Professor Ron Kenett is Chairman of the KPA Group, Israel, Senior Research Fellow at the Neaman Institute, Technion, Haifa and Visiting Professor at the Institute for Drug Research at the School of Medicine of the Hebrew University of Jerusalem, Israel. He is an applied statistician combining expertise in academic, consulting and business domains. Ron is Past President of the Israel Statistical Association (ISA) and of the European Network for Business and Industrial Statistics (ENBIS). He authored and co-authored over 250 papers and 14 books on topics such as biostatistics, healthcare, industrial statistics, customer surveys, multivariate quality control, risk management and information quality. The KPA Group he founded in 1994, is a leading Israeli firm focused on generating insights through analytics. He is editor in chief of Wiley’s StatsRef, serves on the editorial board of several international journals and was awarded the 2013 Greenfield Medal by the Royal Statistical Society and, in 2018, the Box Medal by the European Network for Business and Industrial Statistics. He founded the point and click translator company, and is member of the board of several startup companies. Ron holds a BSc in Mathematics (with first class honors) from Imperial College, London University and a PhD in Mathematics from the Weizmann Institute of Science, Rehovot, Israel. See also

Lecture Series in Analytics
A series of hand’s on lectures using the JMP platform to cover: Information quality, the real work of data science, Decision trees, Regression trees, Random forests, The non performing loans (NPL) case study, Logistic regression, Naïve Bayes, K-means clustering and Text analytics. Students will be asked to evaluate the information quality of one of three case studies and analyze the German credit data to develop a predictive model for nonpaying loans.

Lecture Series in Causality
A focused workshop on causality covering the following topics: Background on causality in science and statistics, Fishbone cause and effect diagrams, Bayesian networks, Randomization in experimental designs, Propensity scores in observational studies, Counterfactuals and do calculus, Personalized medicine, condition based maintenance and Industry 4.0 and Future research areas.

Statistics at a Crossroad
The premise to this seminar is the sense that Statistics is at a crossroad between a path leading to a driver’s seat position in the analytic and scientific world, as Cox writes, a Grammar for Research, and a path where statistics is pushed back to an obscure corner mostly of academic interest. Statistical aspects of reproducibility in research, statistical inference using p-values, confidence intervals and Bayes factors will be discussed along the work of Deborah Mayo on severe testing and various methods for generalizing research claims.

Lecture Series in Analytics (Sala Laurea) [Materials : Kenett – Analytics]
22/01 10.30-13.30
23/01 9.30-12.30
24/01 10.30-13.30

Lecture Series in Causality (Sala Laurea) [Materials : Kenett – Causality ]
28/01 9.30-12.30
29/01 10.30-13.30

Seminar on ‘Statistics at a Crossroad’ [Materials : Kenett – Seminar]
10.45-11.45 Via Santa Sofia, 9 – aula M203

List of references 

Kenett, R. and Zacks, S. (2021) Modern Industrial Statistics: With Applications in R, MINITAB, and JMP, 3rd Edition, Wiley, UK,

Kenett, R. and Redman, T. (2019) The Real Work of Data Science: Turning data into information, better decisions, and stronger organizations, Wiley, UK,

Kenett, R. and Redman, T. (2021) La data science: ruolo e utilità, translated by Silvia Salini and Giancarlo Manzi, Publisher: Giappichelli, Turin.

Kenett, R. and Shmueli, G. (2016) Information Quality: The Potential of Data and Analytics to Generate Knowledge, Wiley,

Shmueli, G., Bruce, P., Stephens, M. and Patel, N. (2016) Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, Wiley, USA

Provost, F. and Fawcett, T. (2013) Data Science for Business, OReilly, USA

James, G.,  Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning : with Applications in R, Springer, Germany