jointly significant

USAGE SUMMARY

The phrase "jointly significant" is correct and usable in written English.
You can use it to describe something that has both individual and collective importance. For example, "The recent election was jointly significant because it had a major impact on both the national and local levels."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Note that although the variables PRODUCTBUND and INDICATN have unexpected signs and individually they may not always reach statistical significance, they are jointly significant in the presence of the other explanatory variables in model C.

Health Economics Review

Multinomial attributes coded as sets of dummy variables were retained or excluded on the basis of joint significance, with each level of a jointly significant set of dummies retained regardless of individual significance.

Cost Effectiveness and Resource Allocation

When additional sociodemographics variables are added, risk game choices are jointly significant for most specifications with marginal joint significance showing when education is controlled for (column 3).

BMC Health Services Research

Moreover, while in the top tercile, we note a negative effect in panel C, the test of significance of the LE and LA estimates is not jointly significant.

IZA Journal of Development and Migration

The evidence shows that these instruments were not jointly significant in the main equation for migration flows at the conventional significance levels.

SERIEs

The cutoff points are jointly significant at 1% level.

IZA Journal of Labor Economics

However, unlike whites, β (t − y),g are jointly significant for both black men and women.

IZA Journal of Labor Economics

All individual characteristics are jointly significant, which is consistent with the results of our primary analysis.

Health Economics Review

The coefficients are jointly significant in all equations and adjusted (R^{2}) is approximately 23%%.

SERIEs

Moreover, the test for weak instruments reveals that they are jointly significant in the probit specification.

SERIEs

Dummy variables of industry fixed effect are jointly significant, indicating that individual-specific industry experiences matter for entrepreneurship.

IZA Journal of Labor Economics

Expert writing Tips

Best practice

When reporting statistical findings, clearly state the test used (e.g., F-test, Wald test) to determine that the variables are "jointly significant". This adds credibility and allows readers to verify your results.

Common error

Avoid the mistake of assuming that because a group of variables is "jointly significant", each individual variable within that group is also significant. Individual variables may not reach statistical significance on their own but contribute to the overall significance when considered together.

Linguistic Context

The phrase "jointly significant" functions as an adjective phrase modifying a noun (e.g., variables, coefficients, effects). It is used to describe a group of variables or parameters that, when considered together, demonstrate statistical significance, even if individual components might not. Ludwig confirms this usage.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "jointly significant" is a statistically valid and "correct" term, as confirmed by Ludwig. It's used to describe a set of variables that, when considered together, have a statistically significant effect, even if individual variables aren't significant on their own. Predominantly found in scientific and academic contexts, it's crucial to specify the statistical test used and avoid assuming individual significance from joint significance. As Ludwig's examples show, it provides a clear and concise way to communicate that combined factors are important in understanding a particular outcome.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

collectively important

Focuses on the group's overall importance rather than individual contributions.

combined significance

Highlights the significance when considering the elements together.

overall relevance

Emphasizes the total relevance of a set of factors or variables.

aggregate importance

Stresses the importance of the whole when individual parts are combined.

group effect

Describes the impact caused by a group of variables or factors.

cumulative impact

Indicates the total effect of multiple elements over time.

combined effect

Highlights that something significant is achieved when things are working together.

shared significance

Emphasizes the significance is because of their interaction.

mutual relevance

Indicates that the items are relevant to each other and as a whole.

correlated importance

Highlights the importance of things that are related each other.

FAQs

How is "jointly significant" used in statistical analysis?

In statistical analysis, "jointly significant" indicates that a group of variables, when considered together, has a statistically significant impact on the outcome, even if individual variables within the group may not be significant on their own. It is often assessed using tests like the F-test or Wald test.

What does it mean if variables are not "jointly significant"?

If variables are not "jointly significant", it suggests that the group of variables, as a whole, does not have a statistically significant impact on the outcome being studied. This might mean that the variables are irrelevant or that their combined effect is negligible.

When should I use "jointly significant" instead of "individually significant"?

Use "jointly significant" when you want to emphasize that the combined effect of multiple variables is important, even if the individual effects are not. Use "individually significant" when you want to highlight that each variable, on its own, has a statistically significant impact. Sometimes, you might find alternatives like "statistically significant" to describe the effects.

What are some statistical tests used to determine if variables are "jointly significant"?

Common statistical tests used to determine if variables are "jointly significant" include the F-test, the Wald test, and likelihood ratio tests. These tests assess whether the group of variables, as a whole, contributes significantly to the model's explanatory power. The Chi-squared test is also related to this concept.