correlate

• v.

• 相関する、関連する

関

associate、association、attach、bearing、connection、correlation、implication、link、pertinent、reference、relate、relation、relationship、relative、relevance、relevant

　 　 　 　

WordNet

1. to bear a reciprocal or mutual relation; "Do these facts correlate?"
2. either of two or more related or complementary variables (同)correlative
3. bring into a mutual, complementary, or reciprocal relation; "I cannot correlate these two pieces of information"
4. the act of consorting with or joining with others; "you cannot be convicted of criminal guilt by association"
5. (chemistry) any process of combination (especially in solution) that depends on relatively weak chemical bonding
6. a relation resulting from interaction or dependence; "flints were found in association with the prehistoric remains of the bear"; "the host is not always injured by association with a parasite"
7. the state of being connected together as in memory or imagination; "his association of his father with being beaten was too strong to break"
8. the process of bringing ideas or events together in memory or imagination; "conditioning is a form of learning by association" (同)connection, connexion
9. (ecology) a group of organisms (plants and animals) that live together in a certain geographical region and constitute a community with a few dominant species
10. a formal organization of people or groups of people; "he joined the Modern Language Association"
11. having partial rights and privileges or subordinate status; "an associate member"; "an associate professor"
12. a person who joins with others in some activity or endeavor; "he had to consult his associate before continuing"
13. a person with subordinate membership in a society, institution, or commercial enterprise; "associates in the law firm bill at a lower rate than do partners"
14. any event that usually accompanies or is closely connected with another; "first was the lightning and then its thunderous associate"
15. make a logical or causal connection; "I cannot connect these two pieces of evidence in my mind"; "colligate these facts"; "I cannot relate these events at all" (同)tie_in, relate, link, colligate, link up, connect
16. refer to; "he referenced his colleagues work" (同)cite
17. the act of referring or consulting; "reference to an encyclopedia produced the answer" (同)consultation
18. the most direct or specific meaning of a word or expression; the class of objects that an expression refers to; "the extension of `satellite of Mars is the set containing only Demos and Phobos" (同)denotation, extension
19. a publication (or a passage from a publication) that is referred to; "he carried an armful of references back to his desk"; "he spent hours looking for the source of that quotation" (同)source
20. the relation between a word or phrase and the object or idea it refers to; "he argued that reference is a consequence of conditioned reflexes"
21. a relation between things or events (as in the case of one causing the other or sharing features with it); "there was a connection between eating that pickle and having that nightmare" (同)connexion, connectedness
22. a connecting shape (同)connexion, link
23. the state of being connected; "the connection between church and state is inescapable" (同)link, connectedness
24. shifting from one form of transportation to another; "the plane was late and he missed his connection in Atlanta" (同)connexion
25. an instrumentality that connects; "he soldered the connection"; "he didnt have the right connector between the amplifier and the speakers" (同)connexion, connector, connecter, connective
26. (usually plural) a person who is influential and to whom you are connected in some way (as by family or friendship); "he has powerful connections"
27. a supplier (especially of narcotics)
28. the means of connection between things linked in series (同)nexus
29. a unit of length equal to 1/100 of a chain
30. an interconnecting circuit between two or more locations for the purpose of transmitting and receiving data (同)data link
31. a fastener that serves to join or connect; "the walls are held together with metal links placed in the wet mortar during construction" (同)linkup, tie, tie-in
32. (computing) an instruction that connects one part of a program or an element on a list to another program or list
33. estimated by comparison; not absolute or complete; "a relative stranger" (同)comparative
34. an animal or plant that bears a relationship to another (as related by common descent or by membership in the same genus) (同)congener, congenator, congeneric
35. a person related by blood or marriage; "police are searching for relatives of the deceased"; "he has distant relations back in New Jersey" (同)relation
36. not varying together

