Technology is advancing at a rapid rate, changing various aspects of our everyday lives. Businesses and governments are trying to adapt to this change by embracing positive aspects, while remaining attentive to negative aspects. This note focuses on technologies such as artificial intelligence, machine learning, and big data, considering the ethical issues that these technologies create within our society and also how these ethical issues are being addressed.
Technology is advancing at a rapid rate, changing various aspects of our everyday lives. Businesses and governments are trying to adapt to this change by embracing positive aspects, while remaining attentive to negative aspects. This note focuses on technologies such as artificial intelligence, machine learning, and big data, considering the ethical issues that these technologies create within our society and also how these ethical issues are being addressed.
Survival analysis refers to a collection of statistical techniques used to analyze the occurrence and expected timing of events. Survival analysis can provide not only the probability of an event of interest occurring, but also the time at which the event is likely to occur. A particularly useful application of survival analysis is in the area of "customer churn"-the loss of clients or customers-which is a major concern in many industries. This note introduces survival analysis and discusses two approaches to examine customer churn: the Kaplan-Meier estimator and the Cox proportional hazards model. Instructions are provided on how to build these models in Microsoft Excel, without add-ins.
Survival analysis refers to a collection of statistical techniques used to analyze the occurrence and expected timing of events. Survival analysis can provide not only the probability of an event of interest occurring, but also the time at which the event is likely to occur. A particularly useful application of survival analysis is in the area of "customer churn"—the loss of clients or customers—which is a major concern in many industries. This note introduces survival analysis and discusses two approaches to examine customer churn: the Kaplan-Meier estimator and the Cox proportional hazards model. Instructions are provided on how to build these models in Microsoft Excel, without add-ins.
Linear regression is a common tool used in statistics and is considered the foundation for most predictive analytics. It creates a line of best fit in a data set and uses that line to explain the relationship between two quantities, which helps forecast future values. This note provides a high-level, non-technical overview of linear regression.
Linear regression is a common tool used in statistics and is considered the foundation for most predictive analytics. It creates a line of best fit in a data set and uses that line to explain the relationship between two quantities, which helps forecast future values. This note provides a high-level, non-technical overview of linear regression.
In November 2014, the founder and chief executive officer of HomeZilla, in Toronto, Canada, was considering how to provide value-added services to his business. One of the company’s main services was to work with real estate agents to provide web listings to attract home shoppers. Many Internet companies were analyzing web-browsing data in an effort to better understand user behaviour and thus improve their business. Inspired by this trend, the founder considered how his company could use web-browsing data to better attract home shoppers on the company’s website and thereby add value to his business.
In November 2014, the founder and chief executive officer of HomeZilla, in Toronto, Canada, was considering how to provide value-added services to his business. One of the company's main services was to work with real estate agents to provide web listings to attract home shoppers. Many Internet companies were analyzing web-browsing data in an effort to better understand user behaviour and thus improve their business. Inspired by this trend, the founder considered how his company could use web-browsing data to better attract home shoppers on the company's website and thereby add value to his business.
The purpose of this note is to illustrate the use of Data Tables in Microsoft Excel. Data Tables are commonly used for performing sensitivity analyses, sometimes called "What If?" analyses, and for preparing data for graphs. Data Tables can also be used to enable a Monte Carlo simulation in a spreadsheet in more advanced models. The technical note includes practice exercises and their solutions, and a supporting Excel workbook for students.
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.
The purpose of this technical note is to introduce and illustrate the use of Microsoft Excel’s built-in advanced logic functions. Excel’s advanced logic functions add logical capabilities to normal descriptive statistics functions. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.
The purpose of this note is to introduce students to the basic use and vocabulary of Microsoft Excel. Excel is commonly used to do quantitative analysis in business. Students will encounter Excel applications in just about every area of business, including finance, accounting, operations management, marketing, and analytics.
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.
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.
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 Data Tables in Microsoft Excel. Data Tables are commonly used for performing sensitivity analyses, sometimes called “What If?” analyses, and for preparing data for graphs. Data Tables can also be used to enable a Monte Carlo simulation in a spreadsheet in more advanced models. The technical note includes practice exercises and their solutions, and a supporting Excel workbook for students.