scholarly journals CONFIDENCE LEVELS FOR THE SAMPLE MEAN AND STANDARD DEVIATION OF A RAYLEIGH PROCESS

1964 ◽  
Author(s):  
Leo M. Keane
Keyword(s):  
Standard Deviation ◽  
Sample Mean ◽  
Confidence Levels ◽  
Rayleigh Process

scholarly journals Phonological Awareness and Print Knowledge of Preschool Children With Cochlear Implants

2012 ◽  
Vol 55 (3) ◽  
pp. 811-823 ◽  
Author(s):  
Sophie E. Ambrose ◽  
Marc E. Fey ◽  
Laurie S. Eisenberg
Keyword(s):  
Standard Deviation ◽  
Speech Perception ◽  
Phonological Awareness ◽  
Speech Production ◽  
Cochlear Implants ◽  
Preschool Age ◽  
Speech And Language ◽  
Sample Mean ◽  
Print Knowledge ◽  
Age Appropriate

PurposeTo determine whether preschool-age children with cochlear implants have age-appropriate phonological awareness and print knowledge and to examine the relationships of these skills with related speech and language abilities.MethodThe sample comprised 24 children with cochlear implants (CIs) and 23 peers with normal hearing (NH), ages 36 to 60 months. Children's print knowledge, phonological awareness, language, speech production, and speech perception abilities were assessed.ResultsFor phonological awareness, the CI group's mean score fell within one standard deviation of the Test of Preschool Early Literacy's (Lonigan, Wagner, Torgesen, & Rashotte, 2007) normative sample mean but was more than one standard deviation below the NH group mean. The CI group's performance did not differ significantly from that of the NH group for print knowledge. For the CI group, phonological awareness and print knowledge were significantly correlated with language, speech production, and speech perception. Together these predictor variables accounted for 34% of variance in the CI group's phonological awareness but no significant variance in their print knowledge.ConclusionsChildren with CIs have the potential to develop age-appropriate early literacy skills by preschool age but are likely to lag behind their NH peers in phonological awareness. Intervention programs serving these children should target these skills with instruction and by facilitating speech and language development.

The Content of IMF Staff Reports for Euro Area Countries

2014 ◽  
pp. 272-292
Author(s):  
Lena Golubovskaja
Keyword(s):  
Standard Deviation ◽  
Euro Area ◽  
Mean Value ◽  
Systematic Bias ◽  
Executive Board ◽  
Sample Mean ◽  
Warning Tone ◽  
The Mean ◽  
Run Up ◽  
The Imf

This chapter analyzes the tone and information content of the two external policy reports of the Internal Monetary Fund (IMF), the IMF Article IV Staff Reports, and Executive Board Assessments for Euro area countries. In particular, the researchers create a tone measure denoted WARNING based on the existing DICTION 5.0 Hardship dictionary. This study finds that in the run-up to the current credit crises, average WARNING tone levels of Staff Reports for Slovenia, Luxembourg, Greece, and Malta are one standard deviation above the EMU sample mean; and for Spain and Belgium, they are one standard deviation below the mean value. Furthermore, on average for Staff Reports over the period 2005-2007, there are insignificant differences between the EMU sample mean and Staff Reports’ yearly averages. Researchers find the presence of a significantly increased level of WARNING tone in 2006 (compared to the previous year) for the IMF Article IV Staff Reports. There is also a systematic bias of WARNING scores for Executive Board Assessments versus WARNING scores for the Staff Reports.

scholarly journals Attention to Diversity in Compulsory Secondary Education

2020 ◽  
Vol 10 (4) ◽  
pp. 1176-1185
Author(s):  
Elia Vázquez Varela ◽  
Iago Portela Pino ◽  
Víctor Domínguez Rodríguez
Keyword(s):  
Secondary Education ◽  
Standard Deviation ◽  
Educational System ◽  
Descriptive Study ◽  
Point Of View ◽  
Quantitative Methodology ◽  
Sample Mean ◽  
Frequency Of Use ◽  
Attention To Diversity ◽  
Didactic Planning

The attention to pupil diversity is still considered one of the main issues to solve in the current educational system. The objective of the study was to establish and analyze the differences between the knowledge and the use of ordinary and extraordinary measures in the attention to diversity from the point of view of compulsory secondary education teachers. A descriptive study was performed with a quantitative methodology, making use of a 452-teacher sample (Mean: 47, Standard Deviation: 8.42) using a survey. The results show a better understanding and frequency of use of the ordinary measures compared to the extraordinary ones. Results also maintain that among the most used we found the adequacy of the didactic planning as well as the support of the therapeutic pedagogy and language and auditory specialist. Moreover, the analysis reveals that the CMOEAD (ordinary and extraordinary measures for the attention to diversity) survey possesses psychometric properties which support its use in further studies. In conclusion, a better knowledge and use of ordinary and extraordinary measures in attention to diversity enables a forward leap in the quality and equity for pupils.

scholarly journals Multipatterns of the National Weather Radar Testbed Mitigate Clutter Received via Sidelobes

2011 ◽  
Vol 28 (3) ◽  
pp. 401-409 ◽  
Author(s):  
Guifu Zhang ◽  
Yinguang Li ◽  
Richard J. Doviak ◽  
Dave Priegnitz ◽  
John Carter ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Phased Array ◽  
Weather Radar ◽  
Main Lobe ◽  
Mean Power ◽  
Sample Mean ◽  
Ground Clutter ◽  
Phased Array Radar ◽  
Weather Observations