PrepTutorEJDIC

1. …‘に'相互関係を持たせる;…'を'(…と)関連させる《+『名』+『with』+『名』》 / 相互に関連がある;(…と)関連がある《+『with』+『名』》 / 相互関係にあるもの
2. 〈U〉(…との)『交際』,付き合い《+『with』+『名』》 / 〈U〉(…との)『連合』,『共同』,関連《+『with』+『名』》 / 〈C〉組合,協会,会社 / 〈U〉連想;〈C〉《しばしば複数形で》連想されるもの
3. 〈人〉'を'(…の)『仲間に加える』,(…に)連合させる《+『名』〈人〉+『with』+『名』》 / (…と)…'を'結びつけて考える(起こす)《+『名』+『with』+『名』》 / (…と)『仲間になる』,交際する《+『with』+『名』》 / (…と)合体する,提携する《+『with』+『名』》 / 『仲間』,『同僚』;組合員 / 準会員 / 付属物;連想されるもの / 連合した;仲間の,同僚の / 補助の,準…
4. 〈U〉(…へ)『言及すること』,(…の)話に触れること《+『to』+『名』》;〈C〉言及した事柄 / 〈U〉(…を)『参照すること』,参考《+『to』+『名』》;〈C〉(本などの)出典,参考書;(出典などの)注記 / 〈C〉(人物・才能などの)証明書;身元保証人,照会先
5. (鎖の)『輪』,環 / 鎖のようにつながったソーセージの一節 / (…と)つなぐ物(人),(…との)きずな,つながり《+『with』(『to』)+『名』》 / 《複数形で》=cuff links / …‘を'『つなぎ合わせる』,連結する;(…と)…‘を'つなぐ《+『名』+『with』(『to』)+『名』》 / (…と)結合する,つながる《+[『up』(『together』)]『with』+『名』》
6. たいまつ
7. 『関係のある』,関連した / 『比較上の』,相対的の / 相互の,相関的な / (文法で)関係を表す / 親類,親戚 / 関係詞,(特に)関係代名詞

Wikipedia preview

出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2015/09/03 09:11:45」(JST)

wiki en

[Wiki en表示]

This article is about correlation and dependence in statistical data. For other uses, see correlation (disambiguation).

In statistics, dependence is any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price.

Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling; however, statistical dependence is not sufficient to demonstrate the presence of such a causal relationship (i.e., correlation does not imply causation).

Formally, dependence refers to any situation in which random variables do not satisfy a mathematical condition of probabilistic independence. In loose usage, correlation can refer to any departure of two or more random variables from independence, but technically it refers to any of several more specialized types of relationship between mean values. There are several correlation coefficients, often denoted ρ or r, measuring the degree of correlation. The most common of these is the Pearson correlation coefficient, which is sensitive only to a linear relationship between two variables (which may exist even if one is a nonlinear function of the other). Other correlation coefficients have been developed to be more robust than the Pearson correlation – that is, more sensitive to nonlinear relationships. Mutual information can also be applied to measure dependence between two variables.

Several sets of (x, y) points, with the Pearson correlation coefficient of x and y for each set. Note that the correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case the correlation coefficient is undefined because the variance of Y is zero.

Pearson's product-moment coefficient

Main article: Pearson product-moment correlation coefficient

The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient, or "Pearson's correlation coefficient", commonly called simply "the correlation coefficient". It is obtained by dividing the covariance of the two variables by the product of their standard deviations. Karl Pearson developed the coefficient from a similar but slightly different idea by Francis Galton.

The population correlation coefficient ρ_X,Y between two random variables X and Y with expected values μ_X and μ_Y and standard deviations σ_X and σ_Y is defined as:

where E is the expected value operator, cov means covariance, and corr is a widely used alternative notation for the correlation coefficient.

The Pearson correlation is defined only if both of the standard deviations are finite and nonzero. It is a corollary of the Cauchy–Schwarz inequality that the correlation cannot exceed 1 in absolute value. The correlation coefficient is symmetric: corr(X,Y) = corr(Y,X).

The Pearson correlation is +1 in the case of a perfect direct (increasing) linear relationship (correlation), −1 in the case of a perfect decreasing (inverse) linear relationship (anticorrelation), and some value between −1 and 1 in all other cases, indicating the degree of linear dependence between the variables. As it approaches zero there is less of a relationship (closer to uncorrelated). The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables.

