Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Confidence Testing | 2 |
| Intervals | 2 |
| Mathematical Concepts | 2 |
| Benchmarking | 1 |
| Business Administration… | 1 |
| Computation | 1 |
| Computer Graphics | 1 |
| Equations (Mathematics) | 1 |
| Error Patterns | 1 |
| Error of Measurement | 1 |
| Focus Groups | 1 |
| More ▼ | |
Author
| Arzumanyan, George | 1 |
| Halcoussis, Dennis | 1 |
| Phillips, G. Michael | 1 |
| Williams, Immanuel James | 1 |
| Williams, Kelley Kim | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Descriptive | 2 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkWilliams, Immanuel James; Williams, Kelley Kim – Teaching Statistics: An International Journal for Teachers, 2018
Many students find understanding confidence intervals difficult, especially because of the amalgamation of concepts such as confidence levels, standard error, point estimates and sample sizes. An R Shiny application was created to assist the learning process of confidence intervals using graphics and data from the US National Basketball…
Descriptors: Learning Processes, Intervals, Computer Graphics, Confidence Testing
Download full textArzumanyan, George; Halcoussis, Dennis; Phillips, G. Michael – American Journal of Business Education, 2015
This paper presents the Agresti & Coull "Adjusted Wald" method for computing confidence intervals and margins of error for common proportion estimates. The presented method is easily implementable by business students and practitioners and provides more accurate estimates of proportions particularly in extreme samples and small…
Descriptors: Business Administration Education, Error of Measurement, Error Patterns, Intervals
