Correlation analysis

With the help of correlation analysis, the linear relationship between variables can be examined. The strength of the correlation is determined by the correlation coefficient, which varies from -1 to +1. This means that correlation analyses can be used to make a statement about the strength and direction of the relationship between two variables.
Interpret correlation
Positive correlation
A positive correlation exists when larger values of variable A are accompanied by larger values of variable B. Body size and shoe size, for example, correlate positively, resulting in a correlation coefficient that lies between 0 and 1, i.e. a positive value.
Negative correlation
A negative correlation exists when larger values of variable A are accompanied by smaller values of variable B. The product price and the sales volume usually have a negative correlation; the more expensive a product is, the lower the sales volume. In this case, the correlation coefficient lies between -1 and 0, i.e. it takes on a negative value.
More information on correlation analysis:
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And here is the online correlation calculator:
datatab.net/st...

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excellent explanation. Thank you so much

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Very useful information. Thank you!

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Glad it was helpful! Regards, Hannah

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Thanks for your explanation!

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New sub and lovely voice❤

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Subtitles are only available in Hindi, we hope to provide them in other languages, such as English or Chinese

[@architasen3761]

Is it the Pearson correlation or the Spearman correlation that is used for variables with an ordinal scale level?

[@lukeasante7893]

Spearman

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Mam can you arrange test for a correlation chapter..as soon as possible...

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I will try!

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Excellent voice

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❤ thanks

[@sabaaslam4766]

Is it necessary to have the same number of observations for every variable to find the correlation?

[@TheMightyWej]

Yes, there needs to be the same number of x and y terms for any calculations to be completed. You need to extrapolate (assume) values for the points you are missing, usually using a mean value will do fine.

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Saying to pay for that

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