If the variables are independent, Pearson's correlation coefficient is 0, but the converse is not true because the correlation coefficient detects only linear dependencies between two variables. For example, suppose the random variable X is symmetrically distributed about zero, and Y = X². Then Y is completely determined by X, so that X and Y are perfectly dependent, but their correlation is zero; they are uncorrelated. However, in the special case when X and Y are jointly normal, uncorrelatedness is equivalent to independence.

If we have a series of n measurements of X and Y written as x_i and y_i where i = 1, 2, ..., n, then the sample correlation coefficient can be used to estimate the population Pearson correlation r between X and Y. The sample correlation coefficient is written

$r_{xy}=\frac{\sum\limits_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})}{(n-1) s_x s_y} =\frac{\sum\limits_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})} {\sqrt{\sum\limits_{i=1}^n (x_i-\bar{x})^2 \sum\limits_{i=1}^n (y_i-\bar{y})^2}},$

where x and y are the sample means of X and Y, and s_x and s_y are the sample standard deviations of X and Y.

This can also be written as:

$r_{xy}=\frac{\sum x_iy_i-n \bar{x} \bar{y}}{(n-1) s_x s_y}=\frac{n\sum x_iy_i-\sum x_i\sum y_i} {\sqrt{n\sum x_i^2-(\sum x_i)^2}~\sqrt{n\sum y_i^2-(\sum y_i)^2}}.$

If x and y are results of measurements that contain measurement error, the realistic limits on the correlation coefficient are not −1 to +1 but a smaller range.

For the case of a linear model with a single independent variable, the coefficient of determination (R squared) is the square of r, Pearson's product-moment coefficient .

Rank correlation coefficients

Main articles: Spearman's rank correlation coefficient and Kendall tau rank correlation coefficient

Rank correlation coefficients, such as Spearman's rank correlation coefficient and Kendall's rank correlation coefficient (τ) measure the extent to which, as one variable increases, the other variable tends to increase, without requiring that increase to be represented by a linear relationship. If, as the one variable increases, the other decreases, the rank correlation coefficients will be negative. It is common to regard these rank correlation coefficients as alternatives to Pearson's coefficient, used either to reduce the amount of calculation or to make the coefficient less sensitive to non-normality in distributions. However, this view has little mathematical basis, as rank correlation coefficients measure a different type of relationship than the Pearson product-moment correlation coefficient, and are best seen as measures of a different type of association, rather than as alternative measure of the population correlation coefficient.

To illustrate the nature of rank correlation, and its difference from linear correlation, consider the following four pairs of numbers (x, y):

(0, 1), (10, 100), (101, 500), (102, 2000).

As we go from each pair to the next pair x increases, and so does y. This relationship is perfect, in the sense that an increase in x is always accompanied by an increase in y. This means that we have a perfect rank correlation, and both Spearman's and Kendall's correlation coefficients are 1, whereas in this example Pearson product-moment correlation coefficient is 0.7544, indicating that the points are far from lying on a straight line. In the same way if y always decreases when x increases, the rank correlation coefficients will be −1, while the Pearson product-moment correlation coefficient may or may not be close to −1, depending on how close the points are to a straight line. Although in the extreme cases of perfect rank correlation the two coefficients are both equal (being both +1 or both −1) this is not in general so, and values of the two coefficients cannot meaningfully be compared. For example, for the three pairs (1, 1) (2, 3) (3, 2) Spearman's coefficient is 1/2, while Kendall's coefficient is 1/3.

Other measures of dependence among random variables

The information given by a correlation coefficient is not enough to define the dependence structure between random variables. The correlation coefficient completely defines the dependence structure only in very particular cases, for example when the distribution is a multivariate normal distribution. (See diagram above.) In the case of elliptical distributions it characterizes the (hyper-)ellipses of equal density, however, it does not completely characterize the dependence structure (for example, a multivariate t-distribution's degrees of freedom determine the level of tail dependence).

Distance correlation and Brownian covariance / Brownian correlation were introduced to address the deficiency of Pearson's correlation that it can be zero for dependent random variables; zero distance correlation and zero Brownian correlation imply independence.

The Randomized Dependence Coefficient is a computationally efficient, copula-based measure of dependence between multivariate random variables. RDC is invariant with respect to non-linear scalings of random variables, is capable of discovering a wide range of functional association patterns and takes value zero at independence.

