0
What else can I help you with?
Which of the represents the highest correlation possible between two variables?
+1.00
If anxiety and depression are correlated what three possible directions of causality might explain this correlation?
If anxiety and depression are correlated, there are three possible directions of causality. These are anxiety causes depression, depression causes anxiety, and there is an environmental stimuli that causes both anxiety and depression.
Does the explanatory variable cause changes in the response variable in a valid sample?
No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.
When do you use a scatterplot?
Scatter plots are used to investigate a possible correlation between two variables that are associated with the same “event.”
Is it possible to make a prediction based in a scatter plot with no correlation?
Correlation is an estimate of a linear relationship between two variables and takes no account of non-linear relationship. If the relationship is quadratic and the domain is symmetric about some point, the correlation will be zero. It is, thus possible for the points on the scatter plot to lie exactly on a parabola while the calculated correlation is zero. In such a case, it is easy to make a prediction despite no correlation.
What is the strongest a correlation could be?
Either +1 (strongest possible positive correlation between the variables) or -1 (strongest possible negativecorrelation between the variables).
What are the three conditions necessary for causation between variables?
The three conditions necessary for causation between variables are covariance (relationship between variables), temporal precedence (the cause must precede the effect in time), and elimination of plausible alternative explanations (other possible causes are ruled out).
Where is it not possible to use correlation?
It is not possible to use correlation when the two variables are not related at all. the corelation coefficient value that will be obtained will have no significance.
Which of the represents the highest correlation possible between two variables?
+1.00
If anxiety and depression are correlated what three possible directions of causality might explain this correlation?
If anxiety and depression are correlated, there are three possible directions of causality. These are anxiety causes depression, depression causes anxiety, and there is an environmental stimuli that causes both anxiety and depression.
Does the explanatory variable cause changes in the response variable in a valid sample?
No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.
When do you use a scatterplot?
Scatter plots are used to investigate a possible correlation between two variables that are associated with the same “event.”
Is it possible to make a prediction based in a scatter plot with no correlation?
Correlation is an estimate of a linear relationship between two variables and takes no account of non-linear relationship. If the relationship is quadratic and the domain is symmetric about some point, the correlation will be zero. It is, thus possible for the points on the scatter plot to lie exactly on a parabola while the calculated correlation is zero. In such a case, it is easy to make a prediction despite no correlation.
What are the possible reasons for a correlation between two variables?
Chance association, (the relationship is due to chance) or causative association (one variable causes the other).
What happens if the correlation coefficient equals 0?
Nothing happens. It simply means that there is no linear relationship between the two variables. It is possible that there is a non-linear relationship or that there is none.
What are the possible ranges of correlation coefficients?
The possible range of correlation coefficients depends on the type of correlation being measured. Here are the types for the most common correlation coefficients: Pearson Correlation Coefficient (r) Spearman's Rank Correlation Coefficient (ρ) 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.
What does a correlation coefficient of 1.1 mean?
Well, friend, a correlation coefficient of 1.1 is not possible because correlation coefficients range from -1 to 1. If you meant 1.0, that would indicate a perfect positive linear relationship between two variables. It means as one variable increases, the other variable also increases proportionally.
