Search results

The learning outcomes are as follows: How to establish credibility of data sources; measurement scales of data; the importance of descriptive statistics and generating the following based on the type of data: mean, median and standard deviation; graphical methods; and test for differences: t-test and analysis of variance.

The case is set during the COVID-19 pandemic and the South African Government’s response to the pandemic. A brief timeline is provided as part of the introduction to the case study, with the following being a timeline of the events: 14 March 2020, 114 South African citizens were repatriated from Wuhan the epicentre of the COVID-19 outbreak; 15 March 2020, South Africa’s President, Cyril Ramaphosa declares a National State of Disaster, and this includes various measures to protect against the spread of COVID-19, while the health-care system is geared up to deal with the pandemic. Among the measures implemented, travel bans from high-risk countries and closing of air-traffic, closing of land ports and banning of gatherings of more than 100 people; 23 March 2020, President Cyril Ramaphosa announced a national lockdown beginning on 27 March 2020 for three weeks; 9 April 2020, President Ramaphosa extends the national lockdown by a further two weeks. The World Health Organisation (WHO) had commended South Africa on the swift action taken to curb the spread of the virus. Individuals and organisational leaders are grappling to make sense of the spread of the virus, and the barrage of the information that is being communicated through multiple channels, formal and informal. To make sense of the information, the case is premised on getting access to the raw data and conducting the analysis based on the publicly available data. The central requirement of the case is to compare the number of positive cases per million, based on the population data contained in the data set, of South Africa to a comparable country.

Post-graduate students learning statistics as part of a degree programme. The case assumes no prior statistics knowledge and therefore is aimed at teaching the importance of the basics of statistical analysis and then progressing to tests for differences.

CSS 7: Management Science

Teaching Notes are available for educators only.

This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study.

This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies’ success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing.

Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment.

This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers’ understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes.

Greg Mills describes his search for the perfect engagement ring which includes an analysis of the prices of 6,000 diamonds. An engineer, Greg hopes to impress Sarah Staggers by using regression to find an underpriced diamond. Students are asked to either select one of the 6,000 diamonds or provide point forecasts for prices of 3,142 diamonds in a hold-out sample. The instructor can use the actual prices of the held-out diamonds to evaluate student pricing models.