missing values given

USAGE SUMMARY

The phrase "missing values given" is correct and usable in written English.
It can be used in contexts related to data analysis, statistics, or programming when discussing the presence of missing data points in a dataset. Example: "In our analysis, we need to account for the missing values given in the dataset to ensure accurate results."

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

6 human-written examples

We used Bayesian networks -- a probabilistic graphical model -- that performs inferences to predict the missing values given the observed data and the dependency relationships between the variables.

Harvard University

Under this approach, a random sample is taken from the original data with replacement and the model is applied to predict (impute) the original missing values, given the observed data [32].

Plosone

We did not replace missing values, given that the attrition rate was modest.

British Medical Journal

During the imputation step, each missing value is replaced with multiple (m >1) imputed values drawn from a predictive distribution for the missing values given the observed data.

BMC Medical Research Methodology

We use Bayesian MI to "fill in" the missing values which draws from the posterior predictive distribution of the missing values given the observed data (for details and underlying assumptions, see (Little and Rubin 2002; Van Buuren 2012)).

International Journal of Bipolar Disorders

The MCMC algorithm used here is a two-step iterative process that begins by imputing plausible values for the missing values given the observed values in order to generate a complete data set [9].

Plosone

Human-verified similar examples from authoritative sources

Similar Expressions

54 human-written examples

The complementary analyses with imputation for missing values gave support to this assumption.

BMC Psychiatry

Prior to imputation, we removed all genes having more than 80% missing values, giving an expression matrix with 6653 clones.

BMC Genomics

Missing values were given the value "0" (null), provided that information was given on at least two of the three variables.

BMC Public Health

The numbers of missing values were given in 'no data' categories.

BMC Cancer

Furthermore, the on-line questionnaires are divided in several sections and a warning against missing values is given to the respondent every time a new section is submitted.

BMC Pediatrics

Expert writing Tips

Best practice

When reporting analyses involving missing data, clearly state the methods used to handle the "missing values given", such as imputation or exclusion, to ensure transparency and reproducibility.

Common error

Avoid ignoring "missing values given" in your dataset. Always address how these missing values might impact your results and justify your chosen method for handling them.

Linguistic Context

The phrase "missing values given" functions as a descriptive term within data analysis and statistics, referring to the presence of incomplete data points in a dataset. Ludwig AI highlights its usage in describing methodologies for dealing with incomplete datasets.

Expression frequency: Uncommon

Frequent in

Science

71%

Academia

29%

News & Media

0%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "missing values given" is a term used to describe datasets with incomplete information, commonly encountered in scientific and academic research. Ludwig AI analysis confirms that the phrase is grammatically correct, primarily used in formal and scientific contexts. When dealing with "missing values given", it's crucial to clearly document your chosen handling method (imputation, deletion, etc.) to ensure the transparency and reproducibility of your analysis. Common errors include ignoring the potential impact of missing data on your results, so always justify your approach and be aware of potential biases. By acknowledging and properly addressing "missing values given", you can improve the reliability and validity of your findings.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

imputation of missing data

Focuses on the act of filling in the missing values rather than acknowledging them as given.

missing data imputation

Inverts the word order of "imputation of missing data".

handling missing data

Emphasizes the process of managing or dealing with the absence of certain values.

treatment of missing values

Similar to 'handling missing data' but may imply a more specific or technical approach.

presence of missing values

Highlights the existence of missing information within the dataset, a prerequisite before any action.

existence of missing data

Highlights the existence of missing data within the dataset, a prerequisite before any action.

accounting for missing values

Focuses on explaining the impact of existing null data.

considering missing values

Highlights the act of including missing data in the evaluations.

addressing missing values

Highlights the act of resolving the situation of the missing data.

dealing with missing entries

Focuses on the actions that must be taken to resolve missing data.

FAQs

How should I handle "missing values given" in my dataset?

Handling "missing values given" depends on the nature of your data and the goals of your analysis. Common methods include imputation (replacing missing values with estimated values), deletion (removing cases with missing values), or using statistical methods that can accommodate missing data. Document your chosen approach and justify it based on your data's characteristics.

What does it mean to impute "missing values given"?

Imputation means replacing "missing values given" with estimated values based on other available data. This can be done using various techniques, such as mean imputation (replacing missing values with the average of the available values), regression imputation (predicting missing values based on a regression model), or multiple imputation (creating multiple plausible datasets with different imputed values). See also: "imputation of missing data".

How do I decide whether to impute or delete "missing values given"?

The decision to impute or delete "missing values given" depends on the amount and pattern of missing data. If a small percentage of data is missing completely at random, deletion might be acceptable. However, if data is missing not at random or a substantial amount is missing, imputation is generally preferred to avoid bias and loss of statistical power.

What are some alternatives to saying "missing values given"?

You can use alternatives such as "provided missing values", "supplied missing values", or simply "missing data" depending on the specific context. The key is to clearly communicate the presence of incomplete data in your analysis. It's also possible to consider "handling missing data" and "treatment of missing values".