To analyze the type of data represented in the scatterplot, we can consider the following options:
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Decreasing or Increasing: These options refer to the trend of the data. If the points in the scatterplot show a downward trend as temperature increases, it would be considered "Decreasing." If they show an upward trend, it would be "Increasing." Without seeing the plot, we can't definitively choose between these options.
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Linear Relationship or Non-Linear Relationship: A linear relationship would mean that there is a straight-line association between the two variables, while a non-linear relationship indicates a curved or more complex relationship. This classification also depends on the visual characteristics of the scatterplot.
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Univariate or Bivariate: Univariate data involves one variable, whereas bivariate data involves two variables. Since the scatterplot shows the relationship between temperature (one variable) and feet above/below sea level (another variable), this is a bivariate relationship.
Given these interpretations, if you were to select three characteristics based on general scatterplot trends and analysis, a reasonable choice could be:
- Bivariate (since it shows two variables)
- Decreasing or Increasing (depending on the trend depicted in the scatterplot)
- Linear Relationship or Non-Linear Relationship (depending on the nature of the data points)
However, without specific details about the scatterplot, it's not possible to definitively choose all three items. Here’s an example of what could be selected if you deduced the trend was decreasing and linear:
- Bivariate
- Decreasing
- Linear Relationship
Remember to evaluate the actual plot to confirm which of these options would be accurate.