#Linear association scatter plot drivers

Scatter plots are not a good option for categorical or nominal data, since these data are measured on a scale with specific values. Some examples of continuous data are:Ĭategorical or nominal data: use bar charts Scatter plots make sense for continuous data since these data are measured on a scale with many possible values. Scatter plots and types of data Continuous data: appropriate for scatter plots Annotations explaining the colors and markers could further enhance the matrix.įor your data, you can use a scatter plot matrix to explore many variables at the same time. The colors reveal that all these points are from cars made in the US, while the markers reveal that the cars are either sporty, medium, or large. There are several points outside the ellipse at the right side of the scatter plot. From the density ellipse for the Displacement by Horsepower scatter plot, the reason for the possible outliers appear in the histogram for Displacement. In the Displacement by Horsepower plot, this point is highlighted in the middle of the density ellipse.īy deselecting the point, all points will appear with the same brightness, as shown in Figure 17. This point is also an outlier in some of the other scatter plots but not all of them. In Figure 16, the single blue circle that is an outlier in the Weight by Turning Circle scatter plot has been selected. It's possible to explore the points outside the circles to see if they are multivariate outliers. The red circles contain about 95% of the data. The new descriptions of strength, linearity and direction.The scatter plot matrix in Figure 16 shows density ellipses in each individual scatter plot. Given a new set of scatterplots below, repeat the same exercise, but now with Portland, OR) there is a strong, linear trend. Though there are a few outliers (citiesĪlong the northwest coast of the US that have temperate winters, such as Negative direction, as the greater the latitude, the colder the Scatter plots are described as linear orįor example, the scatterplot of latitude and January temperatures had

The linearity of scatter plot indicates how close the points are If the points are clearly clustered, or closelyįollow a curve or line, the relationship is described as strong. The more spread out the points are, the weaker The strength of a scatter plot is usually described as weak, Increases, or the points of the scatterplot go down from left to The explained variable decreases as the explanatory variable Increases as the explanatory variable increases, or the points of the The direction is positive when the explained variable The direction of a scatter plot can be described as positive or When describing the shape of the scatter plot and the relationshipīetween the explanatory and explained variable, there are three important This exercise would be simpler given uniform adjectives that everyone could

#Linear association scatter plot drivers

Similarly, drivers with less driving experience are considered riskier and pay greater premiums.

Ĭorrect: Drivers with more driving experience are considered safer, so they pay smaller premiums.

(y) is the insurance premium paid for a sample of drivers. Q-6: The explanatory variable (x) is the years of driving experience and the explained variable