What prediction can you make based on the variables if their correlation r 50?

What prediction can you make based on the variables if their correlation r 50?

A correlation coefficient of r=. 50 indicates a stronger degree of linear relationship than one of r=. 40.

Can you make predictions based on correlations?

Any type of correlation can be used to make a prediction. However, a correlation does not tell us about the underlying cause of a relationship.

How Should predictions be made?

Predicting requires the reader to do two things: 1) use clues the author provides in the text, and 2) use what he/she knows from personal experience or knowledge (schema). When readers combine these two things, they can make relevant, logical predictions.

Is correlation useless?

Correlation (or any other measure of association) is useful for prediction regardless of causation. Suppose that you measure a clear, stable association between two variables.

What are examples of predictions?

Some examples of real world predictions are:

• It is raining and the sun is out one could predict there may be a rainbow.
• A college student is studying hard for their final exam really one might predict they will get an A on it.
• A child has a fever and a sore throat, one might predict the child has strep throat.

Does correlation imply prediction?

Any type of correlation can be used to make a prediction. However, a correlation does nottell us about the underlying cause of a relationship. All we know from the Illinois data is that drinking was negatively correlated with grade-point average. The possible explanations are many.

What is the difference between correlation and simple regression?

The main difference between correlation and regression is that correlation measures the degree to which the two variables are related, whereas regression is a method for describing the relationship between two variables.

What does the correlation tell us?

Correlation is about the relationship between variables. Correlations tell us: whether this relationship is positive or negative. the strength of the relationship.

Which is the appropriate measure of correlation?

The appropriate measure of association for this situation is Pearson’s correlation coefficient, r (rho), which measures the strength of the linear relationship between two variables on a continuous scale. The coefficient r takes on the values of −1 through +1. Values of −1 or +1 indicate a perfect linear relationship between the two variables, whereas a value of 0 indicates no linear relationship.