principal components

USAGE SUMMARY

The phrase "principal components" is correct and usable in written English.
It is typically used in statistical analysis and data science to refer to the main variables that explain the most variance in a dataset. Example: "In our analysis, we identified the principal components that significantly influenced the outcome of the experiment."

✓ Grammatically correct

Science

Encyclopedias

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The principal components of seawater are listed in the table.

Encyclopedia Britannica

Principal components.

Applied Water Science

Generate the principal components.

EURASIP Journal on Wireless Communications and Networking

Each area has its own principal components.

EURASIP Journal on Image and Video Processing

The three principal components in Leptospermum (L).

Chemistry Central Journal

a Principal components analysis of bacterial communities.

AMB Express

Keep the first p - 1 principal components.

EURASIP Journal on Advances in Signal Processing

Figure 2 Examples of different principal components.

EURASIP Journal on Advances in Signal Processing

Principal components of image voxel intensities.

EJNMMI Physics

This led us to principal components.

Transportation Research Part C: Emerging Technologies

Principal components analysis yielded four interpretable components.

NeuroToxicology

Expert writing Tips

Best practice

When using "principal components", ensure you clearly define the data being analyzed and the specific method (e.g., principal component analysis or PCA) used to derive these components.

Common error

Avoid assuming that the first few "principal components" capture all the relevant information. Always assess the cumulative variance explained to determine if additional components are needed for a comprehensive understanding.

Linguistic Context

The phrase "principal components" functions primarily as a noun phrase, often used as a subject or object in sentences describing statistical or analytical processes. It denotes the key, underlying variables derived from a larger dataset through techniques like Principal Component Analysis (PCA), as shown in Ludwig's examples.

Expression frequency: Very common

Frequent in

Science

75%

Encyclopedias

10%

News & Media

5%

Less common in

Formal & Business

3%

Wiki

2%

Reference

1%

Ludwig's WRAP-UP

In summary, "principal components" is a noun phrase widely used in scientific and technical contexts to denote the most crucial variables explaining variance in a dataset. As confirmed by Ludwig, the phrase is grammatically correct and commonly employed in statistical analysis, particularly within Principal Component Analysis (PCA). Its function is to simplify complex data, facilitating informed decision-making. While alternatives like "major constituents" or "primary factors" exist, "principal components" remains the standard term in technical discourse. Therefore, ensure clarity in defining the method and interpreting the variance explained to avoid misinterpretations.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

major constituents

Focuses on the composition aspect, highlighting key elements forming a whole.

primary factors

Emphasizes influence or importance in determining an outcome or result.

leading variables

Highlights the variables with the highest impact or explanatory power.

key elements

Identifies fundamental parts that are essential for understanding or function.

main determinants

Stresses the elements that crucially affect or decide an outcome.

chief ingredients

Focuses on essential components that make up a mixture or system.

core aspects

Emphasizes the most central or fundamental parts of a subject.

critical features

Highlights key characteristics that are crucial or decisive.

fundamental attributes

Stresses the basic qualities or characteristics.

essential parameters

Focuses on the vital measurements or quantities that define a system.

FAQs

How are "principal components" used in data analysis?

"Principal components" are used to reduce the dimensionality of data by identifying the most important variables that explain the variance in the dataset. This helps in simplifying complex data and extracting meaningful insights.

What does it mean to perform a principal component analysis (PCA)?

Performing a principal component analysis (PCA) involves transforming a dataset into a new set of variables, the "principal components", which are uncorrelated and ordered by the amount of variance they explain. The goal is to reduce the number of variables while retaining the most important information.

How do I interpret the "principal components" resulting from PCA?

Each "principal component" is a linear combination of the original variables. Interpreting them involves understanding which original variables contribute most to each component and what that combination represents in the context of your data. Loadings can help determine the importance of each variable.

What are some alternatives to using "principal components" analysis?

Alternatives to principal component analysis include techniques like factor analysis, independent component analysis (ICA), and nonlinear dimensionality reduction methods. The choice depends on the specific goals and characteristics of your data. Other possible alternatives in different contexts are "major constituents" or "primary factors".