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Spearman's rank correlation coefficient for grouped data?
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.
When the data points on a graph are scattered with no clear pattern there is between the two variables?
No correlation. Answer provided by
Which of these correlation numbers shows the strongest relationship?
A correlation coefficient of 1 or -1 would be the highest possible statistical relationship. However, the calculation of correlation coefficients between non independent values or small sets of data may show high coefficients when no relationship exists.
What is the strongest linear correlation?
The relationship between two random independently distributed variables is considered to be theoretically the weakest when the correlation coefficient is zero and theoretically the strongest when the correlation coefficient is one, indicating a positive relationship between two variables and negative one, indicating a negative relationationship between two variables. I state that this is a theoretical result as if variables are not random, independently distributed, then a high correlation coefficient can result. For example, let us say that we obtained the following data on age and frequency of accidents: Age 18 1 in 18 people have accidents in a year Age 25 1 in 25 people have accident in a year Age 30 1 in 30 people have accidents in a year Age 35 1 in 6 people have accidents. Age 40 1 in 400 people have accidents If I selectively calculated a correlation coefficient this data including only the three groups ages 18, 25 and 30, you can see I will have a correlation coefficient of 1, however the data was not a random sample of all ages. See related link.
When do you not connect the dots on a graph?
You do not connect the dots on a graph when the data points are discrete and not continuous. In other words, when the values represent distinct and unrelated data points rather than a continuous sequence. Connecting the dots in such cases would imply a relationship or trend between the points that does not exist. It is important to consider the nature of the data being represented to determine whether connecting the dots is appropriate.
When you removed from the data set how would the correlation coefficient be affected?
If you remove certain data points from a dataset, the correlation coefficient may be affected depending on the nature of the relationship between the removed data points and the remaining data points. If the removed data points have a strong relationship with the remaining data, the correlation coefficient may change significantly. However, if the removed data points have a weak or no relationship with the remaining data, the impact on the correlation coefficient may be minimal.
What shows a correlation which may be positive or negative between two sets of data?
I believe you are asking how to identify a positive or negative correlation between two variables, for which you have data. I'll call these variables x and y. Of course, you can always calculate the correlation coefficient, but you can see the correlation from a graph. An x-y graph that shows a positive trend (slope positive) indicates a positive correlation. An x-y graph that shows a negative trend (slope negative) indicates a negative correlation.
Is the correlation coefficient resistant?
No, it is not resistant to changes in data.
When interpreting a correlation coefficient it is important to look at?
When interpreting a correlation coefficient, it is important to consider both the strength and direction of the relationship between the two variables, as indicated by the value of the coefficient (ranging from -1 to +1). Additionally, one should examine the context of the data, including sample size and potential confounding variables, which can influence the correlation. Finally, correlation does not imply causation, so it's crucial to avoid jumping to conclusions about cause-and-effect relationships based solely on the correlation coefficient.
What is the difference between correlation coefficient and chi square?
correlation is used when there is metric data and chi square is used when there is categorized data. sayan chakrabortty
Spearman's rank correlation coefficient for grouped data?
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.
Will the correlation coefficient for these data points be positive or negative 24 47 512?
positive
What is the correlation coefficient to use for ordinal versus nominal data?
I would use Spearman and Kendall
When the points on a graph tend to scatter randomly we say that they indicate .?
That there is no correlation of the collected data.
What are the disadvantages of a scatter graph?
A scatter graph may use a positive correlation or negative correlation, to shows points of the graph in either a dipping or climbing line, and is fairly easy to read the data. A zero correlation is when the points are scattered across the graph and this can make seeing the data difficult. It's a bit like "dot to dot" in a children's puzzle book, but without the numbers at the side of the dots!
When the data points on a graph are scattered with no clear pattern there is between the two variables?
No correlation. Answer provided by
How is a linear relationship between two variables measured in statistics?
The Correlation Coefficient computed from the sample data measures the strength and direction of a linear relationship between two variables. The symbol for the sample correlation coefficient is r. The symbol for the population correlation is p (Greek letter rho).