The correlation ratio is able to detect almost any functional dependency, and the entropy-based mutual information, total correlation and dual total correlation are capable of detecting even more general dependencies. These are sometimes referred to as multi-moment correlation measures, in comparison to those that consider only second moment (pairwise or quadratic) dependence.

The polychoric correlation is another correlation applied to ordinal data that aims to estimate the correlation between theorised latent variables.

One way to capture a more complete view of dependence structure is to consider a copula between them.

The coefficient of determination generalizes the correlation coefficient for relationships beyond simple linear regression.

Sensitivity to the data distribution

The degree of dependence between variables X and Y does not depend on the scale on which the variables are expressed. That is, if we are analyzing the relationship between X and Y, most correlation measures are unaffected by transforming X to a + bX and Y to c + dY, where a, b, c, and d are constants (b and d being positive). This is true of some correlation statistics as well as their population analogues. Some correlation statistics, such as the rank correlation coefficient, are also invariant to monotone transformations of the marginal distributions of X and/or Y.

Pearson/Spearman correlation coefficients between X and Y are shown when the two variables' ranges are unrestricted, and when the range of X is restricted to the interval (0,1).

Most correlation measures are sensitive to the manner in which X and Y are sampled. Dependencies tend to be stronger if viewed over a wider range of values. Thus, if we consider the correlation coefficient between the heights of fathers and their sons over all adult males, and compare it to the same correlation coefficient calculated when the fathers are selected to be between 165 cm and 170 cm in height, the correlation will be weaker in the latter case. Several techniques have been developed that attempt to correct for range restriction in one or both variables, and are commonly used in meta-analysis; the most common are Thorndike's case II and case III equations.

Various correlation measures in use may be undefined for certain joint distributions of X and Y. For example, the Pearson correlation coefficient is defined in terms of moments, and hence will be undefined if the moments are undefined. Measures of dependence based on quantiles are always defined. Sample-based statistics intended to estimate population measures of dependence may or may not have desirable statistical properties such as being unbiased, or asymptotically consistent, based on the spatial structure of the population from which the data were sampled.

Sensitivity to the data distribution can be used to an advantage. For example, scaled correlation is designed to use the sensitivity to the range in order to pick out correlations between fast components of time series. By reducing the range of values in a controlled manner, the correlations on long time scale are filtered out and only the correlations on short time scales are revealed.

Correlation matrices

The correlation matrix of n random variables X₁, ..., X_n is the n  ×  n matrix whose i,j entry is corr(X_i, X_j). If the measures of correlation used are product-moment coefficients, the correlation matrix is the same as the covariance matrix of the standardized random variables X_i / σ (X_i) for i = 1, ..., n. This applies to both the matrix of population correlations (in which case "σ" is the population standard deviation), and to the matrix of sample correlations (in which case "σ" denotes the sample standard deviation). Consequently, each is necessarily a positive-semidefinite matrix.

The correlation matrix is symmetric because the correlation between X_i and X_j is the same as the correlation between X_j and X_i.

Common misconceptions

Correlation and causality

Main article: Correlation does not imply causation

See also: Normally distributed and uncorrelated does not imply independent

The conventional dictum that "correlation does not imply causation" means that correlation cannot be used to infer a causal relationship between the variables. This dictum should not be taken to mean that correlations cannot indicate the potential existence of causal relations. However, the causes underlying the correlation, if any, may be indirect and unknown, and high correlations also overlap with identity relations (tautologies), where no causal process exists. Consequently, establishing a correlation between two variables is not a sufficient condition to establish a causal relationship (in either direction).

A correlation between age and height in children is fairly causally transparent, but a correlation between mood and health in people is less so. Does improved mood lead to improved health, or does good health lead to good mood, or both? Or does some other factor underlie both? In other words, a correlation can be taken as evidence for a possible causal relationship, but cannot indicate what the causal relationship, if any, might be.

Correlation and linearity

Four sets of data with the same correlation of 0.816

The Pearson correlation coefficient indicates the strength of a linear relationship between two variables, but its value generally does not completely characterize their relationship. In particular, if the conditional mean of Y given X, denoted E(Y|X), is not linear in X, the correlation coefficient will not fully determine the form of E(Y|X).

