Uday Jha, PhD
Full Time Lecturer
Decision & Information Sciences
Contact
508-999-8350
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Charlton College of Business 206
Education
2017 | Rochester Institute of Technology, Rochester, NY | MS Applied Statistics |
2012 | ICFAI University, Tripura, India | Master Aviation Management |
2007 | ICFAI University, Tripura, India | MS Mathematics |
Teaching
- Introduction of Business Analytics
- Business Statistics
- Aircraft Performance
Teaching
Courses
Examines both descriptive and inferential statistics as applied to business. Topics include graphical and tabular methods of data presentation, probability theory and distributions, hypothesis testing, analysis of variance, regression and forecasting. Emphasis is placed on concepts, applications, and the proper use of statistics to collect, analyze, and interpret data. Throughout this course students will use computer software to perform statistical analyses. Students will learn how to make decisions using facts and the techniques of data analysis. Students will also use the internet to supplement classroom learning.
Examines both descriptive and inferential statistics as applied to business. Topics include graphical and tabular methods of data presentation, probability theory and distributions, hypothesis testing, analysis of variance, regression and forecasting. Emphasis is placed on concepts, applications, and the proper use of statistics to collect, analyze, and interpret data. Throughout this course students will use computer software to perform statistical analyses. Students will learn how to make decisions using facts and the techniques of data analysis. Students will also use the internet to supplement classroom learning.
Data analytics to describe, predict, advise decision-making, & improve business performance. The student will learn how to analyze business problems using a quantitative decision-making approach. This course focuses on methods, descriptive/predictive models for decision-making, & possible actions that would profit from analysis & results examined in a business context. This course is required of all undergraduate business majors.
Data analytics to describe, predict, advise decision-making, & improve business performance. The student will learn how to analyze business problems using a quantitative decision-making approach. This course focuses on methods, descriptive/predictive models for decision-making, & possible actions that would profit from analysis & results examined in a business context. This course is required of all undergraduate business majors.
Teaching
Online and Continuing Education Courses
Examines both descriptive and inferential statistics as applied to business. Topics include graphical and tabular methods of data presentation, probability theory and distributions, hypothesis testing, analysis of variance, regression and forecasting. Emphasis is placed on concepts, applications, and the proper use of statistics to collect, analyze, and interpret data. Throughout this course students will use computer software to perform statistical analyses. Students will learn how to make decisions using facts and the techniques of data analysis. Students will also use the internet to supplement classroom learning.
Register for this course.
A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regression-correlation analysis.
Register for this course.
A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regression-correlation analysis.
Register for this course.
Research
Research Interests
- High Dimensional Multicollinear Datasets
- Fluctuation in International Crude Prices
Select publications
- Uday Kant Jha, Peter Bajorski, Ernest Fokoue, Jan van Aardt, Grant Anderson
(2017).
Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset
Open Journal of Statis