Certainly! Here are explanations of the specified scatter plot terminology:
1. Outlier
An outlier is a data point that differs significantly from the other observations in a dataset. In a scatter plot, an outlier appears far away from the cluster of other points. Outliers can result from variability in the data, measurement errors, or they may indicate a novel phenomenon that deserves further investigation. For example, if most data points in a scatter plot show a certain trend but one point lies far outside this trend, that point is considered an outlier.
2. Negative Linear Association
A negative linear association occurs when an increase in one variable is associated with a decrease in another variable. In a scatter plot, this is visually represented by points that tend to slope downward from left to right. This means that as the value of the independent variable (often plotted on the x-axis) increases, the value of the dependent variable (often on the y-axis) tends to decrease. For example, there may be a negative linear association between the number of hours studied and the number of errors made on a test—more hours of study could lead to fewer errors.
3. Positive Linear Association
A positive linear association is the opposite of a negative linear association. It occurs when both variables increase together. In a scatter plot, points showing a positive linear association slope upward from left to right. This indicates that as the independent variable increases, the dependent variable also tends to increase. For example, there might be a positive linear association between the amount of exercise and physical endurance—more exercise might generally lead to greater endurance.
In summary:
- Outliers are unusual points that stand apart from the rest of the data.
- A negative linear association shows that as one variable increases, the other decreases.
- A positive linear association indicates that as one variable increases, the other also increases.