The image on the right shows scatterplots of Anscombe's quartet, a set of four different pairs of variables created by Francis Anscombe. The four y variables have the same mean (7.5), variance (4.12), correlation (0.816) and regression line (y = 3 + 0.5x). However, as can be seen on the plots, the distribution of the variables is very different. The first one (top left) seems to be distributed normally, and corresponds to what one would expect when considering two variables correlated and following the assumption of normality. The second one (top right) is not distributed normally; while an obvious relationship between the two variables can be observed, it is not linear. In this case the Pearson correlation coefficient does not indicate that there is an exact functional relationship: only the extent to which that relationship can be approximated by a linear relationship. In the third case (bottom left), the linear relationship is perfect, except for one outlier which exerts enough influence to lower the correlation coefficient from 1 to 0.816. Finally, the fourth example (bottom right) shows another example when one outlier is enough to produce a high correlation coefficient, even though the relationship between the two variables is not linear.

These examples indicate that the correlation coefficient, as a summary statistic, cannot replace visual examination of the data. Note that the examples are sometimes said to demonstrate that the Pearson correlation assumes that the data follow a normal distribution, but this is not correct.

Bivariate normal distribution

If a pair (X, Y) of random variables follows a bivariate normal distribution, the conditional mean E(X|Y) is a linear function of Y, and the conditional mean E(Y|X) is a linear function of X. The correlation coefficient r between X and Y, along with the marginal means and variances of X and Y, determines this linear relationship:

$E(Y\mid X) = E(Y) + r\sigma_y\frac{X-E(X)}{\sigma_x},$

where E(X) and E(Y) are the expected values of X and Y, respectively, and σ_x and σ_y are the standard deviations of X and Y, respectively.

Partial correlation

Main article: Partial correlation

If a population or data-set is characterized by more than two variables, a partial correlation coefficient measures the strength of dependence between a pair of variables that is not accounted for by the way in which they both change in response to variations in a selected subset of the other variables.

See also

• Association (statistics)
• Autocorrelation
• Canonical correlation
• Coefficient of determination
• Cointegration
• Concordance correlation coefficient
• Cophenetic correlation
• Copula
• Correlation function
• Covariance and correlation
• Cross-correlation
• Ecological correlation
• Fraction of variance unexplained
• Genetic correlation
• Goodman and Kruskal's lambda
• Illusory correlation
• Interclass correlation
• Intraclass correlation
• Lift (data mining)
• Modifiable areal unit problem
• Multiple correlation
• Point-biserial correlation coefficient
• Quadrant count ratio
• Spurious correlation
• Statistical arbitrage
• Subindependence

References

Further reading

• Cohen, J., Cohen P., West, S.G., & Aiken, L.S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Psychology Press. ISBN 0-8058-2223-2. 
• Hazewinkel, Michiel, ed. (2001), "Correlation (in statistics)", Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4 
• Oestreicher, J. & D. R. (February 26, 2015). Plague of Equals: A science thriller of international disease, politics and drug discovery. California: Omega Cat Press. p. 408. ISBN 978-0963175540. 

External links

• MathWorld page on the (cross-)correlation coefficient/s of a sample
• Compute significance between two correlations, for the comparison of two correlation values.
• A MATLAB Toolbox for computing Weighted Correlation Coefficients
• Proof that the Sample Bivariate Correlation Coefficient has Limits ±1
• Interactive Flash simulation on the correlation of two normally distributed variables by Juha Puranen.
• Correlation analysis. Biomedical Statistics
• R-Psychologist Correlation visualization of correlation between two numeric variables

UpToDate Contents

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Related Links

• correlateの意味 - 英和辞書 - 英語辞書 - goo辞書

correlateとは。意味や和訳。[動](他)…と（…とを）相互に関係させる((with ...))correlate geography with other studies地理学を他学科と関連させる. (自)（…と）相互に関連する((with ...))His thesis correlates with mine.彼の卒業論文は私 ...

