Bootstrap standard error and confidence intervals for the correlations corrected for indirect range restriction

2010 ◽  
Vol 64 (3) ◽  
pp. 367-387 ◽  
Author(s):  
Johnson Ching-hong Li ◽  
Wai Chan ◽  
Ying Cui
Keyword(s):  
Confidence Intervals ◽  
Standard Error ◽  
Range Restriction

Questions to the Article of the Infection Rate of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China: a Combined Analysis of Population Samples (Preprint)

2020 ◽  
Author(s):  
CHIEN WEI
Keyword(s):  
Confidence Intervals ◽  
Infection Rate ◽  
Standard Error ◽  
Recent Article ◽  
Forest Plot ◽  
Combined Analysis ◽  
Infected People ◽  
Heterogeneity Test

UNSTRUCTURED The recent article published on July 22 in 2020 remains several questionable issues that are required to clarifications further, particularly for readers who hope to replicate Figure 1 from the data in Table 1. Although I reproduced a similar forest plot based on the effect ratios and their 95% confidence intervals(Cis) similar to Figure 1 in that article, no detailed information about the source of standard error(SE) for each country was seen and addressed. Others like the positive 95% Cis reflecting the negative Z values in the forest plot and the Q statistics used for examining the heterogeneity test are requied to interpretations and classifications. Most importantly, authors did not explain how to estimate the number of infected people in Wuhan, China, to be 143,000 ,significantly higher than the number of confirmed cases(=75,815 in Wuhan, China) that is required to provide the equations or methodologies in an article.

scholarly journals Assessment of decrease in renal function associated with hypothyroidism

2021 ◽  
Vol 12 (1) ◽  
pp. 275-286
Author(s):  
Ayesha Ammar ◽  
Kahkashan Bashir Mir ◽  
Sadaf Batool ◽  
Noreen Marwat ◽  
Maryam Saeed ◽  
...  
Keyword(s):  
Renal Function ◽  
Confidence Interval ◽  
Creatinine Clearance ◽  
Confidence Intervals ◽  
Standard Error ◽  
Radioactive Iodine ◽  
T Test ◽  
Euthyroid State ◽  
Significant Difference ◽  
The Mean

Objective: Study was aimed to see the effects of hypothyroidism on GFR as a renal function. Material and methods: Total of Fifty-eight patients were included in the study. Out of those forty-eight patients were female and the rest were male. Out of fifty eight patients, fifty three patients were of thyroid cancer in which hypothyroidism was due to discontinuation of thyroxine before the administration of radioactive iodine for Differentiated thyroid cancer.Moreover, remaining five patients were post radioactive iodine treatment (for hyperthyroidism) hypothyroid. All of the patients were above eighteen years of age with TSH value > 30µIU/ml. Pregnant and lactating females were excluded.Renal function tests (urea/creatinine, creatinine clearance) and serum electrolytes followed by Tc-99m-DTPA renal scan for GFR assessment (GATES’ method) were carried out in all subjects twice during the study, One study during hypothyroid state (TSH > 30 µIU/ml) and other during euthyroid state (TSH between 0.4 to 4µ IU/ml). The results of Student’s t-test showed significant difference in renal functions (Urea, creatinine, creatinine clearance, GFR values) in euthyroid state and hypothyroid state (p-value <0.05). RESULTS: In case of creatinine the paired t test reveal the mean 1.014±0.428, with standard error of 0.669 within 95% confidence interval, for creatinine clearance 80.11±14.12 with standard error of 1.94 within 95% confidence intervals, for urea the mean 28±12.13 with standard error of 1.607 within 95% confidence intervals and for GFR for individual kidney is 38.056±8.56 with standard error of 1.3717 within 95% confidence interval. There was no difference in the outcome of the 2 groups. Conclusion: Hypothyroidism impairs renal function to a significant level and hence needs to be prevented and corrected as early as possible.

scholarly journals Uncertainty Measures for Economics Journal Impact Factors

2013 ◽  
Vol 51 (1) ◽  
pp. 173-189 ◽  
Author(s):  
David I Stern
Keyword(s):  
Impact Factor ◽  
Confidence Intervals ◽  
Standard Error ◽  
Impact Factors ◽  
Elite Group ◽  
Economics Journal ◽  
Uncertainty Measures ◽  
Economics Journals ◽  
The Impact ◽  
Journal Impact