Abstract The phased-array radar (PAR) of the National Weather Radar Testbed (NWRT) has a unique hybrid (mechanical and electrical) azimuth scan capability, allowing weather observations with different antenna patterns. Observations show the standard deviation of the sample mean power of weather echoes received through the main lobe of a set of squinted beams is less than the clutter received via sidelobes. This then allows use of a multipattern technique to cancel sidelobe echoes from moving scatterers, echoes that cannot be filtered with a ground-clutter canceler. Although the multipattern technique was developed to cancel clutter received through sidelobes, results show clutter from objects moving within the beam can also be canceled.

scholarly journals Properties of the Standard Deviation that are Rarely Mentioned in Classrooms

2016 ◽  
Vol 38 (3) ◽  
Author(s):  
Mohammad Fraiwan Al-Saleh ◽  
Adil Eltayeb Yousif
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Sample Variance ◽  
Mean Absolute Deviation ◽  
Hidden Information ◽  
Absolute Deviation ◽  
Sample Mean ◽  
The Mean ◽  
Vague Concept ◽  
Chebyshev's Inequality

Unlike the mean, the standard deviation ¾ is a vague concept. In this paper, several properties of ¾ are highlighted. These properties include the minimum and the maximum of ¾, its relationship to the mean absolute deviation and the range of the data, its role in Chebyshev’s inequality and the coefficient of variation. The hidden information in the formula itself is extracted. The confusion about the denominator of the sample variance being n ¡ 1 is also addressed. Some properties of the sample mean and varianceof normal data are carefully explained. Pointing out these and other properties in classrooms may have significant effects on the understanding and the retention of the concept.

scholarly journals Determining a One-Tailed Upper Limit for Future Sample Relative Reproducibility Standard Deviations

2006 ◽  
Vol 89 (3) ◽  
pp. 797-803
Author(s):  
Foster D McClure ◽  
Jung K Lee
Keyword(s):  
Standard Deviation ◽  
Linear Combination ◽  
Square Root ◽  
Sample Mean ◽  
Upper Limit ◽  
Reproducibility Standard Deviation ◽  
The Future ◽  
Standard Deviations ◽  
Future Sample

Abstract A formula was developed to determine a one-tailed 100p% upper limit for future sample percent relative reproducibility standard deviations <inline-formula> <inline-graphic href="inline_eq1.gif"/> </inline-formula> , where sR is the sample reproducibility standard deviation, which is the square root of a linear combination of the sample repeatability variance ( <inline-formula> <inline-graphic href="inline_eq2.gif"/> </inline-formula> ) plus the sample laboratory-to-laboratory variance ( <inline-formula> <inline-graphic href="inline_eq3.gif"/> </inline-formula> ), i.e., <inline-formula> <inline-graphic href="inline_eq4.gif"/> </inline-formula> , and y is the sample mean. The future RSDR, % is expected to arise from a population of potential RSDR, % values whose true mean is <inline-formula> <inline-graphic href="inline_eq5.gif"/> </inline-formula> , where R and are the population reproducibility standard deviation and mean, respectively.

Distribution Theory for Glass's Estimator of Effect size and Related Estimators

1981 ◽  
Vol 6 (2) ◽  
pp. 107-128 ◽  
Author(s):  
Larry V. Hedges
Keyword(s):  
Standard Deviation ◽  
Effect Size ◽  
Minimum Variance ◽  
Exact Distribution ◽  
Small Sample ◽  
Weighted Estimate ◽  
Sample Standard Deviation ◽  
Sample Mean ◽  
Minimum Variance Unbiased Estimator ◽  
Sample Bias

Glass's estimator of effect size, the sample mean difference divided by the sample standard deviation, is studied in the context of an explicit statistical model. The exact distribution of Glass's estimator is obtained and the estimator is shown to have a small sample bias. The minimum variance unbiased estimator is obtained and shown to have uniformly smaller variance than Glass's (biased) estimator. Measurement error is shown to attenuate estimates of effect size and a correction is given. The effects of measurement invalidity are discussed. Expressions for weights that yield the most precise weighted estimate of effect size are also derived.

Properties of random variables

2019 ◽  
pp. 60-87
Author(s):  
M. D. Edge
Keyword(s):  
Standard Deviation ◽  
Central Limit Theorem ◽  
Limit Theorem ◽  
Normal Distribution ◽  
Linear Relationship ◽  
Random Variables ◽  
Square Root ◽  
Sample Mean ◽  
The Central Limit Theorem ◽  
Large Numbers

In this chapter, the behavior of random variables is summarized using the concepts of expectation, variance, and covariance. The expectation is a measurement of the location of a random variable’s distribution. The variance and its square root, the standard deviation, are measurements of the spread of a random variable’s distribution. Covariance and correlation are measurements of the extent of linear relationship between two random variables. The chapter also describe two important theorems that describe the distribution of means of samples from a distribution. As the sample size becomes larger, the distribution of the sample mean becomes bunched more tightly around the expectation—this is the law of large numbers—and the distribution of the sample mean approaches the shape of a normal distribution—this is the central limit theorem. Finally, a model describing a linear relationship between two random variables is considered, and the properties of those two random variables are analyzed.

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