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What does it mean when a positive correlation coefficient between the dependent variable Y and the independent variable X indicates?
Wiki User
∙ 13y ago
It means that the two variables are likely dependent. The higher the number of the positive correlation the stronger the connection.
Wiki User
∙ 13y ago
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What does it mean for variables to show correlation?
A positive correlation coefficient means that as the value of one variable increases, the value of the other variable increases; as one decreases the other decreases. A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.
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 correlation resistant or nonresistant to outliers?
correlation is drawn from all data points. if you look at the r^2 value and it's below 0.99 for example (should be higher in non research work (and in much research work) it indicates that 1 of your points may be an outlier. If you input all datapoints into excel, you may be able to see the point that's throwing it off. There are also statistical tests you can do to spot an outlier. In other words, correlation is not independent of an outlier. it will make the r^2 value worse. If the outlier is taken out, then the correlation could be deemed independent but only because you manipulated it and had taken the outlier out
A statistical measure that indicates the extent to which in one factor are accompanied by changes in another is called?
Correlation.
What correlations indicates the weakest relationship plus 83 -74 plus 10 or -21?
The correlation showing the weakest relationship is -74.
What does a correlation coefficient of zero indicates?
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.
A correlation coefficient of zero indicates?
A correlation coefficient of zero means that two things are not correlated to each other.
Correlation coefficient value of 0.00 indicates two variables are not related?
If the correlation coefficient is 0, then the two tings vary separately. They are not related.
Which correlation coefficient indicates the weakest relationship between variables?
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
Which correlation coefficient indicates the strongest relation between two variables?
Generally speaking it is the coefficient that produces a ratio between variables of 1:1. If the variables are of a dependent/independent framework, I find that Chronbach's or Pearson's produces the most accurate (desirable) results. Hope this helps for answering a very good question for what appears to be n enthusiastic novice investigator.
What does a correlation coefficient represent?
The correlation coefficient for two variables is a measure of the degree to which the variables change together. The correlation coefficient ranges between -1 and +1. At +1, the two variables are in perfect agreement in the sense that any increase in one is matched by an increase in the other. An increase of twice as much in the first is accompanied by double the increase in the second. A correlation coefficient of -1 indicates that the two variables are in perfect opposition. The changes in the two variables are similar to when the correlation coefficient is +1, but this time an increase in one variable is accompanied by a decrease in the other. A correlation coefficient near 0 indicates that the two variables do not move in harmony. An increase in one is as likely to be accompanied by an increase in the other variable as a decrease. It is very very important to remember that a correlation coefficient does not indicate causality.
What does it mean for variables to show correlation?
A positive correlation coefficient means that as the value of one variable increases, the value of the other variable increases; as one decreases the other decreases. A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.
A positive value for a correlation indicates?
A positive value for a correlation indicates a positive correlation; e.g. it has a positive slope.
When do you use Pearson's r?
See related link. As stated in the link: In probability theory and statistics, correlation (often measured as a correlation coefficient) indicates the strength and direction of a linear relationship between two random variables
What is the difference between corelation and regression?
I've included links to both these terms. Definitions from these links are given below. Correlation and regression are frequently misunderstood terms. Correlation suggests or indicates that a linear relationship may exist between two random variables, but does not indicate whether X causes Yor Y causes X. In regression, we make the assumption that X as the independent variable can be related to Y, the dependent variable and that an equation of this relationship is useful. Definitions from Wikipedia: In probability theory and statistics, correlation (often measured as a correlation coefficient) indicates the strength and direction of a linear relationship between two random variables. In statistics, regression analysis refers to techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (also called a response variable) and of one or more independent variables (also known as explanatory variables or predictors). The dependent variable in the regression equation is modeled as a function of the independent variables, corresponding parameters ("constants"), and an error term. The error term is treated as a random variable. It represents unexplained variation in the dependent variable. The parameters are estimated so as to give a "best fit" of the data. Most commonly the best fit is evaluated by using the least squares method, but other criteria have also been used.
What can you say about the correlation coefficient and the correlation description of the correlation when the points lie exactly on either vertical line or horizontal line?
The observed relationship indicates that the a one-to-one correspondence exists between the variables of interest. In effect, the value of the obtained r-value is -1 or 1.
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.
