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Related Concept Videos

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Estimating the average need of semantic knowledge from distributional semantic models.

Geoff Hollis1

  • 1Department of Psychology, University of Alberta, P217 Biological Sciences Building, Edmonton, AB, T6G 2E9, Canada. hollis@ualberta.ca.

Memory & Cognition
|July 15, 2017
PubMed
Summary

Continuous Bag of Words (CBOW) is a psychologically plausible model for word meaning. This word embedding model aligns with memory theories and explains lexical access beyond word frequency.

Keywords:
CBOWContextual diversityLikely needNeeds probabilityRational analysisSkip-gramWord frequencyaverage need

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Area of Science:

  • Cognitive Psychology
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Continuous Bag of Words (CBOW) and skip-gram models advance lexical semantics.
  • These models outperform previous methods in capturing semantic relatedness.
  • Psychological implications of these models remain under-explored.

Purpose of the Study:

  • To explore the psychological plausibility of CBOW and skip-gram models.
  • To connect CBOW's learning algorithms with Anderson's rational theory of memory.
  • To investigate the role of word frequency and contextual diversity in lexical access.

Main Methods:

  • Relating CBOW learning algorithms to Anderson's rational theory of memory.
  • Analyzing CBOW's ability to predict lexical access measures.
  • Comparing CBOW's performance against established semantic models like latent semantic analysis.

Main Results:

  • CBOW aligns with Anderson's concept of needs probability.
  • CBOW accounts for significant variation in lexical access measures.
  • Word frequency and contextual diversity are linked to memory retrieval, not learning.

Conclusions:

  • CBOW is a psychologically plausible model for lexical semantics.
  • The findings challenge traditional views of word frequency and contextual diversity.
  • This research bridges computational models of language with cognitive memory theories.