Spearman's rank correlation coefficient

Spearman's rank correlation coefficient is given in the related link at the bottom of this page.

Although Spearman's rank correlation coefficient puts a numerical value between the linear association between two variables, it can only be used for data that has not been grouped.

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I would use Spearman and Kendall

Yes, correlations can be measured using statistical methods such as Pearson's correlation coefficient or Spearman's rank correlation coefficient. These measures quantify the strength and direction of the relationship between two variables.

See: http://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient

The possible range of correlation coefficients depends on the type of correlation being measured. Here are the types for the most common correlation coefficients:

1. Pearson Correlation Coefficient (r)
2. Spearman's Rank Correlation Coefficient (ρ)
3. Kendall's Rank Correlation Coefficient (τ)

All of these correlation coefficients ranges from -1 to +1. In all the three cases, -1 represents negative correlation, 0 represents no correlation, and +1 represents positive correlation. It's important to note that correlation coefficients only measure the strength and direction of a linear relationship between variables. They do not capture non-linear relationships or establish causation.

For better understanding of correlation analysis, you can get professional help from online platforms like SPSS-Tutor, Silverlake Consult, etc.

Ranking of data allows calculation of ranges and percentiles. Quick estimation of correlation coefficient is possible (Spearman's method). Certain graphical displays of data, such as box and whiskers plots use percentiles.

Data ranks come from sorting the data. Manually ordering large sets of data can be time consuming, but very easy with spreadsheet programs. There are alternative means of calculating correlation, but if you are to use Spearman's rank correlation, you have to order each data set and determine ranks.

Yes it can be a correlation coefficient.

No, it cannot be a correlation coefficient.

Try this link: http://mathforum.org/library/drmath/view/52774.html - its quite a complicated explanation!

The number 6 in the Spearman's rank correlation coefficient formula is a constant used to standardize the formula and make it more interpretable. It helps to scale the formula so that the resulting correlation coefficient falls within the range of -1 to 1, which indicates the strength and direction of the relationship between the ranked variables. Essentially, the 6 in the formula is a mathematical adjustment that ensures the correlation coefficient is properly calculated and comparable across different data sets.

Right.. Clearly u are supposed to be in a lesson so why are u asking me ? Not the Teacher ? -.-

No.

The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).

• The correlation coefficient is symmetrical with respect to X and Y i.e.
• The correlation coefficient is the geometric mean of the two regression coefficients. or .
• The correlation coefficient lies between -1 and 1. i.e. .

Fisher's exact probability test, chi-square test for independence, Kolmogorov-Smirnov test, Spearman's Rank correlation and many, many more.

A serious error. The maximum magnitude for a correlation coefficient is 1.

The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them. Here they are given less than -1 value so it is not a value of correlation coefficient.

the correlation coefficient range is -1 to +1

The Spearman coefficient can be calculatated only for two characteristics of the observed population, as for kendall's W there may be two or more characteristics.

Evidence that there is no correlation.

coefficient of determination

A coefficient of zero means there is no correlation between two variables. A coefficient of -1 indicates strong negative correlation, while +1 suggests strong positive correlation.

• The correlation coefficient is symmetrical with respect to X and Y i.e.
• The correlation coefficient is the geometric mean of the two regression coefficients. or .
• The correlation coefficient lies between -1 and 1. i.e. .

The correlation coefficient must lie between -1 and +1 and so a correlation coefficient of 35 is a strong indication of a calculation error.

If you meant 0.35, then it is a weak correlation.

0

correlation is a difference in statistics

good correlation

A coefficient of correlation of 0.70 infers that there is an overall correlation between the trends being compared. The correlation is not perfect, but enough to be acknowledged and researched further.

partial correlation is the relation between two variable after controlling for other variables and multiple correlation is correlation between dependent and group of independent variables.

A correlation coefficient of zero means that two things are not correlated to each other.

34.32245

Correlation coefficient is less than -1 and greater than 1.

Note: The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them.

No. Correlation coefficient is measured from +1 to -1. In addition, if the two sets of exam are exactly same, their correlation coefficient is +1.

Why the value of correlation coefficient is always between -1 and 1?

The weakest correlation coefficient is 0, which means there is absolutely no relationship between the two variables you are correlating.

1.

A correlation coefficient cannot exceed 1.

Correlation coefficient is a measure of the strength and direction of a relationship between two variables. It quantifies how closely the two variables are related and ranges from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no correlation.

No.

The units of the two variables in a correlation will not change the value of the correlation coefficient.

It will be invaluable if (when) you need to calculate sample correlation coefficient, but otherwise, it has pretty much no value.

Assume that you are correlating two variables x and y. If there is an increasing relationship between x and y, (that is , the graph of y=a+bx, slopes upward), the correlation coefficient is positive. Similarly, if there is a decreasing relationship, the correlation coefficient is negative. The correlation coefficient can assume values only between -1 and 1.

A correlation coefficient of 1 (r=1) is a perfect positive correlation.

No, it depends upon the size of the coefficient of correlation: the closer to ±1 the stronger the correlation.

When the correlation coefficient is positive, one variable increases as the other increases; when negative one increases as the other decreases.

The correlation coefficient, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.

not otherwise....split half method is one way of determining reliability of your test and Spearman-Brown formula is a technique used to re-calculate the correlation of your test when you split your test items into half.... this means that the result of Spearman-Brown provides you the correlation of your test in full length.

If the correlation coefficient is 0, then the two tings vary separately. They are not related.

correlation measure the strength of association between to variables.but some times both variables are not in same units.so we cannot measure it with the help of correlation. in this case we use its coefficent which mean unit free. that,s why we use it.

impossible