It suggests that there is very little evidence of a linear relationship between the variables.
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.
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.
Correlation
No correlation. Answer provided by
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.
No, it is not resistant to changes in data.
correlation is used when there is metric data and chi square is used when there is categorized data. sayan chakrabortty
positive
I would use Spearman and Kendall
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).
the R value in the calculator also known as the amount of correlation the data points fit
From Laerd Statistics:The Pearson product-moment correlation coefficient (or Pearson correlation coefficient for short) is a measure of the strength of a linear association between two variables and is denoted by r. Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (how well the data points fit this new model/line of best fit).
It tells you how strong and what type of correlations two random variables or data values have. The coefficient is between -1 and 1. The value of 0 means no correlation, while -1 is a strong negative correlation and 1 is a strong positive correlation. Often a scatter plot is used to visualize this.
Not sure what a "grouped of data" means!
No, it's a small enough value that it doesn't suggest any correlation at all. There's no hard-and-fast rule for interpreting the correlation coefficient: a very strong correlation in one discipline might be considered weak in others, and the correlation coefficient might be misleading in some cases. But most of the time, you want r to be at least plus or minus 0.9 before even thinking about any relation between the data.
It suggests that there is very little evidence of a linear relationship between the variables.