remaining variance

USAGE SUMMARY

The phrase "remaining variance" is correct and usable in written English.
It is typically used in statistical contexts to refer to the portion of variance that is left after accounting for certain factors or variables. Example: "After analyzing the data, we found that the remaining variance could be attributed to external influences not included in our model."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The remaining variance in AO (residual AO) is highly heritable but remains unexplained.

Human Genetics

The factor 2 explains the greatest of the remaining variance and so forth.

Applied Water Science

Factor 2 represents low eigenvalue and explains the greatest of the remaining variance and so forth.

Applied Water Science

The remaining variance could be explained by unique non-shared environmental influences for life satisfaction independent of personality.

Journal of Research in Personality

The remaining variance explained by the random slopes was only 3% in M2c, 4% in M3b and 9% in M1c.

New Zealand Journal of Forestry Science

In order to estimate the variance explained by the predictors, the additional variance explained by the added student-level or school-level variables was computed using this formula: (remaining variance without added predictors-remaining variance with added predictors)/remaining variance without added predictors.

Large-scale Assessments in Education

Further analysis indicated that only approximately 4% of the remaining variance can be explained by stable characteristics of the individual or site.

Journal of Sound and Vibration

Further genetic factors were needed to explain the remaining variance in IQ with a small component of unique environmental variance present.

Intelligence

The last functions only explained 2.1%% of the remaining variance in the variable sets after the extraction of the prior functions.

Journal of Innovation and Entrepreneurship

The second principal component is then constrained to be orthogonal to the first principal component while still capturing the most remaining variance.

Cognitive Research: Principles and Implications

This is to be expected because these factors are extracted successively, each one accounting for as much of the remaining variance as possible.

Applied Water Science

Expert writing Tips

Best practice

When using "remaining variance", clearly define what factors have already been accounted for to provide context for what constitutes the remaining portion.

Common error

Avoid using "remaining variance" without first clarifying which sources of variance have already been considered. This omission can lead to confusion and ambiguity in your analysis.

Linguistic Context

The phrase "remaining variance" functions as a noun phrase typically used in statistical and scientific contexts. It refers to the portion of the total variance in a dataset that is not explained by a particular model or set of variables, as Ludwig AI confirms.

Expression frequency: Very common

Frequent in

Science

98%

Formal & Business

1%

News & Media

1%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "remaining variance" is a commonly used term, particularly within scientific and statistical fields, to denote the portion of unexplained variability within a dataset. As Ludwig AI indicates, the phrase is grammatically correct and most frequently encountered in scientific literature. Related phrases, such as "unexplained variance" or "residual variance", can offer alternative ways to express this concept. When using "remaining variance", ensure that you clarify what factors have already been accounted for to avoid ambiguity and enhance clarity. Remember, while the phrase is valid, context is everything.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

unexplained variance

Focuses on the lack of explanation for the variance.

residual variance

Emphasizes the variance that remains after a model has been applied.

leftover variance

A more informal way to describe the remaining variance.

unaccounted-for variance

Highlights that the variance hasn't been accounted for.

outstanding variance

Implies the variance is still present and needs addressing.

portion of variance not explained

More verbose, but clearly states what the variance represents.

variance component remaining

Technical term, highlighting the component aspect.

variance not captured by the model

Focuses on the model's inability to explain the variance.

the balance of variance

Describes what constitutes the remaining variance.

variance yet to be explained

Implies a future effort will be made to explain variance.

FAQs

How is "remaining variance" typically calculated in statistical models?

The "remaining variance" is usually calculated by subtracting the variance explained by the model's predictors from the total variance in the data. This difference represents the unexplained or residual variance.

What factors typically contribute to the "remaining variance" in research studies?

Common contributors to "remaining variance" include measurement error, unmeasured variables, individual differences, and random noise. These factors can influence outcomes but are not explicitly accounted for in the model.

What is the difference between "residual variance" and "remaining variance"?

While "residual variance" and "remaining variance" are often used interchangeably, "residual variance" specifically refers to the variance left over after a statistical model has been fitted. "Remaining variance" is a broader term that can refer to variance left after any form of explanation or accounting.

In principal component analysis (PCA), how does each component relate to the "remaining variance"?

In PCA, the first principal component explains the largest amount of variance. Subsequent components are orthogonal to the first and each other, and they explain the maximum amount of the "remaining variance" not explained by the preceding components.