center in Nashville, Tennessee. He is provided with 48 weeks of elective surgery schedule data that give the number of surgeries booked on specific dates prior to the surgery day and the actual number performed. Variation in daily operating room volumes is a major problem because of the mismatch in timing between when the staff schedules are made and when the final demand is known (usually the day before). This uncertainty creates staffing challenges for all support and ancillary services, including nurses, orderlies, anesthesiologists, the recovery room, pathology, radiology and the sterile cart centre. Can he develop a method to improve surgical case volume prediction that is actionable in a managerially useful time frame? How can he engage and approach stakeholders who may often consider "analytics," "data analysis" and "computations" as a black hole?

The newly appointed director of Surgical Business Analytics has been charged with the task of improving predictions of surgical case volume at Vanderbilt University Medical Center in Nashville, Tennessee. He is provided with 48 weeks of elective surgery schedule data that give the number of surgeries booked on specific dates prior to the surgery day and the actual number performed. Variation in daily operating room volumes is a major problem because of the mismatch in timing between when the staff schedules are made and when the final demand is known (usually the day before). This uncertainty creates staffing challenges for all support and ancillary services, including nurses, orderlies, anesthesiologists, the recovery room, pathology, radiology and the sterile cart centre. Can he develop a method to improve surgical case volume prediction that is actionable in a managerially useful time frame? How can he engage and approach stakeholders who may often consider "analytics," "data analysis" and "computations" as a black hole?