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Waiting time between eruptions and theduration of the eruption for the Old

Faithful Geyser in Yellowstone National

Park, Wyoming, USA. This scatterplot

suggests there are generally two "types"

oferuptions: short-wait-short-duration,

and long-wait-long-duration.

Bivariate analysisFromWikipedia, the free encyclopedia

Bivariate analysisis one of the simplest forms of quantitative

(statistical) analysis.[1]It involves the analysis of two variables

(often denoted asX, Y), for the purpose of determining the

empirical relationship between them.[1]

Bivariate analysis can be helpful in testing simple hypotheses of

association. Bivariate analysis can help determine to what extent

it becomes easier to know and predict a value for one variable

(possibly a dependent variable) if we know the value of the other

variable (possibly the independent variable) (see also correlation

and simple linear regression).[2]

Bivariate analysis can be contrasted with univariate analysis in

which only one variable is analysed.[1]

Like univariate analysis,bivariate analysis can be descriptive or inferential. It is the

analysis of the relationship between the two variables.[1]

Bivariate analysis is a simple (two variable) special case of

multivariate analysis (where multiple relations between multiple

variables are examined simultaneously).[1]

There are 2 types, one is inferentialand the other is descriptive.

Contents

1 When there is a dependent variable

2 When there is not a dependent variable

3 Graphical methods

4 See also

5 References

When there is a dependent variable

If the dependent variablethe one whose value is determined to some extent by the other, independent

variable is a categorical variable, such as the preferred brand of cereal, then probit or logit regression (or

multinomial probit or multinomial logit) can be used. If both variables are ordinal, meaning they are ranke

https://en.wikipedia.org/wiki/Wyominghttps://en.wikipedia.org/wiki/Yellowstone_National_Parkhttps://en.wikipedia.org/wiki/Scatterplothttps://en.wikipedia.org/wiki/Yellowstone_National_Parkhttps://en.wikipedia.org/wiki/Yellowstone_National_Parkhttps://en.wikipedia.org/wiki/Independent_variablehttp://-/?-https://en.wikipedia.org/wiki/Multivariate_analysishttp://-/?-http://-/?-https://en.wikipedia.org/wiki/Statisticshttp://-/?-https://en.wikipedia.org/wiki/Statisticshttps://en.wikipedia.org/wiki/Multinomial_logithttps://en.wikipedia.org/wiki/Multinomial_probithttps://en.wikipedia.org/wiki/Logithttps://en.wikipedia.org/wiki/Probithttps://en.wikipedia.org/wiki/Independent_variablehttps://en.wikipedia.org/wiki/Dependent_variablehttp://-/?-https://en.wikipedia.org/wiki/Multivariate_analysishttp://-/?-http://-/?-https://en.wikipedia.org/wiki/Univariate_analysishttp://-/?-https://en.wikipedia.org/wiki/Simple_linear_regressionhttps://en.wikipedia.org/wiki/Correlationhttps://en.wikipedia.org/wiki/Independent_variablehttps://en.wikipedia.org/wiki/Dependent_variablehttps://en.wikipedia.org/wiki/Association_(statistics)https://en.wikipedia.org/wiki/Hypotheseshttp://-/?-https://en.wikipedia.org/wiki/Dependent_and_independent_variableshttp://-/?-https://en.wikipedia.org/wiki/Statisticshttps://en.wikipedia.org/wiki/Scatterplothttps://en.wikipedia.org/wiki/Wyominghttps://en.wikipedia.org/wiki/Yellowstone_National_Parkhttps://en.wikipedia.org/wiki/Old_Faithful_Geyserhttps://en.wikipedia.org/wiki/File:Oldfaithful3.png

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in a sequence as first, second, etc., then a rank correlation coefficient can be computed. If just the depende

variable is ordinal, ordered probit or ordered logit can be used. If the dependent variable is continuous

either interval level or ratio level, such as a temperature scale or an income scalethen simple regression

can be used.

If both variables are time series, a particular type of causality known as Granger causality can be tested for

and vector autoregression can be performed to examine the intertemporal linkages between the variables.

When there is not a dependent variable

When neither variable can be regarded as dependent on the other, regression is not appropriate but some

form of correlation analysis may be. [3]

Graphical methods

Graphs that are appropriate for bivariate analysis depend on the type of variable. For two continuous

variables, a scatterplot is a common graph. When one variable is categorical and the other continuous, a bo

plot is common and when both are categorical a mosaic plot is common. These graphs are part of

descriptive statistics.

See also

Coding (social sciences)Descriptive statistics

References

1. Earl R. Babbie, The Practice of Social Research, 12th edition, Wadsworth Publishing, 2009, ISBN 0-495-5984

0, pp. 436440

2. Bivariate Analysis (http://sociologyindex.com/bivariate_analysis.htm), Sociology Index>

3. Chatterjee, Samprit (2012). Regression analysis by example. Hoboken, New Jersey: Wiley. ISBN 978-

0470905845.

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Categories: Multivariate statistics

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