Correlation, Scatterpoints, and Causations

Front

What is correlation?

Back

Correlation is a statistical measure that describes the extent to which two variables change together. A positive correlation means that as one variable increases, the other also increases, while a negative correlation means that as one variable increases, the other decreases.

Front

What is causation?

Back

Causation indicates that one event is the result of the occurrence of another event. In other words, causation implies a direct cause-and-effect relationship between two variables.

Front

What is the difference between causation and association?

Back

Causation implies a direct cause-and-effect relationship, while association indicates a relationship where two variables change together but one does not necessarily cause the other.

Front

Example of a positive correlation.

Back

As the temperature increases, ice cream sales also increase.

Front

Example of a negative correlation.

Back

As the amount of time spent studying increases, the number of mistakes on a test decreases.

Front

What does it mean if two variables are associated?

Back

If two variables are associated, it means there is a relationship between them, but it does not imply that one variable causes the other.

Front

What is a scatterplot?

Back

A scatterplot is a graphical representation of the relationship between two quantitative variables, where each point represents an observation.

Front

What does a scatterplot with a positive slope indicate?

Back

A scatterplot with a positive slope indicates a positive correlation between the two variables, meaning that as one variable increases, the other also tends to increase.

Front

What does a scatterplot with a negative slope indicate?

Back

A scatterplot with a negative slope indicates a negative correlation between the two variables, meaning that as one variable increases, the other tends to decrease.

Front

What is a confounding variable?

Back

A confounding variable is an outside influence that changes the effect of a dependent and independent variable. It can lead to incorrect conclusions about the relationship between the variables.