Squares of Non-Standard-Normal or Non-Student's-t1 RVs Which Have Chi-Square1 or F1,1 Distributions: A Return Visit
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605519676 | ||
| 003 | CHVBK | ||
| 005 | 20210128100731.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11009-014-9425-4 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11009-014-9425-4 | ||
| 100 | 1 | |a Mukhopadhyay |D Nitis |u Department of Statistics, University of Connecticut, 06269-4120, Storrs, CT, USA |4 aut | |
| 245 | 1 | 0 | |a Squares of Non-Standard-Normal or Non-Student's-t1 RVs Which Have Chi-Square1 or F1,1 Distributions: A Return Visit |h [Elektronische Daten] |c [Nitis Mukhopadhyay] |
| 520 | 3 | |a In some quarters, especially beginners learning statistics and probability, one generally accepts a claim such as a χ 1 2 ${\chi _{1}^{2}}$ random variable (rv) must be the square of a standard normal rv or a F 1,1 rv must be the square of a Student's t 1 rv. This feeds into more misconceptions later. Hence, we begin with a brief but general construct and then illustrate a number of rvs explicitly which are drastically different from a standard normal or Student's t 1 rv whose squares are distributed as χ 1 2 $\chi _{1}^{2}$ or F 1,1 respectively. This simply presented note reinforces basic understanding of lessons in such core topics covered in classrooms for both undergraduate and graduate students. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Asymmetric distributions |2 nationallicence | |
| 690 | 7 | |a Cauchy distribution |2 nationallicence | |
| 690 | 7 | |a Laplace distribution |2 nationallicence | |
| 690 | 7 | |a Skewed distributions |2 nationallicence | |
| 690 | 7 | |a Spiky distributions |2 nationallicence | |
| 690 | 7 | |a Wavy distributions |2 nationallicence | |
| 690 | 7 | |a Weibull distribution |2 nationallicence | |
| 773 | 0 | |t Methodology and Computing in Applied Probability |d Springer US; http://www.springer-ny.com |g 17/3(2015-09-01), 817-822 |x 1387-5841 |q 17:3<817 |1 2015 |2 17 |o 11009 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11009-014-9425-4 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11009-014-9425-4 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a Mukhopadhyay |D Nitis |u Department of Statistics, University of Connecticut, 06269-4120, Storrs, CT, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Methodology and Computing in Applied Probability |d Springer US; http://www.springer-ny.com |g 17/3(2015-09-01), 817-822 |x 1387-5841 |q 17:3<817 |1 2015 |2 17 |o 11009 | ||