• correlateの意味・用例｜英辞郎 on the WEB：アルク

correlate 【自動】相互に関連がある 【他動】 〔～を集めて〕相互の関係を比較... - アルクがお届けする進化するオンライン英和・和英辞書データベース。一般的な単語や連語から、イディオム、専門用語、スラングまで幅広く収録。

• Correlate | Define Correlate at Dictionary.com

Correlate definition, to place in or bring into mutual or reciprocal relation; establish in orderly connection: to correlate expenses and income. See more. Thesaurus Translate Puzzles & Games Word of the Day Blog Slideshows ...

Related Pictures

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★リンクテーブル★

リンク元	「associate」「関連」「reference」「connection」「pertinent」

「associate」

　　[★]

• vt.

• 結合する、連合させる。連想によって結びつける。(人を)仲間/友達などとしての関係に置く。(化)会合させる

• vi.

• 仲間/友人などとして交際する(with)。一つにまとめる、連合する

• n.

• 仕事仲間、提携者、同僚。友人、朋友。準会員。従業員、社員
• 密接な蒸す偽付きのある物、付随する物、つきもの。準国家
• 連想によって心に浮かぶ言葉/思い、連想物

• adj.

• 仲間の、同僚の。製紙機械員に次ぐ資格の
• 付随する、連想される

関

accompany、association、associative、attach、bearing、bind、binding、bond、bonding、coassemble、colleague、combine、conjoin、conjugate、conjugation、conjunction、connect、connection、correlate、correlation、couple、dock、engage、engagement、federation、implication、join、joint、ligate、link、linkage、peer、pertinent、reference、relate、relation、relationship、relative、relevance、relevant、symphysial、union

　 　 　 　 　 　

「関連」

　　[★]

英

relation、relationship、correlation、relevance、link、implication、connection、bearing、association、reference、relate、associate、attach、correlate、relative、relevant、pertinent

関

暗示、意味、会合、関係、関する、関連性、基準、共生、結合、結線、言及、参考、参照、照会、親戚、親類、軸受け、相関、相関性、相互関係、相対、相対性、相対的、対合、態度、妥当、適切、同僚、付随、付着、結びつける、類縁体、連関、連結、連合、連合性、連鎖、連接、連絡、群集、添付、協会、接続、関係性、リファレンス、リンク、参考文献、相関関係、関連づける

「reference」

　　[★]

• n.

• (書物などの)参考、参照、照合(to)。言及、論及(to)。関連、関係(to)

idea of reference 関係念慮

• 参考書。参考文献。引用文。参照符、参照指示
• 紹介、問い合わせ(to)。(前歴。身元などの)証明書、推薦状。紹介先、身元保証人
• 指示、表示。(文法)(代名詞が)指すこと、指示
• 委託、付託(to)。(法)仲裁、(仲裁人に対する)事件付託

• adj.

• 基準の、参照用の

• vt.

関

associate, association, attach, bearing, bench mark, benchmark, canonical, cf., connection, correlate, correlation, criteria, criterion, implication, inquire, inquiry, link, measure, mention, pertinent, refer, referral, relate, relation, relationship, relative, relevance, relevant, standard

　 　 　 　 　 　

「connection」

　　[★]

• n.

• 結合、関連、関係、連絡、結線、接続

関

access、associate、association、attach、bearing、bind、binding、bond、bonding、combine、communicate、communication、concern、conjoin、conjugate、conjugation、conjunction、connect、contact、correlate、correlation、couple、dock、engage、engagement、germane、implicate、implication、interplay、join、juncture、liaison、ligate、link、linkage、pertain、pertinent、referable、reference、relate、relation、relationship、relative、relevance、relevant、respect、symphysial、union

　 　 　 　 　 　 　

「pertinent」

　　[★]

• adj.

• 直接関係のある、適切な、的を得た、しっくりした(to)

Note that tobacco, alcohol, and drugs may also be included in the Personal and Social History; however, many clinicians find these habits pertinent to the Present Illness.

関

adequate、adequately、appropriate、appropriately、associate、association、attach、bearing、concern、connect、connection、correct、correlate、correlation、fittingly、germane、implicate、implication、interplay、link、neatly、pertain、proper、properly、reasonable、referable、reference、relate、relation、relationship、relative、relevance、relevant、respect、suitably、valid