partitioning analysis

USAGE SUMMARY

The phrase "partitioning analysis" is correct and usable in written English.
It can be used in contexts related to data analysis, computer science, or any field where dividing data into parts for examination is relevant. Example: "The partitioning analysis revealed distinct patterns in the dataset that were previously unnoticed."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Then, the effluent gas mixture was collected for chromatographic partitioning analysis.

Journal of Hydrology

Variation partitioning analysis showed that landscape configuration explained 48% of the variation in water quality.

Ecological Indicators

The heat input is estimated by applying Loewen and Shaw's heat partitioning analysis.

Journal of Manufacturing Processes

Here, we report the development of an automated robotic screening that enables high-throughput cell partitioning analysis in ATPS.

Journal of Chromatography A

Using recursive partitioning analysis for finding optimal cut points for miRNA expression, we examined time to first therapy.

Plosone

Applying partitioning analysis for each site separately revealed that the within-transect diversity component in Changuu was significantly lower than the null expectation.

Plosone

We discuss how ANCEA, coupled with population partitioning analysis, can greatly facilitate the systematic dissection of transcriptional networks that underlie mammalian patterning.

Plosone

In our previous work [19] we describe a method, called population partitioning analysis (PPA), to systematically identify the active nodes of a system.

Plosone

To determine whether multicollinearity amongst predictor variables had influenced our results, we used a third statistical approach – hierarchical partitioning analysis [42], [43], on R. Hierarchical partitioning analysis was performed on means for each of the 30 sites because it cannot incorporate random factors.

Plosone

Variance partitioning analysis on the bacterial and fungal data sets.

AoB PLANTS

RPA, recursive partitioning analysis, GPA, graded partitioning analysis, P1PS, phase 1 prognostic score, HR, hazard ratio, CI, confidence interval.

BMC Cancer

Expert writing Tips

Best practice

When describing the methodology of a study, use "partitioning analysis" to clearly indicate the process of dividing data for detailed examination. For instance, in ecological studies, specify "variance partitioning analysis" to show how environmental factors influence species distribution.

Common error

Avoid using "partitioning analysis" without specifying the type of partitioning performed (e.g., variance, recursive). Being specific ensures clarity and prevents confusion about the analytical method used.

Linguistic Context

The phrase "partitioning analysis" functions as a noun phrase, typically used as a subject or object in a sentence. Ludwig AI indicates it is grammatically correct and usable. It describes a method of dividing data or problems into smaller parts for examination.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

In summary, "partitioning analysis" is a grammatically correct noun phrase predominantly used in scientific and academic contexts. Ludwig AI confirms its validity and usability. It serves to describe a method for dividing data or problems into smaller parts for detailed examination. While less common in general or informal contexts, its precision and clarity make it essential in research methodologies. Related terms include "segmentation analysis" and "cluster analysis", but the best choice depends on the specific analytical approach used. The key to using "partitioning analysis" effectively is to specify the type of partitioning to avoid ambiguity.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

segmentation analysis

Focuses on dividing a dataset or population into distinct segments.

cluster analysis

Emphasizes grouping data points based on similarity.

decomposition analysis

Highlights breaking down a complex problem into simpler parts.

variance partitioning

Specifically relates to dividing variance in statistical models.

divisional analysis

Highlights the act of dividing into divisions for analysis.

stratification analysis

Emphasizes the layering or stratification of data.

classification analysis

Focuses on categorizing data into predefined classes.

factor analysis

Identifies underlying factors that explain correlations among variables.

tree partitioning

Applies a decision tree structure to divide data.

data segmentation

Highlights the act of segmenting data for analytical purposes.

FAQs

How is "partitioning analysis" used in research?

"Partitioning analysis" is used to divide data into meaningful subgroups for detailed examination. For example, variance partitioning is used to determine the proportion of variance in a dependent variable that can be attributed to different independent variables.

What is the difference between "segmentation analysis" and "partitioning analysis"?

"Segmentation analysis" often refers to dividing a market or customer base into distinct groups, while "partitioning analysis" is a more general term that applies to dividing data or a problem into smaller, more manageable parts for analysis. Partitioning can be used across many different areas and disciplines.

When should I use "recursive partitioning analysis"?

Use "recursive partitioning analysis" when you want to create a decision tree to predict the value of a categorical or continuous variable. It's especially useful when there are complex interactions between predictor variables.

What are some common applications of "variance partitioning analysis"?

"Variance partitioning analysis" is commonly used in ecology to assess the relative importance of environmental and spatial factors on species distribution, and in genetics to determine the genetic and environmental contributions to phenotypic variation.