Academic economists appear to be intensely interested in rankings of journals, institutions, and individuals. Yet there is little discussion of the uncertainty associated with these rankings. To illustrate the uncertainty associated with citations-based rankings, I compute the standard error of the impact factor for all economics journals with a five-year impact factor in the 2011 Journal Citations Report. I use these to derive confidence intervals for the impact factors as well as ranges of possible rank for a subset of thirty journals. I find that the impact factors of the top two journals are well defined and set these journals apart in a clearly defined group. An elite group of 9–11 mainstream journals can also be fairly reliably distinguished. The four bottom ranked journals are also fairly clearly set apart. For the remainder of the distribution, confidence intervals overlap and rankings are quite uncertain. (JEL A14)

Characteristics of statistical parameters used to interpret least-squares results.

1978 ◽  
Vol 24 (4) ◽  
pp. 611-620 ◽  
Author(s):  
R B Davis ◽  
J E Thompson ◽  
H L Pardue
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Confidence Intervals ◽  
Standard Error ◽  
Statistical Parameters ◽  
Average Value ◽  
Data Points ◽  
Independent Variable ◽  
Highly Correlated

Abstract This paper discusses properties of several statistical parameters that are useful in judging the quality of least-squares fits of experimental data and in interpreting least-squares results. The presentation includes simplified equations that emphasize similarities and dissimilarities among the standard error of estimate, the standard deviations of slopes and intercepts, the correlation coefficient, and the degree of correlation between the least-squares slope and intercept. The equations are used to illustrate dependencies of these parameters upon experimentally controlled variables such as the number of data points and the range and average value of the independent variable. Results are interpreted in terms of which parameters are most useful for different kinds of applications. The paper also includes a discussion of joint confidence intervals that should be used when slopes and intercepts are highly correlated and presents equations that can be used to judge the degree of correlation between these coefficients and to compute the elliptical joint confidence intervals. The parabolic confidence intervals for calibration cures are also discussed briefly.

Physiological Correlates to In-race Paratriathlon Cycling Performance

2020 ◽  
Vol 41 (08) ◽  
pp. 539-544 ◽  
Author(s):  
Ben Thomas Stephenson ◽  
Alex Shill ◽  
John Lenton ◽  
Victoria Goosey-Tolfrey
Keyword(s):  
Confidence Intervals ◽  
Standard Error ◽  
Lactate Threshold ◽  
Aerobic Power ◽  
Correlation Coefficients ◽  
Cycling Performance ◽  
Maximal Aerobic Power ◽  
Cycle Performance ◽  
Category Specificity ◽  
Wheelchair Users

AbstractThe purpose was to determine the physiological correlates to cycling performance within a competitive paratriathlon. Five wheelchair user and ten ambulant paratriathletes undertook laboratory-based testing to determine their: peak rate of oxygen uptake; blood lactate- and ventilatory-derived physiological thresholds; and, their maximal aerobic power. These variables were subsequently expressed in absolute (l∙min −1 or W), relative (ml∙kg−1∙min −1 or W∙kg −1) and scaled relative (or ml∙kg − 0.82 ∙min −1, ml∙kg − 0.32 ∙min −1 or W∙kg −0.32) terms. All athletes undertook a paratriathlon race with 20 km cycle. Pearson’s correlation test and linear regression analyses were produced between laboratory-derived variables and cycle performance to generate correlation coefficients (r), standard error of estimates and 95% confidence intervals. For wheelchair users, performance was most strongly correlated to relative aerobic lactate threshold (W∙kg −1) (r=−0.99; confidence intervals: −0.99 to −0.99; standard error of estimate=22 s). For ambulant paratriathletes, the greatest correlation was with maximal aerobic power (W∙kg −0.32) (r=−0.91; −0.99 to −0.69; standard error of estimate=88 s). Race-category-specificity exits regarding physiological correlates to cycling performance in a paratriathlon race with further differences between optimal scaling factors between paratriathletes. This suggests aerobic lactate threshold and maximal aerobic power are the pertinent variables to infer cycling performance for wheelchair users and ambulant paratriathletes, respectively.

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